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ACUTE EFFECTS OF DIET AND EXERCISE ON COGNATIVE FUNCTION
AND BRAIN ACTIVATION IN AN AGING POPULATION

By

Natalie Stein

A THESIS

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

MASTERS OF SCIENCE
Human Nutrition

2009

Abstract

ACUTE EFFECTS OF DIET AND EXERCISE ON COGNITIVE FUNCTION
AND BRAIN ACTIVATION IN AN AGING POPULATION

By

Natalie Stein
Background: Aging and lifestyle factors have been associated with age-related
cognitive decline, including executive function. Epidemiological aging studies
suggest a role of diet and exercise in cognition but less is known about acute effects
in an experimental setting. Objective: Compare the effects of a high saturated fat,
low nutrient-dense standard breakfast (SB), nutrient dense breakfast (NB), and NB
with aerobic exercise (30 min at 50-65% heart rate reserve, AB) on cognitive
function and brain activation in older adults. Methods: A three arm, randomized,
cross-over design was used in healthy adults (n=l9, 69.7 t 4.9 y). Cognitive
function was assessed with CogState® tasks. Evaluation of brain activation was
with the brain blood oxygen level-dependent response during functional MRI with a
ﬂanker arrow task requiring inhibition. Results: Overall, NB with or without AE did
not improve cognition in healthy older adults. In the CogState®, accuracy improved
on prediction (executive function) in NB over NB + AE, and speed declined for
monitoring (attention) in NB compared to SB, p<0.05. No signiﬁcant treatment
effects were found in the flanker arrow task or for brain activation in the inferior
and middle frontal gyrus. Discussion and Implications: This study tested SB, NB,
and NB + AB on cognition in healthy older adults. Although longer interventions of
diet and exercise may affect cognitive function in older adults, our results suggest

these lifestyle factors do not have acute effects.

Dedication

Patri meo

iii

Acknowledgements

Thanks to Jill Slade, Ph.D., and Joe Carlson, Ph.D., R.D., who directed and
supported this study. Dr. Slade is an assistant professor in the Department of
Radiology at Michigan State University. After vast amounts of background
research and collaboration in study design, Dr. Slade devoted countless hours to
every aspect of the study from planning through data analysis. She was the driving
force behind developing study components including the CogState, Eriksen ﬂanker
paradigm, and fMRI pulse sequence, and was essential in the day-to-day study
operations, helping with data collection from pre-visit preparation through the fMRI
portion. Dr. Slade demonstrated her immense conﬁdence in my potential with her
high expectations for me.

Dr. Carlson is an associate professor in the Departments of Radiology and
Food Science and Human Nutrition at Michigan State University. I am grateful to
him for introducing me to the study during its early stages of planning. His
expertise in nutrition and exercise physiology was helpful in designing and
implementing the study. In addition, Dr. Carlson generously dedicated his scarce
time to assist with some of the exercise sessions in the study. He also provided
input on analysis, interpretation, and presentation of study results pertaining to
nutrition and exercise.

This project would not have been possible without the generous help of
Kiran Sarikonda, MD, an Assistant Professor in the Department of Medicine at

Michigan State University, and an American Cardiac Life Support certiﬁed

iv

physician. Despite numerous obligations, Dr. Sarikonda generously volunteered his
time and was present at each exercise session.

David Zhu, Ph.D., is an assistant professor in the Departments of
Psychology and Radiology at Michigan State University. Throughout the project,
he has enthusiastically shared his much-needed expertise in aging, brain function,
and functional magnetic resonance imaging. He has assisted in all phases of the
study from the planning through the analysis. Dr. Zhu also helped to run the fMRI
portion of the study during those times when a subject was scheduled but an expert
was needed to run the MRI scanner.

Colleen Hammond, MRI Services Manager, offered her assistance far
beyond any sense of duty. She cheerfully conducted the vast majority of the blood
draws in our study, many of them in the early hours of the morning or at a moment’s
notice. In addition, Colleen helped to run the MRI scanner various times to
facilitate the study.

Jeff Ambrose, 8.8., was a tremendous help during data collection by helping
during participant visits. Jeff contributed to the study by becoming familiar enough
to be able to help throughout the entire participant visit, from breakfast preparation
through participant ﬁnal checkout and documentation. I was able to depend on him
to conduct any part of the study that was needed and to meet unexpected challenges.
Malak Saddy, B.S., helped prepare various data sets for analysis. Bob McClowry
was also instrumental in preparing data for analysis. Scott Sehnert, MS, RD,
purchased some breakfast foods. Thanks to Stacey Ladri g, M5, for her role in the

preparation of this document.

I am grateful to the Department of Radiology for funding the study as well
as providing facilities and the MRI scanner to complete the study.

Thank you to all of the study participants, who patiently and willingly gave
up their time to participate in the study, which was a signiﬁcant commitment for
each of them.

I would especially like to thank Dale Romsos, Ph.D., professor in the
Department of Food Science and Human Nutrition at Michigan State University. It
has been a true privilege to know him as a professor, undergraduate advisor, and
graduate committee member. He has always made a clear effort to be available to
give advice every time I have asked him. As a member of my graduate committee
from the beginning, Dr. Romsos has consistently provided insightful
recommendations for academics and research. He has given limitless help in the
preparation of this document by proofreading it numerous times, giving useful
suggestions, and continuing to motivate me to complete it. I hold the deepest
respect for him and can never thank him adequately for all that he has done for me .

throughout my career at Michigan State University.

vi

Table of Contents

List of Tables ........................................................................................................... viii
List of Figures ............................................................................................................ ix
Key to abbreviations ................................................................................................... x
Chapter I: Introduction, Background, and Speciﬁc Aims .......................................... 1
Introduction ............................................................................................................ 1
Speciﬁc Aims and Hypotheses ............................................................................... 5
Review of the Literature ......................................................................................... 6
Aging and the brain ............................................................................................ 6
Diet and the brain ............................................................................................... 9
Exercise and the brain ....................................................................................... 19
Methods of measuring brain function ............................................................... 24
Methods of measuring brain activation ............................................................ 27
Study rationale .................................................................................................. 31
Chapter H. Acute effects of diet and exercise on cognitive function and brain
activation in an aging population ............................................................................. 37
Abstract ................................................................................................................. 37
Introduction .......................................................................................................... 38
Materials and Methods ......................................................................................... 41
Discussion ............................................................................................................. 74
Chapter IH: Conclusions ........................................................................................... 81
Population ............................................................................................................. 82
Study Meals: Standard Breakfast and Nutrient Dense Breakfast ......................... 84
Exercise ................................................................................................................ 89
CogState® ............................................................................................................ 95
Eriksen Flanker and Brain Activation .................................................................. 98
Study Strengths ................................................................................................... 101
Study Limitations ............................................................................................... 104
Future directions and implications ..................................................................... 105
Appendices ............................................................................................................. 108
Bibliography ........................................................................................................... 160

vii

List of Tables

Table 1: Nutrients in the 700 kilocalorie breakfasts ................................................. 46
Table 2: Composition of the 700 kilocalorie breakfasts ........................................... 47
Table 3: CogState® battery of tests .......................................................................... 52
Table 4: Participant characteristics ........................................................................... 57
Table 5: Average habitual daily intake of study participants. .................................. 59
Table 6: Timing of data collection ........................................................................... 60
Table 7: Flanker results ............................................................................................ 68
Table 8: Participant report for accuracy ................................................................. 135
Table 9: Participant report for speed ...................................................................... 136
Table 10: Participant report for bloodwork ............................................................ 137
Table 11: Nutrient comparisons for females .......................................................... 140
Table 12: Nutrient comparisons for males ............................................................. 141
Table 13: Flanker results for participants with MRI data ..................................... 145

viii

List of Figures

Figure 1: Schematic of protocol. .............................................................................. 44
Figure 2: Fasting and postprandial serum concentrations of glucose ....................... 62
Figure 3: Fasting and postprandial serum concentrations of insulin ........................ 63
Figure 4: Fasting and postprandial serum concentrations of triglycerides ............... 64
Figure 5: CogState® battery results for response accuracy ..................................... 66
Figure 6: CogState® battery results for response speed ........................................... 67
Figure 7: MRI percent signal change in IFG / MFG / PCG .................................... 70
Figure 8: fMRI percent signal change in MFG / BA6 .............................................. 71
Figure 9: Group BOLD response in IFG / MFG / PCG ........................................... 72
Figure 10: Group BOLD response in MFG / BA6 ................................................... 73
Figure 11: Correlations between physical activity and study outcomes ................ 142
Figure 12: BOLD percent signal change in IFG / MFG / PCG .............................. 146
Figure 13: BOLD percent signal change in MFG / BA6 ........................................ 147
Figure 14: Individual impulse-response functions (A-K) ...................................... 148

ix

Key to abbreviations
A anterior
AD Alzheimer’s disease
AE aerobic exercise
BDNF brain-derived neurotrophic factor
BOLD blood oxygenation level-dependent
CBF cerebral blood ﬂow
EEG electroencephalography
fMRI functional magnetic resonance imaging
HRR heart rate reserve
I inferior
IFG inferior frontal gyrus
L left
MCI mild cognitive impairment
MEG magnetoencephalography
MFG middle frontal gyrus
MMSE Mini-Mental State Examination
NB nutrient dense breakfast
P posterior
PET positron emission tomography

PCG pre-central gyrus

R right
S superior

SB standard breakfast

xi

Chapter I: Introduction, Background, and Speciﬁc Aims
Introduction

Overall, the percentage of older adults is increasing in the United States. By
increasing at the rate of 1.2% per year, the American population aged 65 years and
over is growing faster than any other segment of the population (NCHS 2009).
Within the next 20 years, adults aged 65-74 years old are expected to comprise 10%
of the population (Day 2001). Aging is associated with many chronic health
conditions, and there has been much focus on these age-related conditions.
Cognitive decline, especially in executive function, is a normal part of aging
(Mitchell 2009). Executive function is required for higher order cognitive
processing tasks such as decision making, attention, and memory (Alan, Clemens et
a1. 2006) (Berkman, Seeman et a]. 1993), and performance on these tasks can
decrease with age (West, Schwarb et a1. 2009). Interest in understanding cognition
during aging has prompted numerous studies investigating factors that may
inﬂuence the brain during the aging process.

A wide variety of components combine to affect age-related cognitive
function and decline. Some of these components, such as age, gender, and genetics
are unalterable. However, other factors that likely affect brain health are modiﬁable
through lifestyle choices. Diet and physical activity (PA) are two such factors that
may affect cognition during the aging process. Numerous studies have suggested
associations between habitual diet and physical activity and cognitive function or
decline in older adults (Vaynman and Gomez-Pinilla 2005; Lichtenstein, Appel et

a1. 2006). In addition, some dietary factors and some forms of exercise have the

l

potential to acutely affect cognition (Hindmarch, Quinlan et al. 1998; Tomporowski
2003; Chui and Greenwood 2008).

Dietary factors refer to a wide range of components including dietary
patterns and individual foods, as well as isolated or speciﬁc nutrients. Both foods
and food components have been studied for their possible effects on the brain.
Various studies investigating the effects of diet on the brain have found that
different components can positively or negatively inﬂuence cognitive function
(Gillette Guyonnet, Abellan Van Kan et al. 2007). For example, some
epidemiological studies have found positive correlations between cognitive function
and ﬁsh intake (Nurk, Drevon et al. 2007), or between cognitive function and
vitamin E intake (Masaki, Losonczy et al. 2000). Other factors that may be
beneﬁcial for cognition include consumption of vitamin C, total antioxidants,
certain polyunsaturated fatty acids, and folic acid. Negative factors include
saturated fat intake and simple sugars (Molteni, Barnard et al. 2002).

Physical activity is another modiﬁable lifestyle factor that may greatly affect
cognitive health. Physical activity and exercise has been associated with higher
cognitive performance (Weuve, Kang et al. 2004) or slower cognitive decline during
aging (van Gelder, Tijhuis et al. 2004). Various epidemiological studies have
looked at both physical ﬁtness and habitual exercise. Results have ran ged from
higher cognitive performance or slower cognitive decline with increased habitual
physical or ﬁtness to no correlations with cognition. In contrast to these long-term
studies, other studies have investigated the acute effects of a single bout of exercise

on cognitive performance. Some have shown that a bout of exercise can improve

performance on certain cognitive tasks (Hillman, Snook et al. 2003; Kashihara,
Maruyama et al. 2009). These studies have typically been conducted in healthy
younger adults.

Multiple cognitive tests can be used to detect cognitive impairment or assess
cognitive function (Wild, Howieson et al. 2008; de Jager, Schrijnemaekers et al.
2009). One such tool of cognitive tests is CogState® (CogState® Limited,
Melbourne, Australia). The CogState® tests for research are a battery of
computerized tests, with each short test designed to test speciﬁc cognition domains.
The research investigator can choose which individual tests to administer based on
the cognitive function that is to be evaluated in the study.

Aside from cognitive performance, another aspect of cognitive function is
brain processing during cognitive tasks. While the CogState® exam can evaluate
cognitive performance, brain imaging, particularly functional magnetic resonance
imaging (MRI) may provide insight into pathways that are used during cognitive
tasks. Also, these techniques help assess processing speed. This technique depends
on local blood oxygenation levels in the brain that change in conjunction with
neural processing. The basis of MRI is a change in net magnetization of speciﬁc
brain regions. Neuronal ﬁring, such as in response to a task, leads to changes in
local blood ﬂow and oxygenation. The use of fMRI allows determination of which
speciﬁc brain regions are being used to complete cognitive tasks and the degree of
activation by evaluating the magnitude of the MRI signal.

Established cognitive tasks can be used to intentionally activate speciﬁc

brain regions. This allows the investigation of differences in brain activity due to

different treatments such as a meal or exercise. Different treatment groups might
show different magnitudes of change in activation, or show differences in relative
activation of different parts of the brain. For example, correct performance of the
Eriksen ﬂanker task requires decision making, conﬂict resolution, and inhibition
skills (Ochsner, Hughes et al. 2009) and therefore can activate brain regions
responsible for these different processes. Diet or exercise could potentially affect
these skills. Using altered brain pathways or requiring more or less mental effort
could be detectable if these alterations cause differences in MRI signal location or
magnitude.

It appears that diet and exercise are important elements in inﬂuencing brain
health. Current evidence suggests that habitual diet and exercise patterns affect
cognitive function long term in older adults. In addition, certain dietary components
consumed in one session (Chui and Greenwood 2008) or single bouts of exercise
(Netz, Tomer et al. 2007; Pontifex, Hillman et al. 2009) have been found to acutely
improve cognitive function in healthy younger adults. However, there have been no
studies on the acute effects of a single meal and bout of exercise in an aging
population. Determining their effects on cognitive function and brain activation
would help increase understanding of how diet and exercise affect the brain.

The overall objective of the study was to assess the effects of a standard
breakfast (SB), a nutrient dense breakfast (NB), and a nutrient dense breakfast plus
aerobic exercise (AB) on cognitive function and brain activation in an aging

population.

We compared the acute effects of a single meal: a standard breakfast (SB),
modeled after a realistic American breakfast, and a nutrient dense breakfast (NB).
The SB diet included components thought to have negative effects on the brain,
including high amounts of saturated fat and sugar. The NB included dietary
components associated with improved cognition or endothelial function, including
whole-grain bread and cereals, antioxidants, fruit and vegetables, and omega-3 fatty
acids. Another condition (NB + AE) included a session of aerobic exercise (AE) at
an intensity between 50% and 65% of their heart rate reserve (HRR) after
consuming the NB meal. After the meal and exercise bout or control condition,
cognitive function was evaluated based on performance on the CogState® tests.
Brain activation was assessed using MRI to determine the brain blood oxygen
level-dependent (BOLD) response (Buxton, Uludag et al. 2004) during functional
magnetic resonance imaging (MRI) to determine activation in interference regions
of the brain during an Eriksen ﬂanker test with congruent and incongruent

conditions.

Speciﬁc Aims and Hypotheses

Aim 1: Assess the acute effects of a standard breakfast (SB) and nutrient
dense breakfast (NB) on cognitive function and brain activation.
Hypothesis 1:

a. Compared to SB, NB will improve performance on a battery of

cognitive tasks.

b. Compared to SB, NB will increase brain activation during executive
function processing in the inferior frontal gyrus (IFG) and middle frontal gyrus
(MFG).

Aim 2: Assess the acute effects of a bout of moderate aerobic exercise (AB)
on cognitive function and brain activation.

Hypothesis 2:

a. Compared to SB or NB alone, NB + AE will improve performance on a

battery of cognitive tasks.

b. Compared to SB or NB alone, NB + AE will increase brain activation

during executive function processing in the inferior frontal gyrus (IFG)

and middle frontal gyrus (MFG).

Review of the Literature
Aging and the brain

The population of the United States is aging, with adults over 65 years old
composing 12.4% of the population according to the 2000 Census. Older adults are
also the fastest growing segment of the population and are projected to make up
19.7% of the nation’s population by the year 2030 (Census 2001). The rapid growth
of this population segment is associated with age-related health conditions (CDC
2009). These conditions include increased risk for cardiovascular disease and
osteoporosis, and changes in vision or hearing (CDC 2009). The brain is also
commonly affected in aging, leading to conditions ranging from mild cognitive
impairment (MCI) to Alzheimer’s disease (AD). Cognitive dysfunction can also

occur during normal aging (Thomas, Darvesh et al. 2001), and age-related cognitive

6

impairment is a signiﬁcant contributor to disability in aging (Desai, Grossberg et al.
2004). Cognitive decline is associated with increased mortality and decreased
quality of life (McGuire, Ford et al. 2006).

Changes in the brain that occur during aging are evident as changes in
physiology and in cognitive performance, which may be associated with each other
(Charlton, Barrick et al. 2009). Age—related changes in brain anatomy include
decreases in gray matter volume, in the superior, middle, and medial frontal brain
regions, and superior parietal brain regions (Driscoll, Davatzikos et al. 2009).
Another age-related change in brain physiology is blood ﬂow. During the
progression of AD, increased vasoconstriction impairs cerebral blood ﬂow (CBF)
and likely promotes the cognitive impairment evident in AD (Farkas and Luiten
2001). In fact, decreased CBF was subsequently associated with increased areas of
white matter hyperintensities, which are associated with aging and may result from
reduced local perfusion (Brickman, Zahra et al. 2009).

During aging, changes in cognitive function, especially executive function,
are widely recognized (West, Schwarb et al. 2009). Executive function can be
deﬁned as “computational processes involved in the selection, scheduling and
coordination of complex cognitive functions” (Hillman, Erickson et al. 2008), and it
is likely to be impaired during normal aging (Jones, Nyberg et al. 2006). Age-
related declines in cognitive function are evidenced through decreased performance
in tasks such as working memory (Charlton, Schiavone et al. 2009), inhibition and
executive function (Henry, von Hippel et al. 2009), word generation (Heuer,

Janczyk et al. 2009), episodic memory (Charlton, Barrick et al. 2009), attention

(Silver, Goodman et al. 2009), and cognitive processing speed (Kennedy and Raz
2009; Zanto, Toy et al. 2009).

Although some regional changes in brain volume are normal, changes may
be linked to impaired cognition. For example, one study showed that individuals
with smaller hippocampus and frontal lobe volumes also had lower executive
function. As this cohort was followed over 15 years, these individuals displayed
more rapid decline in executive function than individuals with larger hippocampus
and frontal lobe volumes at baseline (Cardenas, Chao et al. 2009). Results of the
aforementioned Driscoll study were similar. Accelerated changes were measured in
individuals diagnosed with MCI compared to controls in whole brain volume,
orbitofrontal cortex (OFC) volume and temporal gray matter (Driscoll, Davatzikos
et al. 2009). Further links between brain anatomy and cognitive function are
apparent with the association between neuroanatomical degradation and impaired
performance on executive function tasks including processing speed, working
memory, inhibition, task switching, and episodic memory (Kennedy and Raz 2009).
Older adults (74 years) have a smaller increase in frontal lobe CBF than younger
adults (30 years old) in response to executive function (word stem completion and
visual search) tasks measured by transcranial Doppler ultrasound (Sorond, Schnyer
et al. 2008). Diffusion tensor imaging techniques have shown associations between
white matter integrity and processing speed, working memory, inhibition, task
switching, and episodic memory (Kennedy and Raz 2009).

Age-related changes in the brain, reviewed by Baquer et al. (2009), include

metabolism, structure, and function (Baquer, Taha et al. 2009). The nature of these

changes suggests that they are not necessarily inevitable. Theories of causes of
aging, such as oxidative stress, imply that age-related cognitive changes might be
modiﬁable through lifestyle factors. In fact, predictors of cognitive decline or
maintaining cognitive function during aging include modiﬁable lifestyle factors
such as exercise or smoking habits (Yaffe, Fiocco et al. 2009). Evidence supports
the hypothesis that some age-related cognitive changes are inﬂuenced by modiﬁable

lifestyle factors, including nutrition and physical activity.

Diet and the brain

Issues in conducting dietary studies

This section describes some of the many challenges in studying effects of
diet on brain function. The above section describes mechanisms that may contribute
to age-related changes in the brain. The routes of these potential mechanisms
suggest that factors affecting aging in the brain may be modiﬁable, and these may
encompass lifestyle factors including diet and exercise. Various dietary patterns and
food components have been studied for their potential inﬂuence on brain health, but
their acute effects in aging adults are not fully understood. Likely beneﬁcial dietary
factors include fruits and vegetables (Polidori, Pratico et al. 2009), ﬁsh and other
sources of long chain polyunsaturated fatty acids (Nurk, Drevon et al. 2007),
vitamins such as folate and vitamin B 12 as well as the antioxidant vitamins either
from the diet or from supplements, and other dietary factors such as ﬂavanols and
polyphenols from plant-based food sources (Fisher, Sorond et al. 2006). In addition,
saturated fat and sugars have been investigated as having harmful effects (Morris,

Evans et al. 2004).

The acute effects of dietary components on cognition during aging have not
been extensively studied. Instead, studies in aging adults have been long-term
observational studies. The beneﬁts of observational studies such cohort or case-
control studies include lower cost, and greater feasibility. However, cause and
effect relationships cannot be determined from these kinds of studies. Intervention
studies or clinical trials can test hypotheses about cause and effect, but they are
more expensive and time-consuming than observational studies. As a result, much
of the existing literature on humans is focused on long-term associations between
diet and exercise and cognition in aging adults, or on acute cause and effect
relationships of diet and exercise on cognition in younger populations.

Because of the difﬁculties in conducting controlled experimental studies in
humans, efforts to elucidate the potential effects and mechanisms of diet on
cognition during aging have led to animal studies. These have a variety of
economic and logistical advantages. Unlike human studies, which are limited by
practical considerations like palatability, subject compliance, and cost, researchers
can determine dietary composition in animal studies, and can therefore focus on the
component of interest by manipulating the amount present in the diet. Animal
feeding studies also include the opportunity to accurately measure consumption,
which is difﬁcult in humans due to misreporting. This is a particular challenge
when studying older adults (McNeill, Winter et al. 2009). In addition to controlling
dietary factors, researchers can control living and testing conditions in animals, and
minimize the potential confounding effect of differing individual responses to

treatment by using litterrnates. Finally, the short life span of animals compared to

10

humans means faster aging and therefore the ability to determine age-related
changes more quickly than in humans.
Fruit and Vegetables

A number of studies have evaluated the effect of fruits or vegetables on
cognition during aging. For example, vegetable intake has been examined as a
possible factor in cognitive health during aging. In one study, cruciferous and green
leafy vegetables had an apparent protective effect against cognitive decline using
global assessment in women over 70 years old, and higher consumption of green
leafy vegetables was associated with higher scores in episodic memory tests.
However, consumption of other vegetables was not associated with higher test
scores (Kang, Ascherio et al. 2005). A study by Morris et al. found that higher
consumption of vegetables but not fruit was associated with slower cognitive
decline in men and women over age 65 (Morris, Evans et al. 2006). In addition to
these epidemiological human studies, some investigative animal studies have shown
a relationship between fruit and vegetables and cognition (Joseph, Shukitt—Hale et
al. 2005). In aged rats, eight week supplementation with strawberry, spinach, or
blueberry extracts led to improvements in spatial memory compared to a control
group (Joseph, Shukitt-Hale et al. 1999). Furthermore, blueberry supplementation
restored long term potentiation and synaptic strength (measured by the excitatory
post-synaptic potential), which decrease in normal aging, back to the level of young

rats (Coultrap, Bickford et al. 2008).

11

Dietary F at

In addition to fruit and vegetables, dietary fat and sugar have long been
under consideration as important factors in cognitive function and brain health
during aging. As reviewed by Youdim et al., dietary lipids likely affect brain
physiology and consequently affect brain function (Youdim, Martin et al. 2000). In
the aging brain, membrane concentration of docosahexaeneoic acid can decline,
leading to impaired cognition. This long chain n-3 essential fatty acid may protect
against age-related cognitive impairment because it stimulates antioxidant defenses
(Wu, Ying et al. 2004), counteracts impairment of spatial memory induced by
production and accumulation of beta amyloid, a peptide toxin, (Hashimoto, Tanabe
et al. 2005), normalizes metabolism (Tsukada, Kakiuchi et al. 2000), and improves
synaptosomal membrane ﬂuidity. Furthermore, docosahexaeneoic acid
concentrations are inversely correlated with diabetes and cardiovascular disease
(Cole, Ma et al. 2009).

Because of these mechanisms, intake of fatty acids has been hypothesized to
impact cognitive status during aging. The effect of n-3 fatty acids is reasonably
expected to be beneﬁcial, and the effect of saturated fatty acids negative. As in
other areas of health, such as cardiovascular, the potential effects of n-6 and
monounsaturated fatty acids are not clear. Surprisingly, evidence is mixed
regarding the possible protective effects of n-3 polyunsaturated fatty acids on brain
health. These fatty acids appear protective against AD (Cole, Ma et al. 2009), likely
through incorporation into phospholipid membranes over time as shown in rats

(Petursdottir, Farr et al. 2008). In a human cohort study, n-3 polyunsaturated fatty

l2

acid intake was not associated with incidence of dementia over an average follow-
up of six years among participants aged 55 years at baseline (Engelhart, Geerlings et
al. 2002). In addition, a study showing inverse associations of ﬁsh consumption
with cognitive impairment ruled out the n—3 polyunsaturated fatty acid found in ﬁsh
by controlling for overall n-3 polyunsaturated fatty acid intake as the potential
reason for the observed relationship (Morris, Evans et al. 2005).

Studies have also been conducted to investigate dietary fats other than n-3
polyunsaturated fatty acid, and most show neutral or negative effects on cognition.
In a prospective longitudinal study among adults with an average baseline age of 68
years, linoleic acid, an n-6 polyunsaturated fatty acid, was weakly associated with
cognitive decline (Kalmijn, Janssen et al. 2000). However, in the same cohort
mentioned above, neither n-6 polyunsaturated fatty acid nor total polyunsaturated
fatty acid or monounsaturated fatty acid intake were associated with incidence of
dementia (Engelhart, Geerlings et al. 2002). A different cohort study increased the
evidence for the role of dietary fat with its ﬁndings that consumption of
monounsaturated fatty acid and the ratio of polyunsaturatedzsaturated fatty acid
intake were each inversely associated with cognitive decline over 6 year follow-up
period of participants with a baseline age of at least 65 years. Saturated fat and
trans fat were associated with cognitive decline in this study (Morris, Evans et al.
2004), but not in the Rotterdam study, which also found no associated cognitive

decline with total fat or cholesterol intake.

1.3

Dietary sugar

Substantial evidence supports the signiﬁcance of blood sugar levels in brain
health. It is apparent that chronic conditions associated with impairment of
glycemic regulation in the blood are also associated with increased risk for a variety
of cognitive disorders. In an excellent review, Roriz-Filho et al. discuss cognitive
effects of diabetes and impaired glucose tolerance during aging, including
impairments in cerebral vasculature function. Over time, these conditions are
associated with decreases in cognitive performance and differences in brain
anatomy compared to healthy adults (J, Sa—Roriz et a1. 2009).

In humans, there has been much focus on the cognitive effects of altered
glycemic control occurring during insulin resistance and type 2 diabetes (T2D).
Insulin resistance and T2D are each risk factors for developing AD and vascular
dementia (Yaffe, Blackwell et al. 2004; J, Sa—Roriz et al. 2009). Both T2D and pre-
diabetes are associated with impaired cognitive performance and the development of
impaired cognitive performance among post-menopausal women (Yaffe, Blackwell
et al. 2004), and hyperglycemia is associated with other neuropsychiatric disorders
(Bruce, Harrington et al. 2009). Imaging studies have substantiated these negative
effects of dysregulation of blood glucose; for example, an MRI study found that
atrophy of the hippocampus and amygdala was associated with T2DM (den Heijer,
Vermeer et a1. 2003)

Aside from the cognitive effects of impaired glucose metabolism associated
with hyperglycemia, dietary intake of simple sugars may also have negative effects

on cognition and the brain through direct and indirect mechanisms. EXCess sucrose

14

exposure and intake can lead to obesity (Cohen 2008), and obesity in middle age
and later in life has been associated with increased risk of AD development (Mrak
2009), possibly due to changes in cerebral vasculature. More direct evidence of the
negative impact of sucrose on cognition is apparent in a study on diet—induced obese
rats. Rats given access to a sucrose solution or a high fat diet became obese, but
only the sucrose group displayed impaired spatial learning and memory, while the
fat-exposed group did not show impaired cognition (J urdak, Lichtenstein et al.
2008). A different study, in otherwise healthy rats, found that recovery after
traumatic brain injury was impaired in rats on a high sugar diet compared to rats on
a normal chow diet. In addition, levels of BDNF were lower in the high sugar
group, which implies that dietary sugar could decrease synaptic plasticity and impair
learning ability (Molteni, Barnard et al. 2002; Molteni, Wu et al. 2004). These
studies together imply that dietary sugar may be a signiﬁcant factor in cognitive
function, but further acute studies need to be conducted to determine effects on
cognitive performance and brain activation.

Antioxidants (vitamins C and E)

Because cognitive decline has been linked to lipid peroxidation in the brain,
dietary antioxidants are likely to positively inﬂuence cognitive status. Particular
emphasis has been on vitamins E and C. Results have been mixed, with some
studies ﬁnding a beneﬁcial effect of vitamins E and C, and other studies ﬁnding no
association with cognitive performance or cognitive decline. In a two year clinical
trial, vitamin E supplementation ameliorated cognitive decline in AD (Sano, Ernesto

et al. 1997). In the Honolulu Aging Study, initial data analysis showed a

15

dramatically reduced odds ratio (0.12) of developing vascular dementia in men aged
71-94 who took vitamin E and C supplements compared to those who did not
supplement (Masaki, Losonczy et al. 2000). Other studies have found either weak
or no associations between vitamins E and C intake or status and the risk of
cognitive decline or AD incidence (Riviere, Birlouez-Aragon et al. 1998; Foy,
Passmore et al. 1999). Conclusions are further complicated by misalignment of
what would be expected to be adequate dietary intake and the ﬁnding that AD and
Parkinson disease patients may have lower plasma antioxidant capacity than in
controls despite nutritional adequacy, suggesting differential utilization of these
antioxidant vitamins in cognitive impairment.
F olic acid and Vitamin BIZ

Folic acid and vitamin B 12 are two other vitamins that have been implicated
as possible factors in brain health during aging. Older adults are at particular risk of
vitamin B 12 deﬁciency because of age-related impairments in the production of
intrinsic factor, which is required for vitamin B12 metabolism. Vitamin BIZ and
folate are known to be important in overall neurological health, since folic acid
deﬁciency is linked to neural tube defects in fetuses, and vitamin B12 deﬁciency
causes neurological symptoms related to nerve damage (Black 2008). In addition,
both of these vitamins are necessary coenzymes in the cycle that produces
methionine during the metabolism of homocysteine, and higher homocysteine levels
are associated with impaired cognition (Lehmann, Gottfries et al. 1999) and more
atrophy of the hippocampal and cortical regions of the brain in healthy aging adults

(den Heijer, Vermeer et al. 2003). It is not yet certain whether supplemental or

16

pharmaceutical doses of folic acid and vitamin B12, above the RDIs, might have
positive effects on cognition. Instead, consuming adequate amounts of these
vitamins might prevent the neurological symptoms associated with deﬁciency, with
excess intake of these nutrients leading to no further improvements in cognitive
function (Vogel, Dali-Youcef et al. 2009). The potential cognitive effects of each
nutrient are difﬁcult to independently determine because of their close relationship
to each other regarding metabolism, function, consequences of deﬁciency, and
potential effects of supplementation.

The suggested effects of these vitamins on cognition during aging has
generated considerable interest and led to numerous studies. Investigations have
focused on dietary intake (Kang, Ascherio et al. 2005; Devore, Grodstein et al.
2009) as well as serum status (Morris, Jacques et al. 2007) of these nutrients in
subjects ranging from healthy participants (Plotnick, Corretti et al. 1997) to those
with AD (Aisen, Schneider et al. 2008). Long term epidemiological studies have
had mixed results, and may depend on factors such as nutrient status. For example,
a high serum folate level (top quintile) appeared be protective against cognitive
impairment among participants with normal serum B12 levels, but was associated
with faster cognitive decline in participants whose serum levels of vitamin 312 were
deﬁcient (Morris, Jacques et al. 2007). Similar studies have shown mixed results,
ranging from promising suggestions that folic acid or vitamin B12 are associated
with cognition (Balk, Raman et al. 2007), to no association of dietary intake with
AD (Morris, Evans et al. 2006), to increased rate of cognitive decline with higher

serum folate levels (Morris, Evans et al. 2005). In an experimental study among

17

participants aged 50-70 years old, supplementation for three years of 800
micrograms daily of folic acid led to slower declines in selected cognitive domains
compared to a placebo group (Durga, van Boxtel et al. 2007).
Other nutrients

In addition to the dietary factors discussed above, a variety of other
nutritional factors may also affect cognitive performance and brain activation. They
are too numerous to be discussed individually in great detail, but include vitamins,
supplements, herbals, botanicals, and non-nutrient factors including phytochemicals.
Much of the existing data about these nutrients in humans is from cross-sectional or
epidemiological studies rather than experimental interventions. For example, a
higher vitamin D intake was associated with higher cognitive functioning in age 65-
99 black and white elders (Buell, Scott et al. 2009). Other cross-sectional studies
using self reported dietary intake found that curcumin (Ng, Chiam et al. 2006) and
lignans (Kreijkamp-Kaspers, Kok et al. 2007) were associated with less cognitive
impairment among an older population. It is difﬁcult to draw ﬁrm conclusions; for
example, lignans are found in foods associated with health and potential dietary
factors improving cognition, so the results found in the aforementioned study may
not be due solely to lignan intake. Other factors potentially associated with
cognition include garlic (Borek 2006) and soy isoﬂavones (Lee, Lee et al. 2005).
However, it is impossible to determine cause and effect from observational studies.
Some intervention studies have suggested the efﬁcacy of ginkgo biloba (Kanowski,

Herrmann et al. 1996), ginseng (Petkov, Kehayov et al. 1993; Wesnes, Ward et al.

18

2000), and guarana (Kennedy, Haskell et al. 2004; Kennedy, Haskell et al. 2008)
extracts.

Instead of focusing on single nutrients or components, some studies have
investigated the effects of dietary patterns, groups of nutrients, or whole foods.
Fruits and vegetables are a common focus in these studies, and polyphenols from
fruit may be beneﬁcial for cognition, according to a review (Lau, Shukitt-Hale et al.
2005). Adherence to a Mediterranean dietary pattern has been associated with a
lower risk of AD. This dietary pattern is high in fruit and vegetables, legumes,
cereals, and monounsaturated fats mainly from olive oil, low in meat and poultry
and saturated fat, and moderate in low fat dairy and red wine (Scarmeas, Luchsinger
et al. 2009). Studies have found that strawberry and blueberry extract decrease
oxidative stress in aging rats. Also, spinach, strawberry and blueberry increase
vitamin E concentrations in brain, which could also lead to decreased detrimental
oxidative stress. Spinach, strawberry, and blueberry dietary supplementation in
these rats also improved memory compared to control groups (Joseph, Shukitt-Hale
et al. 1999). Antioxidants are another broad group of nutrients that may decrease
cognitive impairment. One type of antioxidants, ﬂavanols, such as those found in

cocoa, may decrease onset of AD and MCI (Patel, Rogers et al. 2008).

Exercise and the brain

Eﬁects of chronic exercise and physical ﬁtness on cognition
Physical activity is a lifestyle factor with a multitude of health beneﬁts.
Regular exercise decreases the risk of many conditions such as type 2 diabetes,

metabolic syndrome, obesity (Brown, Avenell et al. 2009), stroke (Hooker, Sui et al.

19

2008), and hypertension (Brown, Avenell et al. 2009). Other beneﬁts of exercise
include promoting wound healing in low-risk punch biopsy (Emery, Kiecolt-Glaser
et al. 2005) and after brain injury (Devine and Zafonte 2009), improving sleep
quality (Cintra, Poyares et al. 2009), and improving mental health (Williams and
Lord 1997). Another likely beneﬁt of exercise is improved cognitive function.
Positive effects of exercise on cognition have been noted throughout the human
lifespan, from fetal brain development to cognitive performance in late adulthood
(Hillman, Erickson et al. 2008). Exercise may affect cognitive function and the
brain long term and acutely.

Chronic exercise may have beneﬁcial effects on cognition during aging
through a variety of mechanisms. Individuals with habitually higher levels of
exercise or better physical ﬁtness may have enhanced cognitive performance or
decreased rate of cognitive decline compared to sedentary individuals (Colcombe,
Kramer et al. 2004). Many possible mechanisms could explain these potential
advantages of exercise. Effects of exercise related to cognition include increased
neuroplasticity and neurogenesis, changes in endothelial function, and increased
brain volume. Exercise stimulates the hippocampus to produce brain-derived
neurotrophic factor (BDNF), which is a neurotrophic factor that promotes
neurogenesis and is also associated with learning and memory (Minichiello, Korte et
al. 1999). Another explanation for the proposed effects of exercise on cognition is
the cardiovascular ﬁtness hypothesis, which is the link between physical ﬁtness and
cerebral blood ﬂow (CBF) (Swain, Harris et al. 2003; Ainslie, Cotter et al. 2008),

cerebral structure (Colcombe, Erickson et al. 2003), and BDNF levels (Vaynman

20

and Gomez-Pinilla 2005). Exercise can improve vascular function by increasing
vascular elasticity (Yung, Laher et al. 2009). Taken together, these physiological
effects of exercise could conceivably improve cognition through their actions on the
brain. However, studies have not directly tested whether these mechanisms explain
the role of exercise on cognition in aging.

Some experimental studies have tested the effects of exercise on the brain.
In one study among healthy older adults (average age 66 years), a six month aerobic
training regimen consisting of walking for three times a week for 30 minutes found
improved interference processing in healthy older adults compared to a stretching
and toning control group (Colcombe, Kramer et al. 2004). A separate study among
slightly younger (average age 57 years) clinically depressed adults tested the effects
of a four month exercise intervention, and found improvements in executive
function related to exercise compared to antidepressant treatment (Khatri,
Blumenthal et al. 2001).

So far, a number of epidemiological studies have investigated the
associations between physical activity and cognition during aging. Evidence is not
sufﬁcient to conclude a causal effect of exercise on cognition during aging, but
many studies investigating the effects of exercise or physical ﬁtness have found
associations with cognition. These measures include cognitive performance (Bixby,
Spalding et al. 2007) and slower rate of cognitive decline (Morris, Evans et al.
2005) during aging.

A meta-analysis on the effect of physical ﬁtness on cognitive health in older

adults concluded that there is an association between ﬁtness and cognitive

21

performance (Colcombe and Kramer 2003). Another meta-analysis concluded that
exercise training programs in elderly subjects with cognitive impairment or
dementia increase ﬁtness, physical and cognitive function, and positive behavior
(Heyn, Abreu et al. 2004). Because of the implied link between ﬁtness or training
and cardiovascular adaptations, both of these studies support the cardiovascular
ﬁtness hypothesis that links physical ﬁtness to improved cognition. However, in a
separate study, a meta-regression was conducted to determine the effect of changes
in ﬁtness on cognition. While exercise apparently played a role in cognition, it was
unclear whether ﬁtness was even a positive or a negative factor. Factors such as age
and health showed a bigger effect size than physical ﬁtness, and in fact, increases in
physical ﬁtness were negatively associated with cognitive performance changes
(Etnier, Nowell et al. 2006). As with studies on dietary factors, published evidence
is not yet sufﬁcient for making deﬁnitive statements on associations between
exercise and cognition.
Acute effects of exercise on cognition

Observed effects of physical ﬁtness and regular exercise on cognition are
likely related to adaptations that result from regular exercise. There are also several
acute physiological changes with acute exercise that may improve cognitive
function. One study found that acute exercise led to an increase in brain-derived
neurotrophic factor (BDNF) as well as improved cognitive performance on a stroop
task (Ferris, Williams et al. 2007). Other acute effects of exercise include exercise-
induced changes in blood ﬂow and endothelial function. A normal response to

exercise includes increased blood ﬂow to peripheral tissues through vasodilation,

22

possibly through induction of endothelial nitric oxide (Endres, Gertz et al. 2003).
Peripheral blood ﬂow increases via increased systolic blood pressure and decreased
diastolic blood pressure. A review of the literature shows that cerebral blood ﬂow
(CBF) may also increase during exercise, although to a lesser extent than peripheral
blood ﬂow (Querido and Sheel 2007). Heightened CBF may parallel increases in
arterial blood pressure and vascular resistance (Kulikov, Doronina et al. 2009).

These studies did not directly assess measurements of brain function such as
cognitive performance or brain activity, but the documented physiological effects of
exercise may be conducive to cognition. In fact, some studies investigating the
acute effects of exercise on cognition have found promising results for the acute role
of exercise in executive function (Cordova, Silva et al. 2009). However, the effects
are not certain, since not every study has found changes in cognition after exercise
(Hillman, Snook et al. 2003; Audiffren, Tomporowski et al. 2009). Furthermore,
experimental design has not been consistent between studies. Populations have
varied widely, including studies in young healthy adults (Audiffren, Tomporowski
et al. 2009; Pontifex, Hillman et al. 2009) and studies in older adults with chronic
obstructive pulmonary disease (Emery, Honn et al. 2001). Another inconsistency
between studies is the exercise bout, which varies in length, type, and intensity.
Duration of exercise varies, ranging from 20 (Cordova, Silva et al. 2009) to 60 or
more minutes (Hogervorst, Riedel et al. 1996) for the session. Acute bouts of
exercise described in the literature include cycle ergometry (Emery, Honn et al.
2001; Cordova, Silva et al. 2009), maximal resistance exercise, and treadmill

exercise (Pontifex, Hillman et al. 2009). Effort ranges from aerobic to maximal

23

effort (Hogervorst, Riedel et al. 1996; Emery, Honn et al. 2001). Taken together,
these variations in study design and results have prevented the ability to draw

conclusions about the effects of exercise.

Methods of measuring brain function

Numerous tests have been developed to evaluate cognitive function for a
variety of purposes such as determining cognitive impairment or measuring
cognitive performance. Assessing cognitive status is critical in studies related to
brain function to ensure that study participants are at the appropriate cognitive
condition for the requirements of the study design.

A common test used to indicate cognitive impairment, such as that
associated with MCI or AD, is the mini-mental state examination (MMSE). This
diagnostic test was developed as a clinical tool for physicians to evaluate cognitive
status of patients (Folstein, Folstein et al. 1975), and is an excellent tool for
identifying mild cognitive impairment and dementia in community and primary care
settings (Mitchell 2009). Since its inception, the MMSE has also been used for
research purposes. For example, a study among healthy participants might use the
MMSE as a screening tool to ensure that participants are not cognitively impaired
(de J ager, Schrijnemaekers et al. 2009). Another example of the utility of the
MMSE is to monitor changes in cognition over time (Deschaintre, Richard et al.
2009). The MMSE has excellent sensitivity for detecting cognitive impairment,
with one study showing 100% sensitivity in determining cognitive impairment in
determining cognitive impairment compared to DSM-III guidelines among nearly

three thousand participants aged over 65 years (Gagnon, Letenneur et al. 1990).

24

Furthermore, the MMSE has a low rate of false negatives (Anthony, LeResche et al.
1982).

The MMSE targets various cognitive domains relating to executive function,
and these include mental tracking, expressive language, visual constructive,
immediate free verbal recall, and delayed verbal free recall (Cullen, O'Neill et al.
2007). The test is administered in person by an interviewer. Questions are asked
regarding orientation (time and date), registration (such as repeating objects in a
list), attention and calculation (e. g., spelling “world” backward), recall (recalling
objects in a list), and language (including following directions and writing a
sentence). To see a sample MMSE test, please see Appendix 1. Each correct
answer is worth one point, and the maximum possible score is 30 points. A score
over 26 is usually considered normal among individuals with a high school
education or more, and a score over 23 is considered normal among individuals who
did not complete high school. Scores from 20 to 26 indicate mild cognitive
impairment (MCI), 10 to 19 show moderate to severe cognitive impairment, and
below ten indicates severe cognitive impairment (Folstein and Whitehouse 1983).

The St. Louis University Mental Status (SLUMS) examination is another
method of assessing cognitive function. This assessment is a more recent
development than the MMSE, and was designed as an alternative to the MMSE in
evaluating mild neurocognitive impairment (Tariq, Tumosa et al. 2006). Like the
MMSE, the SLUMS examination is administered in person by an interviewer, and it
includes written and oral elements. Questions also include orientation (date and

location), memory (recall of objects in a list), and attention (answering questions

25

about an audio story). A perfect score is 30 points. For a high school graduate, a
score of 27-30 is considered normal, a score of 21-26 indicates mild neurocognitive
impairment, and 1—20 is considered dementia. For a sample test, please see
Appendix 2.

Beyond initial screening for dementia, more specialized tests can be used as
study outcome measures in order to evaluate the effects of different treatments. The
CogState® test (CogState® Limited, Melbourne, Australia) is an example of a novel
method for measuring cognitive function. It is a recently developed battery of
computerized tasks, and can be used for screening for dementia (Darby, Maruff et
al. 2002) or for measuring cognitive performance in various domains (de J ager,
Schrijnemaekers et al. 2009). Individual researchers can customize the battery to
their speciﬁc studies by choosing which tasks to present to participants. Each task
targets speciﬁc cognitive functions or domains, such as executive function,
attention, spatial memory and delayed recall, learning, and memory (Pietrzak, Olver
et al. 2009). The CogState® as chosen for this study takes about 20 minutes to
complete. It is a repeatable test that has been shown not to have practice effects
(Collie, Maruff et al. 2003; Falleti, Maruff et al. 2006). As a relatively new
development, CogState® shows promise for evaluating cognitive performance in
middle aged and older populations (Wild, Howieson et al. 2008; Padmanabhan,
Leslie et al. 2009). One study found that compared to the well-established Hopkins
Verbal Learning Test, the CogState® may have enhanced ability to detect cognitive
deﬁcits aside from memory (de J ager, Schrijnemaekers et al. 2009). Other studies

have also used this battery in older populations to test cognitive function (Cargin,

26

   

Maruff et al. 2007; Paile-Hyvarinen, Raikkonen et al. 2009). The tests are scored
for accuracy and for speed.

Another useful function of cognitive tasks in research studies is to activate
speciﬁc areas of the brain so that brain imaging techniques can determine which
pathways are being used for processing. The ﬂanker interference task is an example
of a task used to measure conﬂict monitoring skills (Eriksen and Schultz 1979).
Conﬂict monitoring enables goal-oriented behavior, which contributes to decision
making used in executive function tasks (Botvinick, Braver et al. 2001). There are
multiple variations of the ﬂanker inhibition task that can be used. In the ﬂanker
task, reaction time and accuracy of responses are measured. The ﬂanker task can be
composed of letters or arrowheads. For the arrowhead version of the ﬂanker,
participants must indicate whether the center arrow is pointing to the left or the
right. Each prompt to the participant is either for a congruent or incongruent
condition. In the congruent condition, all of the arrows are pointing in the same
direction; in the incongruent condition, the center arrow is pointing toward the
opposite direction of the other arrows. The congruent and incongruent conditions of

the ﬂanker task use different pathways in the brain (Ochsner, Hughes et al. 2009)

Methods of measuring brain activation

Measuring brain activation can yield valuable information about processes
that are occurring in the brain during cognitive processes. Measuring brain
activation while subjects are performing a particular task can reveal such
information as processing speed, which brain regions are used for speciﬁc purposes,

and neurocognitive pathways that connect different brain regions. Brain activation

27

can be studied noninvasively using a variety of methods that are based on
neurovascular coupling, which is the principle that neuronal activity and cerebral
blood ﬂow (CBF) are related. Event-related potentials are measured using
magnetoencephalography (MEG) and electroencephalography (EEG). Other
techniques include positron emission tomography (PET), and functional magnetic
resonance imaging (fMRI). These methods are discussed in greater detail in review
papers (W intermark, Sesay et al. 2005; Shibasaki 2008), and have been used to
demonstrate age-related changes in brain function (Prichep 2007; Small,
Bookheimer et al. 2008).

Both MEG and EEG are based on electrophysiology in the brain. MEG
measures the magnetic ﬁelds produced from brain activity, and EEG measures their
associated electric potentials. These methods have excellent temporal resolution, on
the order of milliseconds. However, spatial resolution is as large as a centimeter,
which is a limitation for using MEG and EEG.

Because of their superior spatial resolution within 4-6 mm, both PET and
fMRI have been used to measure regional brain activation. Positron emission
tomography is used to measure regional cerebral blood ﬂow (rCBF), which
increases with neuronal ﬁring, and regional cerebral blood volume, which decreases
with neuronal ﬁring. A radioactive isotope with a short half-life is injected into the
bloodstream. The isotope is then integrated into metabolic pathways so that
metabolism can be measured. One commonly used isotope is 18ﬂuorodeoxyglucose
(FDG). As FDG gets metabolized, the gamma radiation measured can be used to

estimate accumulation of glucose consumption in speciﬁc regions. One example of

28

the use of PET is in the characterization of the brains of senescence-accelerated
mice (SAM), which age rapidly. One study found that compared to normal mice,
SAM exhibited increased oxidative stress and lower performance on behavioral
tests. Using PET allowed the association of these characteristics with decreased
brain glucose consumption and metabolism (Borras, Stvolinsky et al. 2009).

Functional magnetic resonance imaging is another technique used to
measure brain activation in response to stimuli. Magnetic resonance imaging is
based on differences in magnetic properties. One brain MRI technique is the brain
blood oxygenation level—dependent (BOLD) response (Buxton, Uludag et al. 2004).
The BOLD response depends on changes in blood deoxyhemoglobin, which is
paramagnetic. In response to neuronal ﬁring, local blood ﬂow increases to restore
oxygen to active neurons. This increase in local blood ﬂow is greater than local
oxygen uptake by ﬁring neurons, leading to a net decrease in deoxyhemoglobin
concentration. Diamagnetic properties increase, leading to increased signal
intensity. Advantages of fMRI include a shorter scanning time (since it is not
necessary to wait for the uptake of radioactive isotopes), no need for exogenous
contrast agents, and the ability to map the functional images to high-quality
anatomical images gathered by the same scanner.

Functional MRI has been used to map regional changes in brain activation in
response to simple tasks such as tapping a ﬁnger, solving arithmetic problems, or
watching visual stimuli. This method has been used to determine which areas of the
brain are active during various tasks. For example, fMRI experiments have

conﬁrmed that the anterior cingulate cortex (ACC) is active during conﬂict

29

monitoring and resolution, such as tasks requiring inhibition (Pochon, Riis et al.
2008). Furthermore, MRI has shown a link between activity in the ACC and
prefrontal cortex (Kems, Cohen et al. 2004), both of which demonstrate the brain
BOLD response during performance of Eriksen ﬂanker tasks (MacDonald, Cohen et
al. 2000; Botvinick, Braver et al. 2001). In the current study, regions of particular
interest are based on a previous fMRI study measuring brain activation in older
adults during a ﬂanker arrow task. Right inferior frontal gyrus (IFG) / middle
frontal gyrus (MFG) / precentral gyrus (PCG), as well as the right MFG / Brodmann
Area 6 (BA6), showed activation during a ﬂanker arrow task (Zhu 2008).

Various studies investigating the effects of diet and exercise on the brain
have used ﬂVlRI to measure brain activation. A previously mentioned study
assessed the effects of aerobic training, consisting of 30 minutes of walking three
times weekly for six weeks, on cognition and brain activation in older adults
(average age 66 years old). Compared to the control group, which met three times
weekly for stretching and toning, the aerobically trained group showed increased
activation in the prefrontal and parietal cortices and the ACC during a ﬂanker arrow
task (Colcombe, Kramer et al. 2004). There is also evidence that dietary factors can
also affect the brain BOLD response. For example, a pilot study found that single
dose of ﬂavanol-rich cocoa led to increased cerebral blood ﬂow. A consequent
study found that ﬁve days of cocoa ingestion over ﬁve days led to an increase in the
brain BOLD response during a task-switching paradigm (Francis, Head et al. 2006).
A study investigating the acute effects of diet on the brain BOLD response

compared the effects of a effects of a single control (350 kcal, 57% carbohydrates,

30

28% fat) or high-fat (765 kcal, 26% carbohydrates, 67% fat) meal on brain
activation during a motor task. Reported results included a decrease in the brain
BOLD response after ingestion of the high fat meal (Noseworthy, Alfonsi et al.
2003). However, a similar study looked at the acute effects of the same meals on
the brain BOLD response during similar motor tasks, plus visual and
integrative/cognitive tasks in healthy young adults. This study did not ﬁnd a direct
effect of postprandial lipemia on the brain BOLD response (Slade, Carlson et al.
2009). These studies still leave many gaps in the area of the brain BOLD response

to dietary and exercise factors.

Study rationale

Gaps in literature

Despite the signiﬁcance of the topic and the amount of interest, many
aspects of the link between lifestyle and cognitive function during aging remain
unknown (Van Dyk and Sano 2007). Many studies have looked at the relationship
between long-term dietary intake of nutrients or foods and cognitive decline in
aging humans. Epidemiological studies have suggested associations between
dietary intake and brain health during aging, but evidence is inconclusive. Selected
dietary factors appear to be associated with enhanced cognition (dietary fat, apple
juice, vegetables, and B vitamins, for example) (Morris, Evans et al. 2004; Chan,
Graves et al. 2006; Polidori, Pratico et al. 2009), some appear to be associated with
impaired cognitive performance or accelerated cognitive decline (saturated fat, for
example) (Kaplan and Greenwood 1998; Morris, Evans et al. 2004), and some have

still undetermined effects on brain health (for example, total fat intake) (Kaplan and

31

Greenwood 1998; Ariogul, Cankurtaran et al. 2005). Similar to dietary factors,
exercise is likely to impact cognitive function during aging, but here, too, the link is
unclear. Over time, habitual exercise habits or physical ﬁtness level may be
correlated with improved Cognitive function or slower cognitive decline (Cotman
and Berchtold 2002; Colcombe, Erickson et al. 2006), but the possible effects of
exercise on cognition are still unclear (Colcombe and Kramer 2003).

The possible acute effects of diet and PA on cognitive function and brain
activation are also unclear, and less research has been conducted on acute effects
than on long-term epidemiological studies to determine possible associations or
correlations with habitual lifestyle factors. In an aging population especially,
research on the acute effects on cognition is sparse, and the potential acute effects of
various lifestyle factors is largely undetermined. To our knowledge, no studies have
combined a single mixed meal plus a bout of exercise within the recommendations
(non vigorous) in this population of healthy older adults. One study has investigated
the effects of a 14 day lifestyle longevity program of diet, exercise, relaxation
exercises, mental exercises, on cognitive function and brain activation (Small,
Silverman et al. 2006). The intervention group showed improvements in word
ﬂuency compared to the control group. Findings from this study are promising,
since the two-week intervention period is far less than the time periods measured in
prospective epidemiological studies, and suggest the need to determine whether a

single meal or exercise bout can show effects on the brain.

32

Justiﬁcation

The acute effect of diet and exercise on cognition in an aging population is
an exciting and important area of study. The American population includes an
increasing number of older adults. Aging is associated with cognitive impairment
or decline, but it may be possible to slow or decrease these negative effects through
the modiﬁable lifestyle factors of diet and exercise. It is currently unknown whether
a single meal and bout of moderate intensity exercise can acutely affect executive
function and brain activation in healthy older adults. Results of this study are an
important addition to our knowledge of cognition in the aging brain. Determining
whether either a single mixed meal or a bout of exercise is effective in altering
cognitive performance or brain activation will increase our understanding of the
potential ways in which lifestyle impacts cognitive health during aging.

Findings from this study will help elucidate the role of lifestyle factors on
cognition. Any changes or lack of change in cognitive performance or brain
activation between treatments (SB, NB, or NB + AE) will help uncover the potential
connections between these lifestyle factors and cognition. In addition, changes that
are found would imply certain modes of action for how diet or physical activity
would inﬂuence cognition. Eventually, knowing this could help to guide
development of recommendations for the most effective diet and exercise regimens
for cognition in an aging population so that individuals can have greater control
over their brain health.

This study is truly a novel experiment in many regards. The study design is

unique for its combination of population and treatments. Participants in this study,

33

unlike in other acute cognitive studies, are representative of a healthy aging
population (Pfeiffer, Ludwig et al. 2005; Ferris, Williams et al. 2007; Chui and
Greenwood 2008). Furthermore, compared to many published acute studies on
lifestyle factors and brain activation, the treatments in this study could conceivably
be more representative of what an individual might choose in real life regarding diet
and exercise. The SB meal is representative of American dietary patterns in its
macronutrient composition, and it is also composed of palatable, standard foods in
the American diet. While components of the NB diet are by design different from
the SB diet, it is still conceivable that individuals would be willing to choose a
breakfast like this one. This is in contrast to the majority of previous acute
experimental studies whose treatments have instead been a concentrated amount of
a speciﬁc nutrient or food, or combination of nutrients or food that are given in
doses unlikely to be consumed in a typical meal. Similarly, the bout of exercise in
this study may be a more accurate representation of reality for most individuals.
The majority of published studies have looked at the acute effects on cognition
following a bout of more intense exercise (Hogervorst, Riedel et al. 1996; Kashihara
and Nakahara 2005; Cordova, Silva et al. 2009). In contrast, the exercise intensity
in this study is only moderate, and may be a more likely portrayal of what
individuals might be willing and able to do in a free-living setting.

Aside from its novelty regarding the speciﬁc diet and exercise treatments,
this study is innovative because of its outcome measures, which include assessment
both of cognitive performance and of brain activation. While multiple diet or

exercise studies have focused on cognitive performance (Netz, Tomer et al. 2007;

34

Chui and Greenwood 2008; Cordova, Silva et al. 2009; Kashihara, Maruyama et al.
2009; Nilsson, Radeborg et al. 2009) or brain activation using fMRI (Francis, Head
et al. 2006) or other techniques (Kamijo, Hayashi et al. 2009) to measure brain
activity, this study investigates both of these. Executive function will be assessed
using the CogState battery of tests, and changes in brain activation in the middle
frontal gyrus and superior frontal gyrus after treatment will be assessed using MRI
to measure the brain oxygen level-dependent (BOLD) response (Buxton, Uludag et
al. 2004) during a Flanker arrow paradigm. I

The purpose of this study was to assess acute effects of a breakfast and bout
of aerobic exercise on the cognitive function and the brain BOLD response in a
healthy aging population, using two different diets and a bout of moderate aerobic
exercise. We compared the acute effects of a single meal: a standard breakfast (SB),
modeled after a realistic American breakfast, and a nutrient dense breakfast (NB).
The SB diet included components thought to have negative effects on the brain,
including high amounts of saturated fat and sugar. The NB included dietary
components associated with improved cognition or endothelial function, including
antioxidants, fruit and vegetables, and omega—3 fatty acids. To check whether
exercise might have additional effects on cognition, the third branch of the study
(NB + AE) included the NB breakfast plus a bout of moderate exercise within the
American College of Sports Medicine (ACSM) and American Heart Association
(AHA) joint recommendations for older adults (Nelson, Rejeski et al. 2007). We

hypothesized that compared to SB, NB would improve executive function and brain

35

activation in the inferior frontal gyrus and middle frontal gyrus (regions associated

with inhibition), and that AE would further enhance these effects.

36

Chapter II. Acute effects of diet and exercise on cognitive
function and brain activation in an aging population
Abstract
Aging and lifestyle factors have been associated with cognitive decline.

Certain diet components or patterns, or exercise regimens may help increase
cognition or slow age-related cognitive decline. Epidemiological aging studies
suggest a role of diet and exercise in cognition but less is known about acute effects.
This study’s objective was to compare the effects of a high saturated fat, low
nutrient dense standard breakfast (SB), nutrient dense breakfast (NB), and NB
combined with aerobic exercise (30 minutes at 50-65% estimated heart rate reserve,
AB), on cognitive function and brain activation in aging adults. This was a three
arm, randomized, cross-over design among healthy adults (n=l9, 69.7 i 4.9 y).
Participants ate a SB or NB (NB, NB + AE treatments) breakfast and walked (NB +
AE condition) for 30 minutes at 50—65% estimated heart rate reserve. Cognitive
function was assessed with CogState® battery of tasks. Brain activation was
evaluated through the brain blood oxygen level-dependent response during
functional MRI with a ﬂanker arrow task requiring inhibition control. Compared to
SB, NB with or without AE did not improve cognition in healthy older adults. There
was a signiﬁcant improvement in accuracy on the prediction task (executive
function) in the CogState® battery between in the NB compared to the NB + AE

condition (p<0.05). In addition, there was a signiﬁcant increase in response time in

3.7

NB compared to SB (p<0.05) in the monitoring task (testing attention). No
signiﬁcant treatment effects were seen in the ﬂanker arrow task. Brain activation
(percent signal change) was not signiﬁcantly different between treatments in the
regions of interest in the right inferior frontal gyrus, middle frontal gyrus, precentral
gyrus (centroid at R39, A3, 832), and in the right middle frontal gyrus, Brodmann
Area 6 (centroid at R29, P1, SS4). This study was designed to test the effects of
palatable meals and a bout of moderate exercise on cognition in healthy older adults.
Although longer interventions of diet and exercise may affect cognitive function
and/or brain activation in older adults, our results suggest these lifestyle factors do

not have acute effects.

Introduction

Nutrient intake and physical activity patterns throughout the lifespan can
inﬂuence cognition during older adulthood, a time when cognitive function tends to
decline and is of widespread concern (Connell, Scott Roberts et al. 2007; Gillette
Guyonnet, Abellan Van Kan et al. 2007; Hillman, Erickson et al. 2008; Wilcox,
Sharkey et al. 2009). In particular, executive function may be impaired, often
decreasing performance on tasks involving memory or interference (Jones, Nyberg
et al. 2006). Dietary patterns associated with a Mediterranean diet may be
protective against development of AD (Scarmeas, Luchsinger et al. 2009). Foods
such as vegetables and ﬁsh, and nutrients such as antioxidants and omega-3 fatty
acids, may ameliorate age-related decline (Denny 2008). Herbal supplements may
also be effective, as demonstrated in a trial that found improvements in cognitive

symptoms with 52 weeks of ginkgo supplementation in patients with mild AD (Le

38

Bars, Katz et al. 1997). In contrast, long term consumption of diets high in
saturated fat and reﬁned sugar are associated with impaired learning and decreased
neuronal plasticity in rats (Molteni, Barnard et al. 2002). In humans as well,
selected studies have shown associations between consumption of saturated fat,
trans fat, and sugars, and development of impaired cognitive function and
accelerated cognitive decline (Morris, Evans et al. 2004).

Studies also suggest the beneﬁcial effects of exercise on cognition. Hillman,
Motl et al. found that among individuals aged 15-71 years, higher physical activity
levels were associated with faster reaction time in a ﬂanker task, which tests
interference control. In addition, increased physical activity was positively
associated with accuracy in the older adults (40-71 years) (Hillman, Motl et al.
2006). Other studies also correlate increased ﬁtness or physical activity levels with
less cognitive decline over time (Berkman, Seeman et al. 1993; Kramer, Colcombe
et al. 2003; Sturrnan, Morris et al. 2005) or with improved cognitive function
(Colcombe, Kramer et al. 2004; Lindwall, Rennemark et al. 2008). These studies
investigate associations rather than causal relationships, and do not combine the
effects of both diet and exercise. One experimental study looked at the effects of
diet and exercise over two weeks. Small et al. (2006) reported improved executive
function (word ﬂuency) following a lifestyle longevity regime including mental and
physical exercise, a “longevity diet” (high in fruit and vegetables and low in
saturated fat), and relaxation, in a small sample of 35-69 year old adults.
Furthermore, the intervention group displayed a decrease in resting activity in the

left dorsolateral prefrontal cortex. This suggests that lifestyle factors may cause

39

changes in cognition and brain function within weeks (Small, Silverman et al.
2006).

In addition to inﬂuences of chronic intake of certain foods and physical
exercise on cognition in humans, dietary components and exercise may also have
acute effects on cognitive function. For example, a single meal may impair
cognitive among individuals with impaired glycemic control, and these effects are
attenuated with simultaneous ingestion of antioxidant vitamins (Chui and
Greenwood 2008). Phytochemicals or supplements may be effective as well
(Youdim and Joseph 2001). For example, ginseng improved serial sevens
subtraction performance and decreased subjective fatigue (VAS) one hour after dose
(Reay, Kennedy et al. 2005). However, research in this area is limited. The effects
of a single bout of exercise have been studied somewhat more extensively than
effects of a single meal, and exercise may improve cognitive function acutely
(Kashihara and Nakahara 2005; Audiffren, Tomporowski et al. 2009; Cordova,
Silva et al. 2009).

Although diet and exercise are evidently important in cognitive function, no
studies to our knowledge have tested the acute effects of a single meal and bout of
exercise on executive function and brain activation in older adults. We tested the
acute effects of these lifestyle factors on executive performance using a battery of
computerized tests (CogState®). In addition, our study measured the brain blood
oxygen level-dependent (BOLD) response (Buxton, Uludag et al. 2004) during
functional magnetic resonance imaging (fMRI) during a ﬂanker task to determine

activation in interference regions of the brain. Our objective was to compare the

40

acute effects of diet and exercise on cognitive performance and brain activation. To
test the effects of nutrition on cognitive function and brain activation, we compared
two different meals. The standard breakfast (SB) included factors, such as high
levels of sugar and saturated fat and low nutrient density, that may impair cognition
(Molteni, Barnard et al. 2002; Wu, Molteni et al. 2003), while the nutrient dense
breakfast (NB) incorporated numerous factors, such as antioxidants, n-3 fatty acids,
whole grains, fruits, and vegetables, that may enhance cognition (Morris, Evans et
al. 2002; Wu, Ying et al. 2004; Chui and Greenwood 2008). In addition, because of
the evidence suggesting the acute beneﬁts of exercise, we also tested whether a bout
of moderate aerobic exercise (AE) would increase any observed effects of NB. We
hypothesized that NB would improve cognitive function and brain activation in the
inferior frontal gyrus and middle frontal gyrus compared to SB, and that AE would

further enhance these effects.

Materials and Methods

Participants: All procedures were approved by the Michigan State
University Institutional Review Board for human studies. Participants each gave
informed written consent (Appendix 3), and also signed a compliance form for the
Health Insurance Portability and Accountability Act (Appendix 4). Healthy adults
over 60 years old and with no unstable disease state were recruited from the
Lansing, Michigan, area.

Inclusion criteria were right handedness (assessed by the Edinburgh
handedness inventory (Oldﬁeld 1971), age greater than 60 years old, and no

dementia, as assessed by initial self-report and by Mini-Mental State Examination

4]

(MMSE) score (score < 27 among individuals with a high school education, or a
score < 24 among individuals without a high school diploma). Participants had to
be willing to consume each of the meals that were served. They also had to be able
to complete the exercise bout of 30 minutes of treadmill walking, and were required
to obtain written permission from their personal physicians (Appendix 5) to
participate in the AE. Participants had to be able to undergo MRI as determined by
a magnetic materials safety form (Appendix 6).

Exclusion criteria included a self-report of habitually exceeding
recommendations for AE from the American College of Sports Medicine and the
American Heart Association (Nelson, Rejeski et al. 2007). Based on these
recommendations, potential participants were excluded if they reported exercising
for 5 or more days a week for at least 30 minutes at a moderate intensity, or for 3 or
more days a week for at least 30 minutes at a vigorous intensity. Potential
participants were also excluded if they did not consume meat, could not walk for 30
consecutive minutes on a treadmill or could not undergo MRI due to claustrophobia,
body weight > 114 kg or BMI > 35, or metallic implants that were not MRI
compatible. Other exclusion criteria were the use of beta blockers, dementia or anti-
psychotic medications, or insulin, history of stroke, presence of recent
cardiovascular events, severe hypertension (systolic blood pressure > 200 mm Hg),
diabetes, moderate to severe depression, ﬁbromyalgia, chronic fatigue syndrome,
peripheral vascular or arterial disease, or a chronic infectious disease.

Out of the 26 screened individuals who met the inclusion criteria, 19

ﬁnished all three treatment arms of the study. One individual had hyperglycemia,

42

with a fasting glucose level of 13.3 mmol* L"). One subject did not complete the
AE session because of recent hospitalization for atrial ﬁbrillation. Another subject
was unable to complete the AE session because of an abnormal EKG as read by the
physician. Of the 19 participants who completed the three treatments, 11 also
completed the fMRI. The smaller subset was due to technical issues (n=3), enlarged
ventricles (n=2), and excessive motion (n=3) that could not be corrected.
Participants were compensated $25 for each laboratory visit, and participants
received results of their participation in the study (see Appendix 12).

Study overview: We used a three-arm unblinded randomized crossover
design. Subjects made four visits to the laboratory. Visit one was an introductory
visit. Visits two through four were treatments administered in random order,
separated by at least three days.

Previsit instructions for treatment visits included maintaining their habitual
dietary pattern for the duration of the study, and drinking plenty of ﬂuids and
refraining from exercise the day before the visit. Participants arrived at the clinic
after a ten hour fast, with no caffeine consumption or aspirin use on the day of the
visit.

See Figure 1 for the protocol for visits 24. Participants arrived at 9:00 am.
Blood pressure was taken manually with a cuff sphygmanomometer and blood was
drawn. Breakfast was served at 9:20, and participants had 20 minutes to consume
the meal. Next, subjects completed one of the remaining questionnaires (SLUMS or
geriatric depression scale (GDS)), or reviewed their food records and PA logs for

clarity. Subjects then had 30-60 minutes to complete perform a sedentary activity

43

such as reading, and they repeated this activity each visit. At 11:00, they began to

watch a DVD or exercise (AE). At 11:50, subjects took the CogState® exam. The

postprandial blood draw was at 12:30, immediately followed by the MRI exam,

which lasted about 45 minutes.

Figure 1: Schematic of protocol.

 

Arrive at clinic.
Continuation of
subject pre-visit

 

compliance, baseline Questionnaires

 

 

 

 

 

 

 

 

 

Functional MRI

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

biooddraw,andblood andsedentary Q , (including
pressure free time CogState Flanker)
measurement battery
TIME
(minutes)
0 20 100 160 190 210
Breakfast:SBorNB(for Watch”
NBorNB+AE condition) minuteDVDlSB Postprandialblood
and NB), or draw
exercise (AE).

 

 

 

SB = standard breakfast, NB = nutrient dense breakfast, AB = aerobic exercise.

Treatments

Diets: The standard breakfast (SB) was low in nutrient density and high in saturated

fat and simple carbohydrates; it included components that plausibly could impair

cognition and/or vascular function based on previous studies (Kaplan and

44

Greenwood 1998; Molteni, Barnard et al. 2002; Bourre 2006). The nutrient dense
cognitive diet (NB) breakfast included components believed to enhance cognition
and/or vascular function (Gillette Guyonnet, Abellan Van Kan et al. 2007). The
meal provided 400, 550, or 700 calories, or approximately 25% of subjects’ daily
calorie needs based on the Harris-Benedict equation (Harris 1919) and appropriate
activity factors based on self-reported habitual physical activity levels. Breakfast
portions were measured using an Ohaus Scout® Pro Balance electronic scale
(Ohaus Corporation, Pine Brook, New Jersey). Table 1 and Table 2 show the
speciﬁc breakfast foods and nutrients proﬁle, respectively. The macro-composition
as a percent of total kilocalories was 55% fat, 27% carbohydrate, 16% protein, and
25% saturated fat, and it contained < 1 g dietary ﬁber. The macro-composition as a
percent of total kcal was 30% fat, 54% carbohydrate, 7% protein, and less than 1%
saturated fat, and it included 26 g ﬁber. The NB breakfast was also high in
antioxidants with the 700 kcal breakfast containing an oxidative radical antioxidant
capacity value of approximately 21217 based on calculations from the USDA
database (USDA 2007). None of the components in the SB breakfast were included
in the USDA database, implying an oxidative radical antioxidant capacity value of

zero. Subjects consumed each meal within 20 minutes.

45

Table 1: Nutrients in the 700 kilocalorie breakfasts

 

Nutrient dense

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Nutrient Standard breakfast breakfast
% kilocalories from fat 55 30
% kilocalories from saturated fat 25 <1
n-3 faﬂacids, g 0.3 2.2
% kilocalories from protein 16 17
% kilocalories from carbohydrate 27 54
% kilocalories from sugars 25 13
dietary ﬁber, g 1 26
servings of fruit and vegetables 0 3
vitamin C, mg 1.3 49
vitamin E, mg 0.9 3.8
tassium, mg 402 1097
folate, dietary folate equivalents, u g 77 199
vitamin B12, pg 0.41 4.9
kilocalories 700 700

 

 

 

46

 

Table 2: Composition of the 700 kilocalorie breakfasts

 

 

 

 

 

 

 

 

 

 

 

Standard breakfast

Food Amount
white bread 25 g
butter 5 g
'elly (regular sugar) 10 g
fruit drink 177 ml
whole milk 267 ml
2 scrambled eggs 2 large
sausage, breakfastiork 56 g
decaffeinated tea/coffee 177 ml

 

 

 

47

Table 2 (continued)

 

Nutrient dense breakfast

 

 

 

 

 

 

 

 

 

 

 

 

 

Food Amount
cereal, Kashi go lean crunch 53 g
soy milk, vanilla 177 ml
blueberries, unsweetened, frozen 155 g
vegetarian sausage, breakfast patties 76 g
bread, whole wheat, 100% 28 g
almond butter 10 g
fruit smead, blueberry, 100% fruit 15 g
juice, vegetable, canned 177 ml
nuts, walnuts, English, dried 14 g
tea, green, authentic, brewed 240 ml

 

Exercise: The AE bout was supervised by an American Cardiac Life Support
(ACLS)-certiﬁed physician. The exercise session included a 5 min warm-up at a
self-selected walking pace, 30 min of walking at goal intensity, and 5 min of a slow
cool-down. The goal intensity was 50-65% of estimated heart rate reserve,
calculated as RHR+0.5(MHR-RHR), where RHR is resting heart rate and MHR is
estimated maximal heart rate based on 220-age (Fox, Naughton et al. 1971).
Participants maintained the treadmill speed, and the percent incline on the treadmill

was adjusted to achieve the target heart rate. Data were recorded on an exercise

48

 

documentation form (Appendix 8). Electrocardiograms were monitored throughout
the warm—up, exercise, cool-down, and short recovery period, and recorded using
the Nasiff Interpretive PC ECG/PC EKG (Cardiocard) program (Nassiff Associates,
Brewerton, New York). Every 5 minutes, blood pressure was taken manually
according to AHA scientiﬁc guidelines (Pickering, Hall et al. 2005) to ensure an
appropriate response to exercise. In addition, participants were asked to use the
Borg Scale for ratings of perceived exertion (RPE) (Borg, Hassmen et al. 1987).

For the SB and NB visits, subjects viewed a neutral 50 min documentary video prior
to the CogState® and fMRI. Participants’ personal physicians received a letter
summarizing the exercise session (Appendix 13) and the EKG.

Measurements: Clinic visit 1 farniliarized participants with parts of the study
including walking on the treadmill and using the Borg Scale for rating of perceived
exertion, practicing the cognitive battery exam twice, and having a mock MRI
exam. Height and weight, ankle brachial index, and resting heart rate were
measured during this visit, and the Mini-Mental State Examination (Folstein and
Whitehouse 1983) was administered.

Dietary assessment: Participants were asked to complete a three day food
record for assessment of usual intake. The three days consisted of two days during
the week (non-holiday Monday through Friday) and a weekend day (Saturday or
Sunday), and participants were asked to record days that were representative of their
usual intake. Food records were reviewed with participants to clarify items and
portion sizes. Food record data were analyzed using Food Processor (ESHA

Research, Salem, Oregon). Average intake of select nutrients is shown in Table 5.

49

Participant dietary intake was compared both to national recommendations and to
national average intakes.

Physical activity assessment: Habitual physical activity was estimated using
GTlM Activity Monitors (Actigraph, Pensacola, Florida). Participants were asked
to wear an accelerometer for 7 consecutive days during which they kept activity
levels at their normal levels. During those 7 days, participants recorded their sleep,
long distance travel, and activities in a physical activity log, to help interpret the
accelerometer data, which was used to estimate usual physical activity levels of
participants. Participants were asked to place the accelerometers on either hip using
3 provided belt hook, and to remove the accelerometer for water activities and
sleeping. Data were downloaded to a computer. Output was expressed in counts
per l-minute epoch. For each individual, total daily counts were converted to daily
kilocalories of expenditure. Activity was classiﬁed by counts per l-minute epoch
using the cut points 5 1951 (light), 1952-5724 (moderate), 5725-9498 (hard), or
9499 (very hard) counts per minute (Freedson, Melanson et al. 1998). Complete
accelerometer data were obtained for 16 participants. The accelerometers of two
study participants did not download properly, and the ﬁnal participant did not wear
an accelerometer.

Other cognitive assessment: During subsequent visits, participants ﬁlled out
the short form-36, cognitive failures questionnaire (Broadbent, Cooper et al. 1982)
(Appendix 9), and the Saint Louis University Mental Status Examination (Tariq,
Tumosa et al. 2006) (Appendix 2). At the end of the visit, participants were given

instructions for preparation for future visits (Appendix 10).

50

Blood analyses: Blood samples were obtained through routine venipuncture
of the median cubital vein by a trained phlebotomist. For each of the three
treatment visits, blood samples were taken both fasting and postprandial (180
minutes). Samples clotted for 30 minutes, and were centrifuged. Serum was frozen
at -20°C for later analysis by Sparrow Hospital laboratories. Samples were
analyzed for glucose and insulin to assess glycemic control and tolerance, which can
inﬂuence post-prandial cognitive function (Nilsson, Radeborg et al. 2009). Timing
of cognitive testing and MRI were based on the postprandial rise in triglycerides,
which have been shown to peak within 2-3 hours postprandial (Cortes, Nunez et al.
2006). Total cholesterol, HDL cholesterol and LDL cholesterol were also
measured. For each serum measure, a repeated measures analysis of variance was
used to compare fasting concentrations between the three visits. The gains scores
between fasting and postprandial draws were compared using repeated measures
analysis of variance to test the effects of treatment (SB, NB, and NB + AB) on
changes in concentrations. Analysis was conducted using SPSS software 17.0
(SPSS, Chicago, IL), and signiﬁcance was set at p < 0.05.

Cognitive assessment:

The computerized CogState® battery (CogState® Limited, Melbourne,
Australia) included nine tasks, each targeting a certain domain of cognition. For
each of the 9 different tests, Table 3 shows the name of the test, the speciﬁc

cognition, and the question to which participants were responding.

51

Table 3: CogState® battery of tests

 

Cognitive Domain

 

 

 

 

 

 

 

 

 

 

Indrvrdual Test Abbrevratron Tested Test Question
Continuous Paired Memory and Spatial In what locations do
. . CPAL these prctures

Assocrate Learning Memory

belong?

Have you seen this
One Word Learn OWL Verbal Memory word repeatedly

before?
Identiﬁcation H)N Visual Attention Is the card red?
One Back ONB Attentron/Workmg Is the prevrous card

Memory the same?
. . Have you seen this
One Card Learn OCL V‘sual Leammg and card before in this
Memory

task?
Monitoring MON Attention Has a card touched a

white line?
Prediction PRD Executive Function Is the next card red?
Continuous Paired Memory and Spatial In what locations did
Associate Leaming- CPAR Memory-Delayed these pictures
Delayed Recall Recall belong?

Have you seen this
One Word Learn
Delayed Recall OWLR Long Term Memory word repeatedly

 

 

 

before?

 

Each participant practiced the entire battery twice during their initial

introductory visit, and these practice tests were not scored. Each testing session

included task instructions and a non-scored short pre-test for each task to remind

participants of how to complete that particular task. During the testing, a researcher

was in the room to give any necessary assistance with instructions. Each task was

scored for accuracy and for reaction time. To help avoid skewness, accuracy scores

were computed as the inverse sine of the fraction correct, while reaction times were

computed as the logarithm of the response time in milliseconds.

52

 

The study protocol required each participant to take the CogState battery a
total of ﬁve times: the ﬁrst two times, on the introductory visit, were unscored,
while the three times during the treatment visits were scored and used as outcome
measures. A repeated measures analysis of variance was used to evaluate possible
effects of which breakfast (SB or NB) was consumed ﬁrst (n=19). To test effects of
condition (SB, NB, or NB + AE) and time (visit one, two or three) on accuracy and
response speed, a repeated measures general linear model analysis of variance with
Bonferroni—adjusted pairwise comparison was performed using SPSS software 17.0
(SPSS, Chicago, IL). Signiﬁcance was set at p < 0.05.

Flanker paradigm: An Erikson Flanker arrow task was used during the
functional scans of the MRI. There were three functional runs of 7 minutes each,
and it was a rapid event-related paradigm. Stimuli were rows of 7 arrows. The
center arrow pointed either in the same (congruent (C)) (“<<<<<<<” or
“>>>>>>>”) or the opposite (incongruent (IC)) (“<<< > <<<” or “<<< > <<<”)
direction as the ﬂanking arrows. Arrows pointed either to the right or left. Text was
white, presented on a black background.

Each of the stimuli conditions (C or IC) was presented for 2500 ms 48 times
every run, with each condition presented an equal number of times in each direction.
Stimuli were randomized using an “RSF gen” program from AFN I software (Cox
1996). Also, baseline data were gathered by randomly presenting a white ﬁxation
cross at the center of the black viewing screen for 2500ms 72 times within the run,
so that the ratio of stimuli presentation to baseline for each run was 96:72. During

the Flanker stimuli, participants were to indicate as quickly as possible whether the

53

center arrow was pointing to the right or to the left by pressing a button. During the
ﬁxation crosses, participants were instructed to continue viewing the screen but not
to press a response button. A repeated measures general linear model analysis of
variance was used to test effects of condition on accuracy (percent correct) and
response speed (milliseconds). Analysis was performed using SPSS software 17.0

(SPSS, Chicago, IL). Signiﬁcance was set at p < 0.05.

fMRI:

Set-up: The instructions and stimuli were back-projected on a 32 inch LCD
screen behind the magnet bore. Participants viewed the stimuli through a mirror
attached to the top of the head coil. The LCD screen subtended 10° x 13° of visual
angle. Responses of whether the center arrow was pointing left or right and
response times were recorded using a pair of 5-button keypads. The functional
paradigm was controlled by IFIS-SA (Invivo, Gainesville, Florida), and subjects
responded to cues using their right or left index ﬁngers.

Acquisition: Images were collected using a GE 3T Signa® HDx MR scanner
(GE Healthcare, Waukesha, WI) with an 8-channel head coil. Subjects were supine,
and motion was limited by foam padding. During each scanning session, images
were acquired for localization, and then ﬁrst- and higher-order shimming was done
to improve magnetic ﬁeld homogeneity. Functional echo planar images (three runs)

were acquired with 34 axial slices with TE = 27 ms, TR = 2500 ms, ﬂip angle =

80°, ﬁeld of view = 22 cm, matrix size = 64 x 64. The ﬁrst four data points of each

run were discarded. Each set of slices was acquired 168 times during each

54

functional run. After the functional data acquisition, high-resolution volumetric
Tl-weighted spoiled gradient-recalled images with cerebrospinal ﬂuid suppressed were
obtained to cover the whole brain with 120 1.5 mm sagittal slices, 500 ms time of
inversion, 24 cm FOV and 8° ﬂip angle.

Data analysis:

All fMRI data pre-processing and analyses were conducted with AFN I software
(Cox 1996). For each subject, the acquisition timing difference was ﬁrst corrected. for
different slice locations. With the ﬁrst functional image as the reference, rigid-body
motion correction was done in three translational and three rotational directions. The
amount of motion in these directions was estimated and then the estimations were used
in data analysis. For each subject, spatial blurring with a full width half maximum of 4
mm was used to reduce random noise (Parrish, Gitelman et al. 2000), and also to reduce
the issue of inter-subj ect anatomical variation and Talairach transformation variation

during group analysis. For the group analysis, all images were converted to Talairach

coordinate space (Rey, Dellatolas et al. 1988) with an interpolation to 1 mm3 voxels.

Throughout the paper, the coordinates of brain activity are presented in Talairach space
in the format of (RL, AP, IS) in m, where R = Right, L = Left, A = Anterior, P =
Posterior, I = Inferior, and S = Superior.

For the data analysis of each individual subject, the impulse response function
(IRF) at each voxel with respect to each stimulus condition was resolved with multiple
linear regressions using the “3dDeconvolve” software in AFNI (Ward, Shum et al.
2002). The IRFs were resolved to seven points from zero to 15 sec at the resolution of

2.5 sec. The BOLD signal change was calculated based on the area under the [RF curve.

55

The equivalent BOLD percent signal change relative to the baseline state was then
calculated for each ﬂanker type (congruent and incongruent). The MRI signal modeling
also included the subject motion estimations in the three translational and the three
rotational directions, and the constant, linear and quadratic trends for each of the three
functional runs.

A region of interest analysis was used to compare the BOLD response between
conditions. After deconvolution of the impulse response function, the BOLD activation
was extracted from two regions in the frontal cortex previously shown to be

differentially active during a similar rapid-event related ﬂanker paradigm in older adults

(Zhu 2008). These regions were a 849 mm3 cluster at the right inferior frontal gyrus,

middle frontal gyrus, precentral gyrus (centroid at R39,A3,S32) and a 437 mm3 cluster

at the right middle frontal gyrus, Brodmann Area 6 (centroid at R29, P1, S54). The
BOLD activation from the incongruent condition compared to ﬁxation was examined.
Peak BOLD signal intensity (SI) was compared across conditions with repeated

measures ANOVA. Signiﬁcance was set at p < 0.05.

56

Results
Table 4: Participant characteristics

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Characteristic (n = 19 unless otherwise indicated) Value
Age (years) 69.7 1 4.9
Number female/male 14/5
body mass index (kglmz) 24.7 1 3.2
years of education 16 1 3
systolic blood pressure (mmHg) 124 1 l4
diastolic blood pressure (mmHg) 74 1 8
ankle brachial index a 1.2 :l: 0.1
resting heart rate (beats/min) 63 1 7
physical activity (kilocalories/day) (n=l6) 254 1 114
physical activity (bouts per week of moderate or more

intense physical activity) (n=l6) 1.6 1 1.9
Fasting triglycerides (mg/d1) 101 1 16
Total cholesterol (mg/d1) 205 1 12
HDL cholesterol (mg/d1) 70 1 9
LDL cholesterol (mg/d1) 118 1 10
Fasting glucose (mmol/L) 5.0 1 0.2
Fasting insulin (umol/L) 0'2 1 0.1
mini-mental state examination score 291 1 1
St. Louis University mental status score C 28 1 2.5
geriatric depression scale score d 0.1 1 0.4

 

 

57

 

Table 4 (continued)

Values are mean 1 standard deviation.

Abbreviations: HDL = high density lipoprotein, LDL = low density
lipoprotein.

a average of right and left. Ankle brachial index was calculated as the ratio

of the blood pressure in the ankle (average of pressure of posterior and anterior
tibial arteries) to the blood pressure in the brachial artery.

b score out of 30 on MMSE (Folstein and Whitehouse 1983); cognitive
impairment may be indicated by a score < 27 among individuals with a high school
education, or a score < 24 among individuals without a high school diploma.

C O 0 C C
score on SLUMS out of 30; a score < 27 may indicate cognitive
impairment.

score on the short form of the geriatric depression scale (Almeida and
Almeida 1999) out of a possible 15 points. A score > 5 indicates possible
depression and the need for medical follow-up.

58

 

Table 5: Average habitual daily intake of study participants.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Nutrient Intake Range
kilocalories 1778 1214 — 2579
% kilocalories from fat 33 1 4 25 — 38
% kilocalories from saturated fat 10 1 2 6 — l3
n-3 fatty acids, g 0.5 1 0.5 0.1 - 2.5
% kilocalories from protein 16 1 5 11 — 32
% kilocalories from carbohydrate 52 1 6 34 — 58
dietary ﬁber, g 22 1 6 11 — 35
servings of fruit and vegetables 4.3 1 2.3 2.0 — 9.3
vitamin C, mg 122 1 68 55 - 297
vitamin E, mg 9.7 1 8.5 0.7 — 33.5
potassium, mg 2259 1 559 1517 - 3710
folate, dietary folate equivalents, pg 446 1 249 145 — 871
vitamin B12, pg 5.5 1 4.1 2.1 — 16.8

 

Values are based on three day food records of usual intake and presented as
mean 1 standard deviation and range; n=l9.

59

Table 6 shows the timing of data collection for the subjects.

Table 6: Timing of data collection

 

 

 

 

 

 

 

 

 

 

 

SB NB NB + AE
completion of breakfast 16 1 5 l9 1 6 l9 1 6
video or aerobic exercise 98 1 9 93 1 12 111 1 15
completion of aerobic exercise N/A N/A 161 1 17
start of CogState® 156 1 8 152 1 14 172 1 l6
postprandial blood draw 189 1 11 176 1 46 204 1 18
MRI and ﬂanker task (midpoint) 204 1 15 201 1 20 220 1 16

 

 

Values are presented as mean 1 standard deviation in minutes after being served
breakfast (n: 19).

Breakfast: A total of eight participants ate the 400 kcal breakfasts, ten ate the
550 kcal breakfasts, and one ate the 700 kcal breakfasts. Compliance for breakfast
consumption among the nineteen participants was nearly complete. All but one
consumed the entire SB, with the remaining participant refusing to drink the
decaffeinated coffee because of the taste. Two participants, one assigned to the 400
kcal breakfast and one assigned to the 550 kcal breakfast, each reported being too
full to ﬁnish the NB breakfast. The participant assigned to the 400 kcal breakfast
left uneaten half of the toast with almond butter and fruit spread (80 kcal), while the
other left uneaten half of the cereal with soy milk (100 kcal). Study data were

included for these subjects.

60

Exercise: The average exercise intensity during the 30 minute bout was 56 1
6 % HRR. Post-exercise systolic blood pressure tended to decrease from a resting
average of 127 1 15 mmHg pre-exercise to 119 1 13 mmHg post exercise (p=0.09),
and diastolic blood pressure signiﬁcantly decreased from 73 1 9 to 65 1 6 mmHg
(p=0.004). Systolic blood pressure at the end of exercise was 147 1 17 mmHg, and
diastolic was 69 1 9 mmHg. All 19 subjects completed the entire exercise bout.

Serum glucose, insulin, and lipid levels: Blood was drawn at an average of
190 minutes after the meal was served. Serum levels of glucose, insulin, and lipids
are shown in Figure 2, Figure 3, and Figure 4, respectively, for n=19 participants for
glucose, insulin, and triglycerides in SB and NB conditions, and n=18 participants
for NB + AE triglycerides (the laboratory did not report results for triglycerides for
one subject’s postprandial triglyceride levels in the NB + AE condition). There
were no differences in fasting values between the three visits with treatment. The
magnitude of change between fasting and postprandial serum glucose levels was
signiﬁcantly different in SB compared to NB + AE (p = 0.026), with a trend toward
a signiﬁcant difference in SB compared to NB (p = 0.052). For insulin, the
magnitude of the change from fasting levels was signiﬁcantly different in SB
compared to NB (p = 0.001) and to NB + AE (p = 0.013). For triglycerides, the
magnitude of the change from fasting to postprandial measurements was
signiﬁcantly different in SB compared to NB + AE (p < 0.001) with a trend toward
signiﬁcance for SB compared to NB (p = 0.055). For each of the three treatments,
total cholesterol, HDL cholesterol, and LDL cholesterol did not change signiﬁcantly

between the fasting state and the postprandial blood draw (data not shown).

61

Figure 2: Fasting and postprandial serum concentrations of glucose.

I Fasting l Postprandial

Glucose (mM)

 

 

SB NB NB + AE
Treatment

Fasting and postprandial serum glucose concentrations for n=19 participants. SB =
standard breakfast, NB = nutrient dense breakfast, AB = aerobic exercise. Values
are mean 1 SD.

62

Figure 3: Fasting and postprandial serum concentrations of insulin

l Fasting E Postprandial

Insulin (pM)
.6 9
as on

P
A

P
N

 

 

P
e

SB NB NB + AE

Treatment

Fasting and postprandial serum insulin concentrations for n=l9 participants. SB =
standard breakfast, NB = nutrient dense breakfast, AB = aerobic exercise. Values
are mean 1 SD.

63

Figure 4: Fasting and postprandial serum concentrations of triglycerides

I Fasting a Postprandial

180
160
1140 —
120
100

AC)
00

Triglycerides (mg/dl
(D
O

N
O

 

 

0

SB NB NB + AE
Treatment

Fasting and postprandial serum concentrations for n=19 participants for SB and NB,
and n=18 participants for NB + AE. SB = standard breakfast, NB = nutrient dense
breakfast, AB = aerobic exercise. * signiﬁcantly different from fasting, #
signiﬁcantly smaller postprandial increase compared to SB, p < 0.05. Values are
mean 1 SD.
CogState Results

Results for accuracy on the CogState® battery are shown in Figure 5 and
expressed as arcsine (% correct) for each task in the battery. Overall, ND and ND +

AE did not improve cognitive performance compared to SB. Repeated measures

analysis using a general linear model showed that for the prediction task,

64

performance was signiﬁcantly better during the NB condition compared to the NB +
AE condition (p = 0.031). For the other tasks, there was no effect of treatment or
time on accuracy. Results for response time are shown in Figure 6 and expressed as
log-transformation of time, in milliseconds, to answer the question. Repeated
measures analysis using a general linear model showed that for the monitoring task,
performance was signiﬁcantly faster during the NB condition compared to the SB
condition for response time (p = 0.048). There were no signiﬁcant effects of
treatment on accuracy or response time for the other cognitive tasks. In addition,

there was no signiﬁcant effect of which breakfast was served ﬁrst (data not shown).

65

Figure 5: CogState® battery results for response accuracy

Accuracy
2 - ISBINBINB+AE

Accuracy,
arcsine (% Correct)

 

 

CPAL OWL IDN ONB OCL MON PRD CPAROWLR
Cognitive Task

Data are shown as mean arcsine (percent correct) 1 standard deviation; n=l9.
CPAL=continuous paired associated learning, OWL=one word learn,
IDN=identiﬁcation, ONB=one back, OCL=one card learn, MON=monitoring,
PRD=prediction, CPAR=continuous paired associate learning recall, OWLR=one
word learn recall. * signiﬁcantly different from SB condition, p<0.05.

66

Figure 6: CogState® battery results for response speed

Speed
4 _ ISBENBINB+AE

Speed of response, log(msec)

 

 

CPAL OWL IDN ONB OCL MON PRD CPAROWLR
Cognitive Task

Data are shown as mean log (response time, msec) 1 standard deviation; n=l9.
CPAL=continuous paired associated learning, OWI;one word learn,
IDN=identiﬁcation, ONB=one back, OCL=one card learn, MON=monitoring,
PRD=prediction, CPAR=continuous paired associate learning recall, OWLR=one
word learn recall. * signiﬁcantly different from SB condition, p<0.05
Flanker results

Flanker results are summarized in Table lTable 7. Overall, performance
was highly accurate, with a 99% accuracy rate in C and 98% correct in IC over the
three runs and for each treatment condition. There was no signiﬁcant effect of
treatment on accuracy. As expected, the response times for IC and C were
signiﬁcantly different, with the overall average for IC 880 1 24 msec and C 762 1

37 msec (p<0.001). There were no signiﬁcant effects of time or treatment on the

percent difference in response time between [C and C.

67

Table 7: Flanker results

 

 

 

 

 

 

Time L-C,*100%
Acc C Time C Acc IC Time IC (IC — C) C
112 1 60
SB 99% 753 msec 99% 866 msec msec 15.0%
121 1 57
NB 98% 766 msec 98% 888 msec msec 16.1%
121 1 63
NB + AB 99% 766 msec 98% 888 msec msec 16.2%
118
Average 98% 762 msec 98% 880 msec msec 15.5%

 

 

 

 

 

 

 

 

Results for the Flanker test (n=l6). SB = standard breakfast. ND = nutrient dense
breakfast. AB = aerobic exercise. C = congruent condition. IC = incongruent
condition.
fMRI results

Brain activation was extracted from two distinct regions of interest: a 849
mm3 cluster at the right IFG, MFG, PFG (centroid at R39,A3,S32), and a 437 mm3
cluster at the right MFG, BA 6 (centroid at R29, Pl, S54), shown in Figure 9 and
Figure 10. The brain BOLD response between incongruent ﬂanker and baseline
(ﬁxation cross) conditions was similar between treatment conditions. Neither
region showed a signiﬁcant effect of treatment on peak BOLD response between
any of the conditions (Figure 7 and Figure 8). The peak percent signal change from
baseline for each condition averaged between 0.16% to 0.19% in the IFG / MFG /
PFG cluster (Figure 7), and 0.17% to 0.19% in the MFG / BA6 cluster (Figure 8).

When averaging over all treatments, the range of peak BOLD was 0.079-0.0359 %

in the IFG / MFG IPCG and 0.048 — 0.291 % in the MFG / BA6 cluster. The

68

average hemodynamic IRFs are shown for each condition in Figure 9 and Figure 10
for each region. Although there are subtle visual differences in the response curves,

there were no signiﬁcant differences between the conditions.

69

Figure 7: MI percent signal change in IFG / MFG / PCG

 

0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00

 

 

 

 

 

Percent Signal Change

 

SB NB NB+AE

Treatment Condition

Values are mean 1 standard deviation of percent signal change in the brain BOLD
response for the incongruent condition compared to baseline in a 849 mm3 cluster at
the right inferior frontal gyms (IFG), middle frontal gyrus (MFG), precentral gyrus
(PCG)(centroid at R39,A3,S32) (n=1 1). SB = standard breakfast, NB = nutrient
dense breakfast, NB + AB = nutrient dense breakfast plus aerobic exercise, R =
right, A = anterior, S = superior.

70

Figure 8: fMRI percent signal change in MFG / BA6

0.35 Y*—‘— 777-7” 7.11. 11,

 

0.3 #47 Md-“ , —ﬁ ,1 1”—

 

 

Percent Signal Change

  

Treatment Condition

Values are mean 1 standard deviation of percent signal change in the brain BOLD
response for the incongruent condition compared to baseline in a 437 mm3 cluster at
the right middle frontal gyrus (MFG), Brodmann Area 6 (BA6) (centroid at R29, Pl,
S54) (n=1 1). SB = standard breakfast, NB = nutrient dense breakfast, NB + AB =
nutrient dense breakfast plus aerobic exercise, R = right, A = anterior, S = superior.

71

Figure 9: Group BOLD response in IFG I MFG / PCG

 

 

 

Percent Signal Change

 

 

 

 

 

Group-averaged ROI analysis of the brain BOLD response in the IFG/MFG/PCG
region (n = 11). The graph shows the average impulse response function in
response to incongruent ﬂanker stimulus presentation. Images are of a 849 mm3
cluster located at the right inferior frontal gyrus (IFG), middle frontal gyrus (MFG),
precentral gyrus (PCG) (centroid at R39,A3,S32). SB = standard breakfast, NB =
nutrient dense breakfast, NB + AE = nutrient dense breakfast plus aerobic exercise,
R = right, A = anterior, S = superior.

72

Figure 10: Group BOLD response in MFG / BA6

 

0.2
cu 0.15
U
I:
(I
.C
2 0.1
N
C
.9
1”. 0.05
C
GD
2
3 o
3 4 5
-o.05

Time, TR (TR = 2.55)

 

Group-averaged ROI analysis of the brain BOLD response in the MFG/BA6 region
(11 = 11). The graph shows the average impulse response function in response to
incongruent ﬂanker stimulus presentation. Images are of a 437 mm3 cluster at the
right middle frontal gyms (MFG), Brodmann Area 6 (BA6) (centroid at R29, Pl,
SS4). SB = standard breakfast, NB = nutrient dense breakfast, NB + AB = nutrient
dense breakfast plus aerobic exercise, R = right, A = anterior, S = superior.

73

Discussion
The objective of this study was to evaluate the acute effects of a meal and

aerobic exercise on cognitive function and brain activation. Our study is an
important addition to current knowledge about the effect of lifestyle factors in this
population. In contrast to our hypotheses, we did not ﬁnd large overall effects of
treatment, with signiﬁcant differences found only in accuracy on the prediction task
(Figure 5) and in response speed in monitoring (Figure 6) in the CogState® battery.

To our knowledge, this study was the ﬁrst to compare the acute effects on
cognitive function and brain activation in a healthy aging population of a high
saturated fat, high sugar, low nutrient density meal with a nutrient-dense mixed
meal, comprised of nutrients such as antioxidants, n-3 fatty acids, and folic acid, in
combination with a bout of exercise. Much of the current data on healthy brain
aging comes from epidemiological studies demonstrating correlations rather than
causation. Participants in previous studies of acute effects have been younger adults
(Nappo, Esposito et al. 2002; Kashihara and Nakahara 2005). Furthermore, existing
studies on the acute effects of diet and exercise on cognition have only investigated
speciﬁc nutrients, such as cocoa ﬂavanols (Francis, Head et al. 2006) or a sole bout
of exercise rather than an entire meal in addition to exercise. Our study was also
novel in its inclusion of fMRI as well as the CogState® test battery to measure brain
activation in addition to cognitive performance in executive tasks.

Participant baseline characteristics categorized them as a healthy aging
population (Table 1). There was no cognitive impairment as measured by the

MMSE assessment, which is a widely used method of detecting cognitive

74

impairment (Mitchell 2009). The SLUMS assessment was developed in order to
improve diagnosis of mild neurocognitive disorder (Tariq, Tumosa et al. 2006). Our
study resulted in three subjects achieving normal MMSE scores of 30, 29, and 28,
and scores on the SLUMS (23, 24, and 25) that indicate possible cognitive
impairment. In general, lipid proﬁles were low risk, serum insulin and glucose were
within the healthy range, and the average blood pressure was normal. Analysis of
three-day food records showed that participants had adequate intake of key
nutrients, which may have impacted response to treatment. As an example, the
average intake for ﬁber of study participants reported in their three-day food records
was above the national average intake (Appendix 14); it is possible that participants
with a lower average ﬁber intake at baseline may have shown a greater effect of the
NB breakfast.

The American Heart Association and American College of Sports Medicine
recommends that older adults complete 30 minutes of moderate physical activity,
ﬁve or more days a week (Nelson, Rejeski et al. 2007). Out of the 16 participants
who had complete accelerometer data over seven days, two met those criteria
according to the accelerometer classiﬁcations of exercise intensity based on counts
per minute. This information was validated with subjective assessment of activity
in the corresponding logs.

Other tests have documented declines in executive function with age
(Zimmerman, DelBello et al. 2006), and the CogState® test also shows potential for
use in aging populations, as in our study. Another study that used this battery

resulted in similar scores on the identiﬁcation (attention) and one-back (visual

75

attention and working memory) tasks, which are the two speciﬁc tasks in common
with our study. Participants included healthy adults who were 69 years old on
average (Cargin, Maruff et al. 2007).

We had hypothesized a beneﬁcial effect of NB and a further increase in
performance with the addition of AE, but found no difference from the SB
treatment. The frontal lobe hypothesis of aging partially attributes decline to slow
changes in neuroanatomy during aging (Park, Smith et al. 1996). Therefore,
measurable changes in executive function would require time for interventions to
show effects. Changes in cognitive performance would not be detectable in an acute
study such as ours. The novelty of the CogState® battery and its customizable
nature make comparison between studies difﬁcult, but participants in our study
showed no signs of cognitive impairment based on their CogState® results when
compared to published data on healthy younger (age 46 1 10 years) participants
(Maruff, Thomas et al. 2009).

We did randomize the study treatment order, but it is unlikely that this study
was affected by potential practice effects in the CogState® battery. In one study, no
practice effects were shown with repeated testing over a period of one month. The
tasks tested in that study included identiﬁcation, monitoring, and one-back tests
(Falleti, Maruff et al. 2006). Additional testing investigated the practice effects of
taking the identiﬁcation and one-back tasks four times within three hours, and found
a maximum of 90 msec improvement in reaction time and 15% improvement in

accuracy between test-taking occasions (Collie, Maruff et al. 2003).

76

The Flanker arrow task tests inhibition abilities, which decline with age due
to decreased executive function (McCabe, Robertson et al. 2005) and decreased
ability to ignore proactive interference during memory tasks (Emery, Hale et al.
2008). Further potential differences between processing in younger and older adults
are revealed by the ﬁnding that aging is associated with slower reaction times in
ﬂanker tests both in congruent and incongruent conditions (Hillman, Motl et al.
2006; Zhu 2008). A study using a similar arrow ﬂanker paradigm found similar
results to our study. The study by Kamijo et al. did not ﬁnd signiﬁcant effects of
light and moderate exercise on ﬂanker inhibition in older adults aged 60-74 years
old (Kamijo, Hayashi et al. 2009). Reaction time improved only slightly in both
congruent and incongruent conditions. Therefore, our data are not inconsistent with
these recently published ﬁndings which do not show improved cognitive
performance during inhibition in older adults following acute aerobic exercise.

We had hypothesized that the difference in reaction time between C and IC
conditions would decrease in NB and even further with the addition of AE,
demonstrating enhanced ability to perform inhibition and decision making tasks.
However, the analysis of percent change in reaction time in the IC condition
compared to the C condition did not show an effect of treatment on any task. A
possible explanation for our ﬁndings could be that the SB, NB, and NB + AE
treatment conditions did not affect the particular functions tested by the Eriksen
ﬂanker arrow task at a detectable magnitude.

Another novel aspect of this study is the inclusion of fMRI to determine the

acute effects of diet and exercise on brain activation. We had hypothesized that NB

77

and AE would increase activation in inhibitory regions of the brain such as IFG and
MFG. We had expected an increase in BOLD response indicating increased
neuronal ﬁring during NB and AE treatments due to an improved cognitive response
during the Flanker task. However, as we did not measure behavior differences, lack
of differences in activation are expected, and we did not see differences between
treatments in the BOLD response. It is plausible that different factors in our study
may have had opposing effects on cerebral blood ﬂow, which may be correlated
with the endothelial function, or the brain BOLD response. Treatment factors in our
study that likely increased blood ﬂow or endothelial function included AE and
certain components of the NB breakfast such as monounsaturated fat (Karatzi,
Papamichael et al. 2008), and the antioxidant vitamins C and E (Plotnick, Corretti et
al. 1997). However, these factors may have been negated by a potential decrease in
the brain BOLD response in our target brain regions after NB and AE due to
decreased effort used to complete the Flanker task. In addition, the fMRI exam was
completed on average approximately 90 rrrinutes after AE, which may have been too
long after AE for increased blood ﬂow to continue. As our study also found (data
not shown), blood pressure drops below pre-exercise levels within minutes after
completion of AE but recovers 90 minutes post-exercise (Kenney and Seals 1993;
Kulics, Collins et al. 1999).

The results of our study imply that the beneﬁcial effects of diet and exercise
do not occur immediately, and may only occur after longer exposure to a speciﬁc
treatment. For example, a cross sectional study among healthy older adults found

an association between vitamin B6 and 312 intake and greater grey matter volume,

78

and this is likely a long term effect of diet (Erickson, Suever et al. 2008). This
conclusion is consistent with longer term studies of both diet and exercise effects.
Certain dietary patterns are associated with maintenance of cognitive function
during aging (Kang, Ascherio et al. 2005) and (Masaki, Losonczy et al. 2000), while
others are associated with accelerated cognitive decline (Morris, Evans et al. 2004).
Physical ﬁtness, which is a likely result of habitual exercise, is associated with
increased cortical capillary supply, synaptic connections, and neuron development
among older adults (Kramer, Bherer et al. 2004). In addition, increased habitual
levels of physical activity may be associated with higher cognitive performance and
less cognitive decline (Weuve, Kang et al. 2004; Hillman, Motl et al. 2006),
although the reduction in relative risk of developing cognitive impairment may be
small (Podewils, Guallar et al. 2005).

The acute effects of diet and exercise on cognitive performance and brain
activation in an aging population have not been thoroughly investigated. Our study
adds to the knowledge in this area. Future acute studies may help elucidate the
mechanism behind the effects of diet and exercise. For example, future studies
could measure brain-derived neurotrophic factor (BDNF), which has been shown to
be important in cognition. The neurotrophic factor BDN F is associated with less
cognitive impairment in adults and is associated with increased synaptic and
neuronal plasticity (Gunstad, Benitez et al. 2008) which typically decline with age
(Burke and Barnes 2006). Since BDNF decreases with saturated fat intake
(Molteni, Wu et al. 2004) and increases with exercise (Gold, Schulz et al. 2003), our

study treatments likely affected BDNF levels.

79

Additional future directions could include more intervention studies. Some
studies should investigate effects of long-term dietary and exercise interventions on
cognition in older populations. Other studies might evaluate acute effects of
isolated nutrition components to determine speciﬁc effects, since our study only
looked at mixed meals. Additionally, studies could. compare results in younger and
older adults.

Future studies are needed to determine if longer term diet and exercise
regimens, such as for weeks or months, can improve cognition in older adults. An
effective method of determining the required time to achieve results would be to
implement a diet and exercise regimen and monitor subjects regularly over time
until changes in cognitive performance or brain activation can be detected. In
addition, the particular dietary components and exercise that may acutely affect
cognition should be further investigated in this population. This could be done by
including separate and combined treatments to unravel speciﬁc effects. In addition,
there is a need to determine the speciﬁc frequency, duration, and intensity of

exercise needed to achieve results.

80

Chapter III: Conclusions

This study was the ﬁrst we know of to investigate the acute effects of a
nutrient dense mixed meal in combination with a bout of exercise on cognitive
function and brain activation in healthy older adults without dementia. Many
previous studies on determinants of healthy brain aging have been epidemiological,
allowing them to demonstrate only correlations rather than causative relationships
between lifestyle factors and cognitive performance or decline. For example, a
Mediterranean diet and PA were associated with a lower risk of developing AD
according to the results of a prospective cohort study (Scarmeas, Luchsinger et al.
2009). Acute studies have shown positive effects of exercise on cognitive
performance, but subjects in these studies have mainly been animals (Soya,
Nakamura et al. 2007) or younger adults (Chui and Greenwood 2008; Audiffren,
Tomporowski et al. 2009). Older adults may respond differently than young adults
to a meal or a bout of physical activity.

Furthermore, existing studies investigating acute effects of diet and exercise
have looked only at isolated meal components or a bout of exercise rather than an
entire meal in addition to exercise (Hillman, Snook et al. 2003; Francis, Head et al.
2006). The present study looked at the effects of a full meal in addition to a bout of
exercise. Compared to a speciﬁc food or nutritional supplement, a mixed meal,
such as the NB breakfast that was included in this study, is more representative of
what individuals might choose to improve their cognitive and overall health during
aging. Our study did not ﬁnd acute effects of a single meal on cognitive function,

underscoring the importance of the need to systematically evaluate long-term

81

dietary patterns in brain health during aging. Long-term intervention studies could
support data from observational studies that show associations between diet and

cognitive function.

Population
The relative uniqueness of our study population compared to populations

studied in the published literature may help to explain our study results. Cognitive
status, typical dietary intake and nutritional status, and physical activity habits may
all inﬂuence the effect of diet or exercise interventions such as those in our study.
Previous studies have mainly been conducted in populations that may be different
from in the current study in their ages (Francis, Head et al. 2006) or health
condition, such as cognitive or nutritional status (Malouf, Grimley et al. 2003). For
example, one of the few MRI studies investigating the effects of a dietary
component found that cocoa ﬂavanol ingestion led to an increase in the brain BOLD
response (Francis, Head et al. 2006). However, this study was conducted in younger
adults, who may have a greater BOLD response than older adults (Ances, Liang et
al. 2008).

Cognitive health is another important factor for study results. Some
experimental studies ﬁnding clear implications about the role of diet in cognitive
health focus on populations with cognitive impairment at baseline. For example,
studies have examined the progression of cognitive decline during cognitive
disorders such as AD (Aisen, Schneider et al. 2008) or MCI or dementia (Malouf,
Grimley et al. 2003). One such investigation found that increased intake of vitamins
C and E from supplements or food sources like oils may slow cognitive decline

during AD (Engelhart, Geerlings et a1. 2002); this may be because of certain altered
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metabolism displayed by AD patients compared to healthy individuals. Patients
with AD have decreased vitamin C and E antioxidant capacity (Foy, Passmore et al.
1999), as well as increased oxidative stress in brain tissue and an associated lower
vitamin C status (Riviere, Birlouez-Aragon et al. 1998) than healthy controls. This
may explain the efﬁcacy of antioxidant supplementation in AD, and suggests that
supplementation does not necessarily improve cognition function in healthy
individuals. Healthy individuals may respond to speciﬁc lifestyle regimens
differently than AD or otherwise cognitively impaired individuals. This is
illustrated by a set of data from the Honolulu Aging Study also focusing on the
effects of vitamin C and E supplement use on cognitive function and decline during
aging. Initial analysis suggested a protective effect of vitamin C and E
supplementation against vascular dementia and cognitive decline (Masaki,
Losonczy et al. 2000). However, later analysis to exclude those with dementia at
the start of the study found that there was no association between supplementation
use and risk of dementia (Laurin, Foley et al. 2002). Our study, which was
conducted among individuals with no known cognitive impairment, may have
different results than studies among cognitively impaired individuals. Our study
population was also highly educated, with an average of 16 years of formal
education.

Nutritional status, typical intake, and exercise habits are other important
considerations when interpreting the results of studies on nutrition and cognition
because they can determine how certain nutrients affect cognition. For example,

data from NHANES show varying relationships between folate intake and cognitive

83

function depending on nutritional status. In participants with adequate vitamin B12
status, higher folate intake was associated with decreased relative risk (0.4) of
cognitive impairment; conversely, higher folate intake along with vitamin B12
deﬁciency was associated with a higher relative risk (2.6) of cognitive impairment
(Morris, Jacques et al. 2007). These results demonstrate the signiﬁcance of nutrient
status when evaluating the effects of dietary interventions on cognitive function.
For our study, participants were likely nutritionally adequate based on their general
health and three-day food record of usual intake (Table 5). Furthermore, compared
to national averages, study participants on average had higher intakes of nutrients
such as ﬁber (22 grams compared to 14) that indicate overall dietary quality.
Participants were near national intakes for folate, vitamin B12, and potassium

(USDA 2008).

Study Meals: Standard Breakfast and Nutrient Dense Breakfast
Formulation of the SB and NB breakfasts was based on available studies

regarding the relationship between nutrition and exercise. According to the
literature, certain dietary patterns are associated with improved cognition and slower
cognitive decline over time (Masaki, Losonczy et al. 2000; Kang, Ascherio et al.
2005), while others are associated with accelerated cognitive decline (Morris, Evans
et al. 2004). The SB meal was reﬂective of a common American breakfast,
including pork sausage, scrambled eggs and cheese, toast and jelly, decaffeinated
coffee, and a sugar-based fruit drink. The SB meal included dietary factors that may
be detrimental to cognitive health over time. As shown in Table 1, 25% of the

calories in SB were from saturated fat, and another 25% of the calories were from

84

sugar. Each of these nutrients has been linked to impaired cognition or accelerated
cognitive decline (Molteni, Barnard et al. 2002; Morris, Evans et al. 2004).

In contrast to the SB breakfast, the NB breakfast was formulated to beneﬁt
cognitive function. It minimized saturated fat (< 1% total energy), and was
extremely low in added sugars, both of which may impair cognition. This breakfast
was designed to improve cognitive performance by incorporating foods and
nutrients such as vegetables, n-3 fatty acids, soy products, and blueberries, which
have been linked to improved cognition according to the results of previous studies
(Denny 2008). Overall, there were minimal effects of diet on cognitive function;
most tasks showed no difference between treatments.

Response time during a monitoring task involving attention was slightly
faster for NB compared to SB. The outcome of minimal differences in the main
cognitive outcomes in this study may partially be attributed to the dietary factors
used in our study. For example, the NB breakfast incorporated a generous amount
of fruits and vegetables (2.5 servings per 700 kcal) because of evidence suggesting
that fruit and vegetable intake may be a positive factor in brain health during aging
(Polidori, Pratico et al. 2009). One particularly convincing cross-sectional study
compared healthy subjects aged 45-102 years classiﬁed as having high intake or low
intake according to responses on food frequency questionnaires. After accounting
for age, nearly every individual in the higher intake group scored higher than nearly
every individual in the low intake group. Similar results were found with plasma
antioxidant status and cognitive scores (Polidori, Pratico et al. 2009). A separate

study found a slower rate of cognitive decline among aging women with the highest

85

cruciferous vegetable and green leafy vegetable intakes (Kang, Ascherio et al.
2005)

The fat content of the SB and NB breakfasts was another interesting dietary
factor in our study. Studies have published conﬂicting results about the associations
between dietary fats and cognition. For instance, the incidence of dementia in
adults over 55 years old was found to have no association with dietary intake of
polyunsaturated fat, monounsaturated fat, n-3 or n-6 fatty acids, trans fats, saturated
fat, total fat, or cholesterol (Engelhart, Ruitenberg et al. 2005). However, other
studies have found that fatty acid composition can impact cognition during aging.
Morris et al found that saturated and trans fat intakes were associated with greater
score decreases over a 6 year follow-up of African-American and white males over
65 years old in the Chicago Health and Aging Project (Morris, Evans et al. 2004).
Two studies reported conﬂicting results when searching for a link between ﬁsh
intake, which is associated with omega-3 FAs, and cognitive decline (Morris, Evans
et al. 2005; Nurk, Drevon et al. 2007). To add to the assortment of results, another
study among 50-70 year old adults found no cross-sectional differences between
those with higher n-3 versus lower n-3 intakes, but a slower decline over three years
in sensorimotor and complex speed (Dullemeijer, Durga et al. 2007). We chose to
include saturated fat in the SB meal because of its possible actions to decrease
cognitive function (Solfrizzi, Panza et al. 2003), and to include n-3 fatty acids in the
NB diet for their potential to improve cognitive function (Kalmijn, Feskens et al.

1997).

86

Another explanation for our results is that despite basing our SB and NB
breakfasts on available information about cognition, it is true that currently, little is
deﬁnitive regarding the effects of nutrients on cognition. In addition to individual
variation combined with differences in measurement techniques attributed to study
design, many personal factors such as health and nutritional status could potentially
inﬂuence the brain directly, or interact with nutrients to change the magnitude or
even direction of cognitive effects. For instance, numerous cognitive studies have
focused on folic acid and B 12, which are likely to be signiﬁcant factors in brain
health. These vitamins have been studied somewhat extensively compared to some
other nutrients. They are a good example of complex interactions between nutrients
and personal factors, since conclusions have varied between studies and differences
may be attributable to population characteristics. One study found that folate and
vitamin B12 supplementation were associated with slowed decline in AD patients
(Remington, Chan et al. 2009), while another found that supplementation with a
combination of folic acid, vitamin B6, and vitamin BIZ in subjects sufﬁcient in
those nutrients had no effect on progression of AD (Aisen, Schneider et al. 2008).
In non-AD subjects, results from studies on the effects of folic acid and vitamin B 12
on cognition also vary. In a biracial longitudinal study, dietary intakes of neither
folic acid nor vitamin B 12 were associated with development of AD (Morris, Evans
et al. 2006). In this same study, analysis of data from non-AD aging adults
determined a protective effect of folate against cognitive impairment among those
with normal vitamin B12 status. However, folate was associated with increased

impaired in those with low vitamin BIZ status (Morris, Jacques et a1. 2007). And,

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in another study, daily supplementation with a mixed supplement including folic
acid and vitamin 812 for eighteen months showed no effect compared to the
placebo group on tests of cognitive function (van Uffelen, Chinapaw et al. 2008).

These studies have drawn different conclusions regarding the efﬁcacy of
dietary factors such as folic acid and vitamin B 12 in cognition. These conﬂicting
results suggest that we do not yet deﬁnitively know the effects of dietary factors on
cognition during aging. Furthermore, our study design had some differences from
much of the published literature. It is possible that if our study participants had a
lower habitual dietary quality, such as fewer fruits and vegetables, they might have
been more responsive to the study treatments. Based on analysis of usual dietary
intake of food records, our subjects were likely sufﬁcient in key nutrients including
folic acid and vitamin B12. In addition, subjects were cognitively healthy according
to MMSE and SLUMS evaluations. These and related factors, such as lower
saturated fat intake of our subjects compared to national averages, may have
affected the potential results of the different breakfasts in our study and explain
differences in results between our study and published studies.

This study was acute, investigating the effects of one single meal on
cognitive function and brain activation within hours of consumption. Since the
published literature is sparse regarding the acute effects of speciﬁc diet components
on cognition, dietary components in this study were largely chosen based on studies
correlating chronic diet with age-related changes in cognition. Other components
were chosen based on their short term effects on endothelial function. It is possible

that long term consumption of these diets could result in either accelerated cognitive

88

decline (SB) or in maintenance of cognitive function (NB) over longer time periods,
even though we found that acute differences were unremarkable.

While chronic dietary intervention could conceivably be required before the
emergence of measurable differences in the cognitive outcomes we measured, there
are other changes that may have taken place with the acute breakfasts in this study.
For example,-this study did not measure endothelial function or blood ﬂow, but
these may have been affected by the diet treatments. Many dietary components can
also either acutely increase or decrease endothelial function and blood ﬂow.
Consuming a high fat meal impairs endothelial function about 2-4 hours
postprandial compared to a lower fat meal (Vogel, Corretti et al. 1997). Saturated
and trans fatty acids may be especially potent in decreasing vasoreactivity. The
postprandial decrease in ﬂow-mediated vasodilation can be attenuated by
consuming walnuts, a source of n-3 polyunsaturated fatty acids, or salad (romaine
lettuce, carrot, and tomato) along with the meal, or consuming the antioxidant
vitamins C and E with the high fat meal (Plotnick, Corretti et al. 1997; Cortes,
Nunez et al. 2006). In one study, cocoa ﬂavanols increased vasodilation and also
the brain BOLD response during an MRI task (Francis, Head et al. 2006).
Together, these observations demonstrate nutrition’s role in acute physiological

effects, some of which could conceivably affect cognition.

Exercise
Our study investigated the acute effects of a bout of moderate AE, i.e.,

walking on a treadmill at 50-65% HRR, on cognitive performance and brain

activation in an aging population. The methods and results lead to interesting

89

comparisons with previous studies. Studies investigating the acute effects of
exercise on cognition are varied in their participants, study design, and results.
Many of the previous studies on acute effects of exercise were conducted in a
younger adult population (Ferris, Williams et al. 2007; Coles and Tomporowski
2008; Pontifex, Hillman et al. 2009) or in animals (Huang, Jen et al. 2006; Soya,
Nakamura et al. 2007). In addition, the bout of exercise in these studies varies in
their mode, intensity, and duration. Our study adds to the body of knowledge about
the effects of a speciﬁed dose of exercise performed at these speciﬁcations.

Taken together with previous ﬁndings from long term studies, the ﬁndings
of our study suggest that AE may be more effective in long term rather than acute
cognition. Some intervention studies have been conducted to investigate the effects
of exercise on cognitive function. Some have found positive cognitive effects of
exercise programs (Cotman and Berchtold 2002). In one study, 70-80 year old men
and women diagnosed with MCI walked twice weekly in a group setting for a year.
Compared to a low-intensity exercise placebo control group, men who were
assigned to group walks improved memory (auditory verbal learning), and women
in the exercise group improved attention (stroop speed) and memory (auditory
verbal learning) (van Uffelen, Chinapaw et al. 2008). A six-month clinical trial in
healthy but sedentary adults aged 60-79 years compared the effects of three times
weekly 60 minute sessions of aerobic training to stretching and toning controls. This
study found increases in gray and white matter brain regions in the training group

(Colcombe, Erickson et al. 2006).

90

The signiﬁcance of routine exercise in brain health is consistent with the
overall trend found in a meta-analysis of epidemiological studies that found
associations between ﬁtness and cognitive function in older adults (Berkman,
Seeman et al. 1993; Colcombe and Kramer 2003). Other reviews have also
suggested an overall effect of exercise or ﬁtness in reducing cognitive impairment
or maintaining cognitive function with aging (Hillman, Erickson et al. 2008;
Deslandes, Moraes et al. 2009). However, one meta-regression was unable to make
deﬁnitive conclusions regarding the effects of ﬁtness, ﬁnding that effects varied
based on age and health status (Etnier, Nowell et al. 2006). Similarly, another study
included participants aged 65 or older at baseline and found a only a non-signiﬁcant
relative risk of 0.85 for developing dementia over the next 5.4 for those in the
highest quartile of physical activity compared to the lowest quartile (Podewils,
Guallar et al. 2005).

As mentioned earlier, a separate study looked at the effects of a 14-day
“longevity” lifestyle intervention on cognitive function and brain activation. The
exercise component of the intervention included 3 bouts of walking per week.
(Small, Silverman et al. 2006). Interestingly, the intervention showed positive
effects on cognition despite the exercise program falling short of national
guidelines. It is important to note, though, that the exercise was only a portion of
the intervention and may not have affected results. These results show promise for
positive effects of exercise even without meeting the recommendations for at least 5
days per week of moderate intensity (Nelson, Rejeski et al. 2007). Exercise regimes

less rigorous than national guidelines may be more realistic for older adults, since

91

only a self-reported 32% of Americans meet national guidelines for physical activity
(1996), and over half of adults over age 65 report no leisure time physical activity
(2008). Our study population was consistent with these national ﬁgures,
averaging1.6 bouts per week (Table 4). Only 13% (2 of 16 subjects with complete
physical activity data) met the recommended ﬁve bouts per week, and 25% (4 out of
16 subjects) achieved at least three bouts per week.

Improvements in, maintenance of, or slower declines in cognitive parameters
are consistent with what we know about the adaptations that occur with habitual
exercise. The cardiovascular ﬁtness hypothesis relates physical ﬁtness to the
adaptations that occur over time that reduce the risk of cardiovascular disease
(Hamilton, Hamilton et al. 2007). Adaptations associated with exercise that reduce
risk of CVD include improved vasoelasticity, weight control, decreased blood
pressure, and improved blood lipid proﬁle (Brown, Avenell et al. 2009; Yung, Laher
et al. 2009). Exercise also increases CBF (Swain, Harris et al. 2003) and increases
production of brain-derived neurotrophic factor (BDNF) (Vaynman and Gomez-
Pinilla 2005). In further support of the cardiovascular ﬁtness hypothesis, a cross
sectional study compared active older adults (deﬁned as at least 180 minutes per
week in sport or aerobic activity over the past ten years) to inactive (deﬁned as less
than 90 minutes per week over the past ten years) older adults. The study used MR
angiography to show that the highly active group had lower tortuosity in large
vessels and an increased number of small vessels than lower physically active group
in cerebrovasculature (Bullitt, Rahman et al. 2009). Another observation supporting

the cardiovascular ﬁtness hypothesis is the association of faster left ventricular

92

expansion, which is part of the pathophysiology of coronary heart disease, with
earlier development of MCI (Carlson, Moore et al. 2008).

Serum BDNF is another factor implicated in the relationship between
exercise and cognitive function. Higher levels of BDNF are associated with less
cognitive impairment in otherwise healthy older adults (Gunstad, Benitez et al.
2008). Higher BDNF levels lead to increased synaptic and neuronal plasticity,
which is lower with aging (Burke and Barnes 2006). Serum levels of BDNF have
been shown to increase with short bouts (15-30 minutes) of exercise (Gold, Schulz
et al. 2003; Tang, Chu et al. 2008). However, these studies were both done in
younger adult populations, and BDN F increases were transient, lasting only about
30-50 minutes. Since the present study did not measure BDNF, it is unknown
whether BDNF levels were affected by the bout of exercise. Even if BDNF levels
did increase, it is possible that there would have been no effect on the cognitive
outcomes of the study. Exercise-induced BDN F is only transient, about 30-50
minutes, and participants ﬁnished their cognitive and fMRI testing approximately
80 minutes after exercise completion. Another reason for the longer term effects of
exercise on cognition might be that BDNF increases plasticity in the brain, which
aids learning by allowing the development of novel pathways.

The parameters of our bout of AE complement the existing literature and
may help give insight into how exercise might impact cognition in an aging
population. The AB in our study was a relatively moderate session of walking on a
treadmill at an intensity of 50-65% of estimated HRR based on the age of the

participant. Some studies ﬁnding rapid improvements in cognition after a single

93

period of exercise have used resistance exercise rather than continuous exercise as
the treatment. Some have found that resistance exercise is effective (Lin-Ambrose
and Donaldson 2009), while others have found AE to be more effective than
resistance exercise for cognition (Pontifex, Hillman et al. 2009). Others have used
cycle ergometry as the mode of exercise, and found improvements in executive
function (Cordova, Silva et al. 2009). It also appears that intensity may be a factor
in the efﬁcacy of exercise in cognitive function. In the study mentioned above,
physically active women aged an average of 64 years were randomly assigned to 20
minute bouts of exercise on a cycle ergometer at an intensity of 0% (no exercise),
60%, 90%, and 110% of aerobic threshold. After exercising, subjects underwent
cognitive testing for domains requiring executive function. Simple response time
(alertness) was improved for all exercise groups, and other test results were
unchanged for the 60% and impaired for the 110% group. In the group performing
exercise at 90%, the executive function tasks of verbal ﬂuency, Tower of Hanoi
(number of movements), and Trail Making Task B, were improved compared to the
control group. (Cordova, Silva et al. 2009). Results of this and other studies
underscore the signiﬁcance of exercise intensity on cognitive function.

Aside from type and intensity of exercise, the timing of measurement of
cognitive function or brain activation relative to the bout of exercise is another
important variable. Our study measured cognitive function using the CogState®
test battery with the testing session beginning at an average of 10 minutes after
completion of AE including cool down. The fMRI exam commenced an average of

35 minutes after starting the CogState® battery, or a total of 45 minutes after

94

completion of AE. Previous studies have implied that small differences in timing
may greatly affect results. In studies that tested cognitive function at differing time
points following exercise, healthy older adults showed improvements in cognitive
function after eight minutes after completing exercise (Cordova, Silva et al. 2009),
and adults with chronic obstructive pulmonary disease showed some improvements
in verbal ﬂuency twenty minutes after ﬁnishing maximal exercise (and ﬁve minutes
after completing cool down) (Emery, Honn et al. 2001). In contrast, younger adults
showed no changes in executive function when tested 48 minutes after exercise
completion (Hillman, Snook et al. 2003). Another study found improved executive
function in young adults during but not after completion of an exercise bout on a
cycle ergometer (Audiffren, Tomporowski et al. 2009). Because there are further
differences in these studies beyond timing of testing, currently we can only
speculate about the role of timing when trying to determine the acute effects of AB

on cognition.

CogState®

This study was compared executive performance using CogState® tests
during each of the three study treatments: SB, NB, and NB + AB. The CogState®
computerized system is a recently developed method to test cognitive function in a
short amount of time. To compose the CogState® battery for a particular study, the
study investigator chooses speciﬁc tasks from the selection of tasks developed by
the CogState® company (CogState® Limited, Melbourne, Australia). The speciﬁc
tasks and cognition skills used in our study are listed in Table 3. To achieve normal

distributions, accuracy was reported as the inverse sine function of the percent

95

correct (Figure 5), and speed was reported as the logarithm of the response time in
milliseconds (Figure 6).

The only signiﬁcant effects of treatment that were detected in the CogState®
battery were on the prediction and monitoring tasks. For prediction, accuracy was
found to be different in NB versus NB + AE (p < 0.05). The prediction task targets
executive function, which was the focus of this study and is known to decrease
during aging. The decrease in accuracy in prediction with the addition of AE to NB
may plausibly be attributable to mental fatigue caused by exercise, but the
difference in percent correct (83% in NB versus 78% in NB + AB) is very small
despite its statistical signiﬁcance. For the monitoring task, which tests attention,
speed of response was signiﬁcantly slower in NB than in SB (p<0.05).

No signiﬁcant effect of treatment was found for the other tasks in the
battery; nor did we detect signiﬁcant effects of time. In addition, we found no
signiﬁcant differences between speed of response due to treatment or time.
Previous data have shown a practice effect between the ﬁrst and second attempts
when consecutive CogState® tests were administered with a 10 minute break. These
effects leveled off with repetition of the tests at one week and one month intervals
(Falleti, Maruff et al. 2006). Our study design included two unscored practice tests
during the introductory visit to the lab, and each task during those practice tests and
the subsequent scored tests during SB, NB, and NB + AB was preceded by a
practice round of tests. This study design appears to have been effective in

minimizing effects of time.

96

The MMSE scores of our population classiﬁed them as cognitively normal,
but the relative novelty of the CogState® test battery combined with its feature of
custornizability means that only a few previous studies are available to compare
baseline results. Another factor limiting the availability of baseline data is the
nature of the computerized battery. The researcher’s ﬂexibility in choosing which
tests to include limits the ability to compare the battery. Instead, each CogState®
test battery is composed of a unique collection of tasks, which are sequentially
ordered according to the researcher’s preference.

Furthermore, the company is continually updating the tasks that are offered,
meaning that some tasks, available for only limited periods of time, are included in
only a relatively small number or studies. However, one previous study
investigating cognitive function was conducted over six years in a comparable study
population of healthy adults averaging 69.2 years of age at baseline, and 64%
female. Cognitive changes were tracked using a variety of cognitive tests, including
two of the same CogState® tasks to assess cognitive function as in our study. These
tasks were the identiﬁcation (to test attention) and the one-back (to test visual
attention and working memory) tasks. Among adults in the study who did not
display cognitive decline during the duration of the study, scores on the
identiﬁcation task averaged 97% accuracy and 550 msec reaction time, compared
to our average of 99% accuracy and 540 msec reaction time, and scores on the one-
back task averaged 82% accuracy and 851 msec reaction time, compared to our
average of 96% accuracy and 770 msec reaction time (Cargin, Maruff et al. 2007).

It is difﬁcult to compare across studies, but these appear to be minor differences. A

97

possible inﬂuence is the average education level attained by participants in each
study. Cargin et al. reported an average of 13 years of education, while our study
participants averaged 16 years.

Existing studies have largely focused on different applications than our
study. The CogState® test battery has been used to detect mild cognitive
impairment (Darby, Maruff et al. 2002) or memory decline over the course of
months (Maruff, Collie et al. 2004). Other applications have included assessment of
cognitive status following head injury during sports such as soccer (Makdissi, Collie
et al. 2001). However, the battery has not previously been used to detect acute

treatment effects in a healthy aging population.

Eriksen Flanker and Brain Activation
To compare acute effects of SB, NB, and NB + AB on cognition and brain

activation, this study also assessed performance on an Eriksen ﬂanker arrow task
and measured brain activation using MRI and the brain BOLD response. The
Eriksen ﬂanker arrow task presents conditions that are congruent or incongruent,
with the incongruent condition requiring response inhibition. The task was designed
to elicit nearly perfect responses in accuracy in both congruent and incongruent
conditions, and our study’s results were consistent with this intention (e.g., 98%
accuracy). To assess the effects of treatment and time, we compared relative
changes in response time between congruent and incongruent conditions across
treatment conditions and time (visit number). This analysis yielded no signiﬁcant
differences.

The Eriksen ﬂanker arrow task paradigm was also used as a stimulus to

activate relevant areas of the brain for detection of the brain BOLD response during

98

fMRI. A recent fMRI study investigated brain activation during a similar conﬂict
resolution paradigm to our study. That study detected activation in the
IFG/MFG/SFG and MFG/BA6 regions (Zhu 2008). Other studies have shown
activation in the anterior cingulate cortex (Pochon, Riis et al. 2008; Ochsner,
Hughes et al. 2009), and medial frontal cortex (Ochsner, Hughes et al. 2009). These
areas are known to be important in conﬂict resolution (Andersson, Ystad et al.
2009), and function may be impaired with age.

We had hypothesized that AE would increase blood ﬂow to the brain, thus
improving cognitive performance and increasing the brain BOLD response.

Overall, the study’s participants were healthy older adults, with no known
peripheral arterial disease or unstable cardiovascular conditions. The healthy status
of our participants may have masked possible changes in the BOLD response.

The brain BOLD response is dependent on the amount of oxygen reaching
the brain, and the magnitude of the response has been shown to be dependent on
baseline cerebral blood ﬂow (CBF). Increased CBF leads to a smaller response,
while decreased CBF allows for a larger BOLD response. Factors affecting
vasoreactivity of vessels in the brain and brain perfusion can potentially affect the
magnitude of the BOLD response. Nutrients and exercise each have the potential to
inﬂuence endothelial function.

Dietary components that have been shown to improve endothelial function
include antioxidants (Plotnick, Corretti et al. 1997), n-3 fatty acids (Cortes, Nunez et
al. 2006), and cocoa ﬂavanols (Fisher, Sorond et al. 2006), while saturated fats have

been shown to impair endothelial function (Cortes, Nunez et al. 2006) and high

99

carbohydrate meals impair endothelial function in insulin-resistant individuals

(W est 2001). Some components of the SB and NB meals in our study were based
on these effects because of the possibility of effects on the BOLD response.

Like diet, exercise also affects peripheral vasculature, leading to our hypothesis that
the NB + AE condition would increase brain activation compared to the SB and NB
conditions. A normal response to exercise includes increased systolic and decreased
diastolic blood pressure (Singh, Larson et al. 1999). As reported in the results,
blood pressure dropped below pre-exercise levels within minutes of ﬁnishing AE.
This was likely due to vasodilation arising from decreased sympathetic output.
These changes are consistent with previous studies showing that vascular
compliance improves after moderate exercise (Kingwell, Berry et al. 1997). In
addition, exercise increases cerebral blood ﬂow through the mechanisms of
increased blood pressure and decreased pH from increased C02 (Ide, Boushel et al.
2000). These peripheral effects of exercise could potentially eventually affect the
brain BOLD response.

The incongruent ﬂanker task condition elicited a greater BOLD response
compared to baseline in the lFG/MFG/PCG (Figure 9) and MFG/BA6 (Figure 10)
regions. These ﬁndings are consistent with brain imaging studies investigating
executive function during aging. For example, (Small, Silverman et al. 2006) used
positron emission tomography to show differential activation with a longevity
lifestyle task in Brodmann Area 6. More important, a recent study was conducted
using a similar Eriksen ﬂanker arrow paradigm in the same MRI scanner (Zhu

2008). That study found activation in the inferior and middle frontal gyrus and is

100

consistent with our ﬁndings. In fact, our analysis was based on the R013 deﬁned in
the study by Zhu et al. Repeated measures analysis of variance did not detect an
effect of treatment among our subjects in either ROI (Figure 7 and Figure 8).

We did not ﬁnd differences in the percent change in activation between the
C and IC conditions during the ﬂanker task in frontal regions. Even if the treatment
conditions altered CBF as expected, this lack of treatment effects may partially be
explained by the age of our participants. For example, although CBF and the brain
BOLD response are typically correlated, this relationship is less clear in older
adults. One study found the BOLD response to be lower in older adults than
younger ones despite similar changes in CBF in response to a visual checkerboard
stimulus (Ances, Liang et al. 2008). This ﬁnding could explain why we found only
nominal effects of treatment on the brain BOLD response during fMRI. Another
explanation is that effects might not necessarily be systemic; on the contrary,
peripheral effects of treatment might not be generalizable to CBF and brain

activation.

Study Strengths

This study was an important initial step in determining the role for diet and
exercise in brain health during aging. A practical long term goal is to adequately
understand the role of lifestyle factors to be able to make recommendations for
individuals to follow in daily life to optimize brain health. To do this, it is necessary
to increase our understanding of the lifestyle factors that impact cognitive function
during aging. This is done using experimental studies to evaluate cause and effect
versus the potential correlations or associations found in epidemiological cohort

studies. This acute study directly compared the effects of different treatments in a

101

three arm randomized control design, so that any differences found could reasonably
be attributed to treatment effects. Furthermore, this study addressed the issue of
concern, which is brain function during aging, because study participants were from
an aging population rather than a standard young healthy adult population as in
some previous studies (Hindmarch, Quinlan et al. 1998; Coles and Tomporowski
2008; Pontifex, Hillman et al. 2009).

Another strength of this study was its design. It was a randomized,
crossover design study with three arms: SB, NB, and NB plus AE. Participants
were healthy residents 61-84 years of age, and were included in the study only after
screening procedures. Before undergoing study treatment, participants were
familiarized with the different aspects of the study to ensure accurate results and
avoid difﬁculties on the actual days of data collection. This included an
introduction to the MRI scanner using a mock brain image to ensure that
claustrophobia was not a problem. Another procedure to enhance participant
familiarity included walking on the treadmill, during the familiarization session, at
the projected incline and pace of the bout of exercise to be performed during the NB
plus AE session. Participants also practiced the CogState® battery of tests twice to
avoid practice effects during the real testing periods, and were introduced to the
Flanker arrow task. The study design was also strong because treatments were
representative of situations that could be replicated in real life. The diets were
mixed meals composed of ordinary foods, and the bout of exercise fell within the
range of the intensity and amount recommended by the AHA and ACSM for older

adults (Nelson, Rejeski et al. 2007). Finally, combining diet and AE, as was done

102

for the third treatment arm of the study (NB + AE), is practical because some
individuals seeking to improve brain health may be attentive to both diet and AE.

This study was well controlled to minimize effects of potential confounders.
Each participant had their laboratory visits scheduled to be separated by at least
three days to avoid any possible potential for residual effects of treatment. To
ensure that results were due to treatment and not to confounding recent lifestyle
factors, participants were asked to continue their usual diet and exercise regimens
for the duration of the study. The exception to this request was that individuals
were asked to refrain from exercising the day before each treatment visit to be sure
that any effects of AE or lack thereof were due to the study treatment. Because of
the acute nature of the study, subject visits were carefully timed to maximize
similarities in experiences between and within subjects. An ACLS-certiﬁed
physician attended each bout of AE during the study. Aside from ensuring safety,
this supervision increased conﬁdence that participants were at the target intensity
through careful monitoring of blood pressure, heart rate, and ECG. All of these
factors combine to demonstrate the novelty, solid design, and careful control of this
study.

Another strength of this study was its multiple outcome measures. We
evaluated both cognitive function, using the CogState® battery, and brain
activation, using fMRI. Assessing both of these measures increased the likelihood
of observing potential effects of diet and PA on the brain. Beside uncovering
differences due to treatment, including the CogState® and fMRI could help explain

how these treatments might be effective. Understanding both cognitive

103

performance and brain activation could give insight into potential mechanisms for
how lifestyle factors could affect the brain. For example, a change in brain
activation without a change in cognitive performance with treatment could imply a
compensatory effect that uses alternate pathways in the brain to process information.
Study Limitations

This study focused on the acute effects of the modiﬁable lifestyle factors of
diet and exercise on cognition. A longer term goal would be to improve brain health
during aging through altering diet and exercise. The treatments in this study did not
result in dramatic differences in their effects on cognition or brain activation, and
this may have been a result of being realistic rather than in supplemental doses or
intense exercise. Each treatment was deliberately developed to reﬂect reasonable
and realistic meals or exercise that an average older adult could conceivably
integrate into daily life. The potentially effective ingredients, such as vegetables,
omega-three fatty acids, or added sugars, might have been at quantities too small to
show an acute response if one existed. The AE bout may similarly have been too
moderate in intensity. To address these issues, it would be necessary to undertake
studies investigating different nutrients individually, and to conduct dose-response
studies both with nutrients and with quantity and intensity of exercise.
Another apparent weakness of the study is the lack of the logical fourth arm, which
would have been the treatment of SB plus AE. This fourth treatment was excluded
from the study for a variety of reasons, including money, time, and increased
participant commitment. The study design may also have introduced selection bias
into the study. Recruiting was through posted ﬂiers at the university where the

study was conducted, and potential participants chose to call and become involved

104

in the study. Our subjects were likely more interested in the study topic than the

general population and may have been more conscious of their lifestyle choices.

Future directions and implications
This study is an early investigation of the acute effects of diet and exercise

on cognition in an aging population. Future acute studies might seek to determine
the individual effects of speciﬁc nutrients or foods on cognitive function and brain
activation when doses are higher than in this study. Exercise duration, type, and
intensity could also be investigated. In addition to altering speciﬁc diet or exercise
treatments, the time course could also be modiﬁed in future studies to detemrine
whether these treatments are effective at times other than we measured. For
example, one study found an effect of exercise on cognitive function when
performance was measured at 8 minutes after completion of exercise (Cordova,
Silva et al. 2009), but another study found no effect at 48 minutes after exercise
completion (Hillman, Snook et al. 2003). Future studies should also measure BDNF
because it is increased with long term exercise and could be important in improving
cognitive function because of its role in synaptic plasticity (Vaynman, Ying et al.
2004).

Unlike the results of some epidemiological studies (Donini, De Felice et a1.
2007), this study showed no clear relationship between diet and AE and cognitive
function or brain activation in an aging population. This implies that effects may
require longer term interventions. While acute studies might provide insight to
mechanisms that would explain long-term effects and acute studies might not be

enough to cause the adaptations necessary at the cellular level that lead to visible

105

effects over time. Future studies are needed to determine the length of time
necessary to achieve improvements in cognition due to diet and exercise. An
effective method of determining the required time to achieve results would be to
implement a diet and exercise regimen and monitor subjects regularly over time
until changes in cognitive performance or brain activation can be seen. Study
groups would be a control group, a group with a nutrition intervention, a group with
an exercise intervention, and a group with both nutrition and exercise interventions.
Beyond determining the precise effects of certain diet and exercise
treatments on acute cognitive function, it is also important to establish achievable
diet and exercise guidelines to improve the cognitive health of the target population
of aging adults. Currently, only a minority of Americans meet national
recommendations for diet and exercise. For example, less than 25% of the
population of the United States reported achieving the recommended ﬁve servings
of fruit and vegetables per day (2007). Physical activity is also limited in
Americans, with the Centers for Disease Control and Prevention estimating that
about 74% of adults in the United States do not meet the recommendation for 30
minutes of moderate PA most days of the week (2003). With these statistics, it is
important to attempt to develop realistic and feasible guidelines rather than
prescribe stringent dietary constraints and difﬁcult quantities of exercise.
Developing such guidelines would require further studies whose treatments
involved ordinary types and quantities of foods, and achievable exercise. For
example, while it is exciting to discover that cycle ergometry exercise at 90% of

anaerobic threshold led to improvements in multiple executive function tasks

106

(Cordova, Silva et al. 2009), it may not be realistic to expect that the general
population would incorporate such sessions into their typical lifestyles. Also
important is to conduct studies in this particular population of healthy aging adults

because studying other populations lowers the external validity of the study.

107

Appendices

Appendix 1: Mini-Mental State Examination (MMSE)

For explanation and scoring, please see Table 4.

 

 

 

 

Subject Examiner
Date MMSE SCORE
Maximum Score
Orientation
5 ( ) What is the (year) (season) (date)
(day) (month)?
5 ( ) Where are we (state) (country)
(town) (campus) (building)?
Registration
3 ( ) Name 3 objects: 1 second to say

each. Then ask the patient

all 3 after you have said them.
Give 1 point for each correct
answer.

Then repeat them until he/she
Ieams all 3. Count trials and
record.

Trials

 

Attention and Calculation

Serial 7’s. 1 point for each
5 ( ) correct answer. Stop after 5

answers.

Alternatively spell “world”

backward.

Recall

3 ( ) Ask for the 3 objects repeated
above. Give 1 point for each
correct answer.

Language
2 ( ) Name a pencil and watch.
1 ( ) Repeat the following “No ifs,

ands, or buts”

108

Follow a 3-stage command:
“Take a paper in your hand, fold
it in half, and put it on the ﬂoor.”

Read and obey the following:
CLOSE YOUR EYES
Write a sentence.

Copy the design shown.

109

Appendix 2: St. Louis Mental Status Examination (SLUMS)

For explanation and scoring, please see Table 4.

VAMC
SLUMS Examination (Tariq, Tumosa et al. 2006)
Questions about this assessment tool? Email aging@slu.edu
Name Age

Is the patient alert? Level of education

 

(1) 1. What day of the week is it?

(1) 2. What is the year?

(1) 3. What state are we in?

4. Please remember these ﬁve objects. I will ask you what they are later.

Apple pen tie house. car

5. You have $100 and you go to the store and buy a dozen apples for $3 and a
tricycle for $20.

( 1) How much did you spend?

(2) How much do you have left?

6. Please name as many animals as you can in one minute

(0) 04 animals (1) 59 animals (2)10-14 animals (3)15+ animals
7. What were the ﬁve objects I asked you to remember? 1 point for each one
correct.

8. I am going to give you a series of number and I would like you to give them to
me backwards. For example, if I say 42, you would say 24.

(0)87 (1)649 (1)8537

110

9. Th
else
12le
llill
1111
HIV
1].]
goin

llll l

9. This is a clock face. Please put in the hour markers and the time at ten minutes to
eleven o-clock.
(2) Hour markers okay

(2)Time correct

 

 

(l) 10. Please place an X in the triangle.

(1) Which of the above ﬁgures is largest?

 

 

 

 

 

 

11. I am going to tell you a story. Please listen carefully because afterwards, I’m
going to ask you some questions about it.

Jill was a very successful stockbroker. She made a lot of money on the stock

market. She then met Jack, a devastatingly handsome man. She married him and had
three children. They lived in Chicago. She then stopped work and stayed at home to
bring up her children. When they were teenagers, she went back to work. She and
Jack lived happily ever after.

(2) What was the female’s name? (2) What work did she do?

(2) When did she go back to work? (2) What state did she live in?

TOTAL SCORE

111

Appendix 3: Informed Consent

INFORMED CONSENT
Effect of exercise and nutrition on cognitive function in aging
Depts. Radiology, Physiology and Osteopathic Manipulative Medicine
Michigan State University

1. You are being asked to participate in a study on the effects of exercise and
nutrition on cognitive function (thinking and cognition). The purpose is to
examine changes in cognition function and brain activation following one
period of exercise (30 minutes) and following meals with various nutrient
compositions.

2. Your participation in this study will require a minimum of 4 laboratory
visits. The sessions will last between 2 and 5 hours. You will be required to
walk on a treadmill for 30 minutes at a sub-maximal exercise intensity using
a self-selected walking speed. You will also be required to consume a meal
with various nutrient composition. You will have blood drawn for measures
of cholesterol, triglycerides, lipoproteins, glucose and insulin. You will also
have magnetic resonance imaging (MRI) to take pictures of your brain while
performing cognitive tasks. MRI is a safe and noninvasive instrument that is
often used for examining soft tissue like your organs, cartilage, ligaments and
brain. You will lie supine on the MRI table and watch a computer monitor for
instructions during the MRI. The MRI will take approximately 30-45
minutes. You will also perform a battery of cognitive tests on a computer in
the lab. You will receive the results from your blood tests (fasting glucose
and insulin, cholesterol and lipids) and the results from the cognitive
performance tests. You will also be compensated $25 per visit for study
participation.

3. The only known risks of MRI are the possibility that loose metallic objects
might be attracted into the magnet, or that metallic prostheses or pacemakers
might be affected by the magnet. Therefore, you should remove all metal
objects from your person; you should not participate if you have a pacemaker
or other implanted metallic device. You should not participate if you have an
unstable heart, metabolic, neurological disease, or if you have peripheral
arterial disease. For the MRI test, you might experience claustrophobia or
discomfort from lying in the same position for 30-45 minutes. We will
provide you with cushions and pillows to increase your comfort during the
MRI. The machine is also equipped with mirrors and a fan which may make
you feel more comfortable. In addition, you can communicate with the
researchers at any time through a microphone inside the machine. However,
you may not want to participate if you have extreme claustrophobia.

112

Although there are not known risks of MRI exams, the possibility that there
might be unknown risks cannot be ruled out. Because the imaging process
produces loud pinging noises, you will be provided with earplugs to wear
during the experiment.

4. You are free to discontinue your participation in the study at any time, and
for any reason, including feelings of claustrophobia in the MRI machine,
discomfort during the test procedures, etc. You will not be charged or billed
for any of the procedures or laboratory tests associated with this study.

5. Any information you provide, and all data collected, will be treated in
conﬁdence. Your privacy will be protected to the maximum extent allowable
by law.

6. The results of this study will be made available to you at your request.
Furthermore, on your request you can receive an additional explanation
during the study, or after your participation is completed. In that case, you
will contact Dr. Jill Slade, phone 355-0120, x351 or Dr. Joe Carlson, 355-
0120, x346. You may also contact Jill Slade or Joe Carlson by mail (184
Radiology Building, Michigan State University, East Lansing, MI 48824) or
by email_(jslade@msu.edu, jjc@radiology.msu.edu). If you have questions or
concerns about your rights as a research participant, please feel free to contact .
Peter Vasilenko, Ph.D., Director of the Human Subject Protection Programs
at Michigan State University by phone: (517) 355-2180, fax: (517) 432-4503,
email: ucrihs@msu.edu, or regular mail: 202 Olds Hall, East Lansing, MI
48824

7. Your participation in this study does not guarantee any beneﬁcial result to
you. If the MRI data collected suggests a need for further study, you will be
referred to an appropriate physician.

8. If you are injured as a result of your participation in this research project,
Michigan State University will assist you in obtaining emergency care, if
necessary, for your research related injuries. If you have insurance for
medical care, your insurance carrier will be billed in the ordinary manner. As
with any medical insurance, any costs that are not covered or in excess of
what are paid by your insurance, including deductibles, will be your
responsibility. Financial compensation for lost wages, disability, pain or
discomfort if not available. This does not mean that you are giving up any
legal rights you may have. You may contact Jill Slade 517-355-0120, x3510r
Joe Carlson 355-0120, x346 with any questions.

113

Your signature below indicates your voluntary agreement to participate in this
study.

 

 

Participant’s Signature Date

 

 

Witness’ Signature Date

114

Appendix 4: Patient authorization for disclosure of health information for research

Patient Name:

 

Address:

 

Date of Birth:

 

I AUTHORIZE THE DISCLOSURE OF MY HEALTH INFORMATION
FROM: TO: Jill Slade. PhD

184 Radiology

Department of Radiology, Michigan State University

East Lansing, MI 48824

(517) 355-0120, x. 351

DESCRIPTION OF INFORMATION TO BE DISCLOSED (Select one of the
following):

_ALL information contained in my medical record OR

_X__ONLY disclose the following information: information related to
cardiovascular, peripheral vascular, or neural conditions/disorders and information
on surgeries that may have involved medical implants

RESEARCH STUDY FOR THIS DISCLOSURE:

Title of Study: Effect of Exercise and nutrition on cognitive function in aging
Name of research leader: Jill Slade, PhD.

Afﬁliation of Researcher: Assistant Professor, Department of Radiology

IRB# i026479#06-1080 Name of IRB: MSU BIRB

EXPIRATION: expires at the end of the research study

REVOCATION, REFUSAL, REDISCLOSURE

115

Appendix 5: Permission letter to physicians for exercise

Month Day, Year
John Doe, MD
1234 Some Street
City, MI 12345
Fax: 123-456-1234
Tele: 123-456-1235

Dear Dr. Doe

 

Your patient, Jane Smith (DOB) has volunteered to participate in a study at Michigan
State University. The study focuses on the inﬂuence of nutrition and exercise on
cognitive function.

This study has been approved by the Institutional Review Board at Michigan State
University (please see the enclosed consent form).

For this study, the research participant will walk on a treadmill at a self-selected pace
for 30 minutes at a moderate, sub-maximal exercise intensity (50% of heart rate
reserve) under the supervision of an ACLS certiﬁed clinician. Ms. Smith needs to
obtain medical clearance from you in order to participate in this research.

At your earliest convenience, please return this form via fax (517-432—2849) or mail. If
you have any questions, please contact Jill Slade, PhD, at (517) 355-0120 ext. 351 or
Joe Carlson, PhD, RD, at 355-0120 ext. 346.

Sincerely,

Jill M, Slade, Ph.D.
Jane Smith may participate in the research study entitled “The effect of exercise and
nutrition on cognitive function in aging.”

Approve Decline

Date

 

Physician Signature

 

John Doe, MD

116

Appendix 6: Magnetic Materials Safety Questions

Magnetic Materials Safety Questions:
Please explain to the subject that the following questions are standard
precautionary questions related to MRI procedure. A MRI involves a powerful
magnetic ﬁeld that will cause problems for people with any metal within their

bodies.

1.) Do you have a neurostimulator (nerve stimulator)? Yes No
2.) Do you have a heart Pacemaker or implanted deﬁbrillator? Yes No-
3.) Do you have an ear implant? Yes No
4.) Do you have any metal fragments in your head, eyes or skin? Yes No
5.) Do you wear any patches that deliver medications through the skin? Yes No

6.) Do you have any implants of any kind, metal joints, rods, plates, pins, screws,

 

 

 

nails, clips, stents?Yes No
Explain:
7.) Have you ever worked as a machinist, metal worker or welder? Yes No
8.) Have you had any surgeries? If yes what type and when? Yes No
9.) Do you have problems with claustrophobia? Yes No

10.) Do you have epilepsy or seizure disorder? Yes No

117

Appendix 7: Geriatric Depression Scale

MOOD SCALE
(short form)

Choose the best answer for how you have felt over the past week:
1. Are you basically satisﬁed with your life? YES / NO

2. Have you dropped many of your activities and interests? YES / NO

00

. Do you feel that your life is empty? YES / NO

4. Do you often get bored? YES / N 0

L11

. Are you in good spirits most of the time? YES / NO

0

. Are you afraid that something bad is going to happen to you? YES / NO
7. Do you feel happy most of the time? YES / NO

8. Do you often feel helpless? YES / NO

9. Do you prefer to stay at home, rather than going out and doing new
things? YES / N O

10. Do you feel you have more problems with memory than most? YES /
NO

11. Do you think it is wonderful to be alive now? YES / NO

1.2. Do you feel pretty worthless the way you are now? YES / NO

13. Do you feel full of energy? YES /NO

14. Do you feel that your situation is hopeless? YES / N O

15. Do you think that most people are better off than you are? YES / NO

Answers in bold indicate depression. Although differing sensitivities and
speciﬁcities have been obtained across studies, for clinical purposes a score > 5
points is suggestive of depression and should warrant a follow-up interview. Scores
> 10 are almost always depression.

118

Appendix 8: Exercise documentation form

#8

M

Date

Pre-exercise protocol followed (consent, mods etc.)

Resting HR 50% HRR 65% HRR

RHR: THR

DOB

Age Gender

 

[(MHR-RHR) x 50% 1 +

 

Resting Blood Pressure, Heart Rate & ECG

 

 

Sitting Standing
Blood Pressure / mm Hg / mm
Hg
Heart Rate (ECG) beats/min beats/min

Exercise Warm-up (3-5 minj- BP

 

/ mm Hg; ECG HR

 

Exercise BP & HR Q RPE Assess every 5 min u_nless SSs; print ECG every 5 min

ECG time min BP
MPH %Incline

 

ECG time min BP
MPH %Incline

ECG time min BP
MPH %Incline

 

ECG time min BP
MPH %Incline

 

ECG time min BP
MPH %Incline

 

ECG time min BP
MPH %Incline

 

Exercise duration-

/

 

Cool down time

Post Exercise BP & HR

/

I

/

/

/

minutes; Average HR

HR _ RPE—
HR _ RPE—
HR _ RPE—
HR _ RPE—
HR __ RPE____
HR RPE

beat] min

 

MPH %Incline

119

Immediate Post- BP / HR beat lmin
ECG

5 min post BP I HR beat lmin
ECG

10 min post BP / HR beat Imin
ECG

 

 

 

Comments/ Summary-

120

Appendix 9: The Cognitive Failures Questionnaire

Please see Table 4 for explanation.

The Cognitive Failures Questionnaire

Modified from (Broadbent, Cooper, FitzGerald & Parkes, 1982)

The following has been modified from the original questionnaire:

A column has been added for the subject to decide if the question/item has
gotten worse or occurs more frequently with age. The subject will answer
“Yes” or “No”.

Broadbent, D.E., Cooper, P.F., FitzGerald, P., & Parkes, K.R. (1982). The
Cognitive Failures Questionnaire (CFO) and its correlates. British Journal of
Clinical Psychology, 21, 1-16.

121

The following questions are about minor mistakes which everyone makes
from time to time, but some of which happen more often than others. We
want to know how often these things have happened to your in the past 6
months. Please circle the appropriate number. Also, please circle whether,
in your opinion, this has gotten worse or occurs more frequently with age.

Answer for each question:

Very often Quite often Occasionally Very rarely Never
4 3 2 1 0
Worse with age? Yes No

1. Do you read something and find you haven’t been thinking
about it and must read it again?

2. Do you find you forget why you went from one part of the
house to the other?

3. Do you fail to notice signposts on the road?

4. Do you find you confuse right and left when giving
directions?

5. Do you bump into people?

6. Do you find you forget whether you’ve turned off a light or
a fire or locked the door?

7. Do you fail to listen to people’s names when you are
meeting them?

8. Do you say something and realize afterwards that it might
be taken as insulting?

122

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

Do you fail to hear people speaking to you when you are
doing something else?

Do you lose your temper and regret it?
Do you leave important letters unanswered for days?

Do you find you forget which way to turn on a road you
know well but rarely use?

Do you fail to see what you want in a supermarket
(although it’s there)?

Do you find yourself suddenly wondering whether you’ve
used a word correctly?

Do you have trouble making up your mind?
Do you find you forget appointments?

Do you forget where you put something like a newspaper
or a book?

Do you find you accidentally throw away the thing you
want and keep what you meant to throw away — as in the
example of throwing away the matchbox and putting the
used match in your pocket?

Do you daydream when you ought to be listening to
something?
Do you find you forget people’s names?

Do you start doing one thing at home and get distracted
into doing something else (unintentionally)?

Do you find you can’t quite remember something although
it’s “on the tip of your tongue”?

123

23.

24.

25.

Do you find you forget what you came to the shops to
buy?

Do you drop things?

Do you find you can’t think of anything to say?

124

Appendix 10: Instructions for treatment visits

These instructions were for participants to follow before each of their three
treatment visits and during the duration of the study.

Instructions for Visits 24.

Please come in after an overnight fast (at least 10 hours).

Do not drink any coffee, tea, or other beverages in the morning except water.
During the duration of the study, please eat what you would consider to be your
normal diet.

If you regularly consume alcoholic beverages, please restrict your intake as
described. Please limit your alcohol consumption the evening before your visit to no
more than 1 drink for females or 2 drinks for males. 1 drink is the equivalent of a
12 oz. can of beer or a 4 02. glass of wine.

Please try to follow a regular sleep pattern for the duration of the study.

Do not take any aspirin the morning of your visit.

Please avoid exercise the day before your visit and the morning of your visit.

Wear comfortable walking clothes and shoes for your visit.

Please do not wear eye shadow.

Please bring in your diet and/or physical activity log and monitor with you for your
visit.

Feel free to bring books, crossword puzzles, or music with you; you will have about

an hour of free time in the lab.

Date Time
Visit 2
Visit 3

Visit 4

Please contact Natalie Stein at 517-355-0120, x242 with any questions about your

appointment!

125

4.1

5.1

U.)

 

Appendix 11: Telephone Screening Questionnaire

Date Screened Screened By

 

Status

 

1.) Name Phone #

 

2.) Mailing Address / email:

3.) DOB Sex M/ F Height Weight
BMI
4.) Are you right or left handed? RIGHT / LEFT

5.) How many years of schooling?

 

6.) Are you worried about your cognition (your ability to think, remember, make
decisions?) Yes / No

7.) Current Health Status in general

 

 

8.) Do you have any physical limitations? Yes / No If yes, list:

 

9.) Do you use an assistive device for walking? Yes / No
10.) Do you have joint pain/arthritis? Yes/

No

 

11.) Do you have any allergic reactions? Yes / No

 

12.) Are you anemic? Yes / No

13.) Have you had a stroke? Yes / No

126

Have you had a mini stroke or transient ischemia attack? Yes / No
14.) Do you have unstable/recent cardiovascular (including unstable

ischemia, heart failure, myocardial uncontrolled arrhythmia, and aortic

stenosis) or unstable metabolic disease (hypertension, diabetes, thyroid disease)?

Yes / No

Details:

 

15.) Do you have chronic heart failure or hypertrophic cardiomyopathy? Yes /
No

16.) Do you /have you had an aneurysm? Yes / No

17.) Do you have severe arterial hypertension (systolic BP > 200mmHg, diastolic
pressure >110mm Hg? Yes / No

18.) Do you have high blood pressure? Yes / No

19.) What your most recent BP? mmHg, Date: Don’t
remember

20.) Do you have acute pulmonary embolism? Yes / No

21.) Do you have a chronic infections disease (AIDS, mononucleolus, hepatitis)?
Yes / No

22.) Do you have cardiovascular disease? Yes / No

 

23.) Have you had a recent heart attack? Yes / No Date:

 

24.) Do you have peripheral arterial disease or peripheral vascular disease? Yes

/No

127

25.) Do you have ﬁbromyalgia? Yes / No
26.) Do you have chronic fatigue syndrome? Yes / No
27.) Have you been diagnosed with dementia? Yes / N o
28.) Have you been diagnosed with moderate or severe depression? Yes / No
29.) Have you been diagnosed with schizophrenia or bipolar disorder or do you
have hallucinations? Yes / No
30.) Do you have diabetes? Yes / No Type I / Type II
If yes, how long have you been diagnosed?

If yes, do you take insulin? Yes / No

 

31.) Do you have any other chronic medical conditions? Yes / No

List:

 

32.) Do you have a pacemaker? Yes / No
33.) Do you have an implanted medical stent? Yes / No

If yes, where is the stent and what kind is it (look on your medical card)?

 

34.) Do you have any other implanted metal? E. g. neurostimulator, pins, plates,
rod, screws? Yes / No If yes, where is the metal

located?

 

35.) Do you smoke? Yes / No

If yes, for how long?

 

How much? ppw / ppd
36.) Do you chew tobacco? Yes / No

If yes, for how long? How much? per day

128

37.) Are you a past smoker? Yes / No
38.) Do you wear dentures? Yes / No
If yes, are they: removable / partial (bridge/crown) / ﬁxed

What is the brand?

 

39.) Do you wear a hearing aid? Yes / No
40.) Do you wear corrective lenses? Yes / No

If yes, what kind of correction?

 

41.) Are you taking a beta blocker? Yes / No If yes, circle the med:

Betapace (sotalol), Blocadren (timolol), Brevibloc (esmolol), Cartrol (carteolol),
Coreg (carvedilol), Corgard (nadolol), Inderal (propranolol), Inderal-LA
(propranolol), Kerlone (betaxolol), Levatol (penbutolol), Lopressor (metoprolol),
Normodyne (labetalol), Sectral (acebutolol), Tenormin (atenolol), Toprol-XL
(metoprolol), Trandate (labetalol), Visken (pindolol), Zebeta (bisoprolol),
Alprenolol,Corgard (Naldolol), Nebilet (Nebivolol)

42.) What other medications are you taking?

 

 

 

43.) Are you taking any of the following dietary supplements? (Circle those that

apply, and note duration and frequency of use)

Gingko Biloba DHA (Docosahexaenoic acid, ﬁsh oils) Vitamin E
gotoKola (Centella Asia Tica) Ginseng
Notes:

 

44.) What (other) supplements/vitamins are you taking and how often?

129

 

45.) Are you allergic to any foods or beverages? Yes / No

If yes, List:

 

46.) Do you currently exercise (walking included)? Yes / No

Mode Duration (time/day):

 

Frequency (days/wk): Other details:

 

47.) Has this level of physical activity been consistent over the last 3 months?
Yes/No

48.) Is fatigue a problem for you? Yes / No Leg fatigue? Yes / No

49.) Can you walk continuously for 30 minutes? Yes / No / Do not know

50.) When was the last time you walked continuously for 30 minutes?

 

51.) Do you have difﬁculty breathing or shortness of breath when walking? Yes
/ No
52.) Have you ever walked on a treadmill? Yes / No
53.) How many alcoholic drinks do you have per week? Per day
9

54.) Do you have any dietary restrictions? Yes / No. If yes, please list:

 

55.) What is the name and phone number of your primary physician?

 

130

56.) When was your last doctor’s visit?

 

57.) When was your last physical?

 

58.) Do you have a cardiologist? Yes / No

If yes, provide contact info

 

59.) Have you ever had an MRI? Yes / No If yes, when?

 

[If the person clearly does not qualify at this point, ask the following:]

60.) Would you be interested in other research studies at MSU? Yes/ No

If yes, may we keep your contact information on ﬁle and contact you about future
studies? Yes / No

[If the person may qualify at this point, ask the following:]

When is a good time to reach you (day/time)?

 

131

Appendix 12: Study summary report to participants

Month Day, Year
Jane Smith

Street Address
City, MI, zip code

Dear Jane,

Thank you for participating in our study on the effects of nutrition and exercise on
cognition supported by the Michigan State University Radiology Department! We
know this report is long overdue and we thank you for your patience, and more
importantly for your participation. Enclosed, please ﬁnd a summary of your results
from our study. The ﬁrst two pages show your performance on the computerized
CogState battery compared to the study’s average (average age 70 years). The next
page of the report shows your fasting blood measures. Also included is an analysis
of your regular nutrient intake based on the three days of food records that you
completed.

We have selected certain key nutrients to include in this report because they are
important for your health. Eating an appropriate number of calories is an integral
part of maintaining an appropriate weight, which can help decrease the risk of
chronic diseases like cardiovascular disease and diabetes. Diets low in saturated fat,
sodium, and cholesterol, and high in ﬁber, unsaturated fat, potassium, and fruits and
vegetables are likely to decrease the risk of chronic diseases and may slow the aging
process in the brain. Vitamins A and C are healthful antioxidants that are found in
many fruits and vegetables. There is also an estimate of your daily intake of the
important nutrients folate, iron, and calcium, whose intakes are sometimes
inadequate in our diets. The ﬁnal page of the nutrient summary gives an indication
of the variety of foods you ate by showing the average number of servings that you
ate from each food group. All of your nutrient intakes are compared to the
recommended levels.

Our main study focus was to determine if a single meal or bout of exercise
inﬂuenced cognitive function in older adults. In this study that you participated in,
we did not ﬁnd an improvement in cognitive function with a Nutrient Dense Diet or
exercise compared to a Standard American Diet. We are still analyzing the brain
MRI data, but we also have not found an effect of the meal or exercise on brain
activity during the task that you completed in the MRI scanner. We were
particularly interested in your executive processing because aging is sometimes
associated with a loss of the ability to focus on a speciﬁc task. For example, in one
of the tasks you completed, you were asked to identify the direction of the center
arrow while ignoring distracters (the directions of the surrounding arrows). This is

132

an example of the inhibitory process, which is more difﬁcult as we age. Other
research groups have shown that chronic or regular exercise does improve cognitive
function and that certain nutrients can also improve cognition. Our research suggests
that consistent lifestyle choices of a nutrient dense diet and exercise program are
required for positive adaptations to occur. In other words, keep the ﬁber, fruits and
vegetables, healthy fats, and daily exercise in your lifestyle - they are still important
factors when it comes to brain health!

Thank you again for your participation in our study. We greatly appreciate your
generous help! Please review the material enclosed and feel free to call Natalie Stein
if you have any questions.

Sincerely,

The Exercise and Nutrition Laboratory

Department of Radiology

517-355-0120, x242

Jill M. Slade, PhD, Joseph J. Carlson, PhD, RD, Natalie J. Stein, BS

133

Name:

 

The following tables summarize the scores from the battery of cognitive tests
preformed on the computer during the study. For each of the tests and treatments,
your score is an average from all your visits. The last column lists the average from
the entire group that completed the study. Your accuracy and speed in performing
each task is listed in the tables below. There is also a description of the test.

134

Table 8: Participant report for accuracy

Accuracy is the proportion of correct responses given during the task. 100% would
mean that you did not make any errors and 50% would mean that you made a

mistake 1/2 the time. Of course, higher accuracy is better.

ACCURACY Cognitive area

Task

description Score Average
Continuous In what locations

Your Group

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Paired Associate Memory and do these pictures 67%
Learning Spatial Memory belory?
Have you seen
this word before 94%
One Word Learn Verbal Memory in this task?
Identification Visual Attention Is the card red? 99%
Have you seen
Visual Learning this card before 70%
One Card Learn and Memory in this task?
AttentionNVorking Is the previous
One Back Memory card the same? 97%
Has a card
touched a white 900/
Monitoring Attention line? 0
Is the next card
red? (Then, 81%
remember the
Executive sequence of
Prediction Function cards)
Continuous In what locations
Paired Associate Memory and did these 72%
Learning Delayed Spatial Memory- pictures belong?
Recall Delayed Recall (repeat task 1)
Have you seen
this word 90%
repeatedly
One Word Learn Long Term before? (repeat
Delayed Recall MemorL task 2)

 

 

 

 

 

 

 

 

135

Table 9: Participant report for speed

Speed is the length of time it took to respond to each stimulus (e. g. card, picture or
word). The time is reported in milliseconds (msec). The lower number means a
faster or better time.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

SPEED Cognitive area Task Your Group
description Score Average
Continuous
Paired In what locations 2897 msec
Assocrate Memory and do these pictures
Learning Spatial Memory belong?
Have you seen
One Word this word before in 827 msec
Learn Verbal Memory this task?
Identification Visual Attention Is the card red? 540 msec
Have you seen
One Card Visual Learning this card before in 1016 msec
Learn and Memory this task?
AttentionNVorking Is the previous
One Back Memory card the same? 770 msec
Has a card
touched a white 442 msec
Monitoring Attention line?
Is the next card
red? (Then, i673 msec
remember the
Executive sequence of
Prediction Function cards)
Continuous
Paired In what locations 2902 msec
Assoc:ate Memory and dld these pictures
Learning Spatial Memory- belong? (repeat
Delayed Recall Delayed Recall task 1)
Have you seen
One Word this word 921 msec
Learn Delayed Long Term repeatedly before?
Recall Memory (repeat task 2)

 

 

 

 

 

 

 

136

 

Table 10: Participant report for bloodwork

These are the values from your bloodwork. These values are from the fasting blood
draw taken in the morning before you ate breakfast. For each measurement, your
individual values are listed next to the normal range.

Your Value Normal Range

 

 

 

 

 

 

 

 

 

 

Total cholesterol
(mg/dl) 1 25-200
HDL (mg/dl) 30-60
LDL (mg/dl) 60-130
Triglycerides (mg/dl) 1 0-1 50
Glucose (mg/dl) 80-1 10
lnsulin (lU/ml) 6.0-29.0
Total cholesterol/HDL R'Sk Gmp Me" w°men
. .5X 3.4 3.3
ratio
Average 5 4,4
2X 9.6 7
3X 23 1 1

 

 

 

 

 

 

 

137

Appendix 13: Sample letter to physicians after exercise
Month Day, Year

John Doe, MD
1234 Some Street
City, MI 12345
Fax: 123-456-1234
Tele: 123456-1235

Dear Dr. Doe,

Your patient, Jane Smith (DOB) recently volunteered as a research participant in a
study at Michigan State University. The study focused on the inﬂuence of nutrition
and exercise on cognitive function. For this study, the research participant walked on a
treadmill at a self-selected pace for 30 minutes at a moderate, sub-maximal exercise
intensity (50% of heart rate reserve) under the supervision of an ACLS certiﬁed
clinician. During the duration of the exercise, the participant’s ECG and blood
pressure were monitored.

Enclosed, please ﬁnd the ECG and blood pressure reports from the resting state,
warmup, 30 minutes of exercise, and post-exercise.

If you have any questions, please contact Jill Slade, PhD, at (517) 355-0120 ext. 351 or
Joe Carlson, PhD, RD, at 355-0120 ext. 346.

Sincerely,

. 11' .
.3. ~ V ,.
batik“: " i, 4&ng
It

I

\r‘
Jill M, Slade, Ph.D.

138

Resting and sub-maximal exercise EKG

Name

 

Date

 

DOB

 

Medications:

 

Participant’s physician
Address

 

 

 

 

Phone

 

Fax

 

Supervising physician
Kiran Sarikonda, MI)
Pager 517-432-3411

Description of Exercise: 30 minutes of treadmill walking at a
sub-maximal intensity (SO-65% heart rate reserve) plus 5

minutes warm-up and cooldown.

 

Kiran Sarikonda, MD

139

Appendix 14: National recommendations compared to participant intake and

national average intake.

These tables compare select nutrients that were highlighted in this study. The

recommended intakes are Dietary Reference Intakes from the Institute of Medicine.
Subject intake was calculated based on a three-day food record, as described in the

text and reported in Table 5. Average national intake was based on NHAN ES
(USDA 2008) and National Cancer Institute (NCI 2009) data.

Table 11: Nutrient comparisons for females

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

FEMALES
Recommended Subject National
intake intake AveragL

Ag (years) 51-70 67.9 :l: 3.5 60-69

kilocalories 1978 1669 i 365 1598

% kilocalories from fat 20-35** 32 1 4 36

% kilocalories from saturated

fat * 9 :l: 2 12

n-3 fatty acids, g 0.9 :l: 0.8

% kilocalories from protein 10-35 ** 17 :l: 5 16

% kilocalories from

carbohydrate 45-65** 51 :t 7 49

dietary ﬁbeng 21 19 1 4 14.3

servings of fruit and

vegetables 2.8 4.4 :t 2.4 2.8

vitamin C, mg 75 136 :t 73 79.7

vitamin E, mg 15 11 t 9 6.5

potassium, mg 3500 2327 i 613 2376

folate, dietary folate

equivalents, pg 200 374 :l: 203 449

vitamin B12,p.g 1.5 6.3 :l: 4.6 4.69

 

 

 

 

 

 

* Not necessary for prevention of chronic disease, so there is no recommendation

for intake.

**Amounts given in Acceptable Macronutrient Distribution Ranges.

140

Table 12: Nutrient comparisons for males

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MALES
Recommended Subject National
intake intake Average_
_Age (years) 2 70 75 1 4.6 2 7O
kilocalories 2054 2082 1 466 1984
% kilocalories from fat 20-35%** 33 1 3 35
% kilocalories from saturated
fat * 11 1 l 12
n-3 fatty acids, L 0.8 1 0.5
% kilocalories from protein 10-35%** 14 1 2 16
% kilocalories from
carbohydrate 45-65%** 54 1 3 48
dietary fiber,g 3O 27 1 7 16.8
servinggof fruit and vggetables 3.1 4.1 1 2.3 1.7
vitamin C, mg 75 82 1 29 97.4
vitamin E£g 15 6 1 3 7
potassium, mg 3500 2059 1 344 2863
folate, dietary folate equivalents,
_g 200 644 1 278 590
vitamin B12,gg 1.5 3.4 1 1.2 6.09

 

 

 

* Not necessary for prevention of chronic disease, so there is no recommendation

for intake.

**Amounts given in Acceptable Macronutrient Distribution Ranges.

141

Appendix 15: Correlations between habitual physical activity and selected
study outcome measures

Weekly energy expenditure was estimated using GTlM Activity Monitor
accelerometers as described in the Materials and Methods section in Chapter II of
the text. These ﬁgures show correlations of individual weekly physical activity per
body weight in kilograms with key study outcome measures averaged across all
three treatment conditions (SB, NB, and NB + AE). kcal = kilocalories, IFG =
inferior frontal gyrus, MFG = middle frontal gyrus, PCG = precentral gyrus, BA6 =
Brodmann’s Area 6, C = congruent condition of ﬂanker task, IC = incongruent
condition of ﬂanker task, SI = signal intensity of BOLD response, R = right, A =
anterior, S = superior. R2 = goodness of ﬁt, calculated from Pearson’s correlation
coefﬁcient.

Figure 11: Correlations between physical activity and study outcomes

A. Physical activity versus maximum percent change in signal intensity in the brain
BOLD response during the incongruent condition of the ﬂanker compared to
baseline in a 849 mm3 cluster at the right inferior frontal gyrus, middle frontal
gyms, precentral gyrus (centroid at R39,A3,S32).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.2
u 2 _
2 0.15 e R '0’15
0:
m 0
4 g 0.1
s° -'-' o
g E 005 4 .
8. E . o 9
§ ._ o . . 9 , . . ¢ .
g a) 10 20 30 40 so 60
8 -o.05 ’
no

-o.1 . . .

physncal actmty, kcal/kg/wk

 

142

Figure 11 (Continued)

B. Physical activity versus maximum percent change in signal in the brain BOLD

response during the incongruent condition of the ﬂanker compared to baseline in a
437 mm3 cluster at the right middle frontal gyrus, Brodmann’s Area 6 (centroid at

R29, P1, SS4).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.08 2
o ‘ R =0.50
i: 0.06 Q
1-1 0.
m 0.04
<1 8
e =
R g 0.02 g 9
$3“ 9
‘7' I: 0 c .
a E r T T . r m
E E (I) 10 20 3o 40 50 60
-0.02
a
u—l
O -0.04
a:
O
-0.06 . . .
physrcal actrvnty, kcal/kg/wk

 

 

143

Figure 11 (Continued)

C. Physical activity versus difference in response time, in milliseconds, between the
incongruent and congruent conditions in the ﬂanker task.

 

350

 

R2 = 0.04

 

 

300 O

 

 

250

 

200

 

 

150 ‘ .
100 3‘ . ’ f

50 1

 

 

 

Difference in response time,
msec
O

 

0 I I I I I l

 

physical activity, kcal/kg/wk

 

 

D. Physical activity versus response time, in milliseconds, on the prediction task of
the CogState® battery.

 

 

 

 

 

 

 

 

 

 

 

 

3000
O 2 _
8 2500 R — 0.0002
E 2000 o .
g ’ ’ .9
‘4': 1500 9. . ‘
E ’0
a 1000 v
8
a: 500
O I I I I I l
0 10 20 30 40 50 60
physical activity, kcal/kg/wk

 

 

144

Appendix 16: Flanker results
Table 13: Flanker results for participants with MRI data

 

 

 

 

 

 

 

 

 

 

 

 

Time g*100%
Acc C Time C Acc IC Time IC (IC-C)
109 1 70
SB 99% 751 msec 99% 860 msec msec 15%
119 1 59
NB 99% 771 msec 99% 890 msec msec 16%
119 1 77
NB + AB 99% 763 msec 99% 881 msec msec 16%
115
Average 99% 761 msec 99% 877 msec msec 16%

 

 

Results for the Flanker test (n=11; same subjects as included in MRI analysis). Acc
= accuracy. Time = average response time. SB = standard breakfast. ND = nutrient
dense breakfast. AB = aerobic exercise. C = congruent condition. IC =
incongruent condition. Values are mean or mean 1 SD.

145

Appendix 17: MRI BOLD percent signal change in congruent ﬂanker
condition in inferior frontal gyrus / middle frontal gyrus / precentral gyrus

Figure 12: BOLD percent signal change in IF G / MFG / PCG

 

 

 

 

 

Percent Signal Change

 

 

SB NB NB+AE

Treatment Condition

 

 

Values are mean 1 standard deviation for percent signal change in the brain BOLD
response for the congruent condition compared to baseline in a 849 mm3 cluster at
the right inferior frontal gyrus, middle frontal gyrus, precentral gyrus (centroid at
R39,A3,S32) (n=1 1). SB = standard breakfast, NB = nutrient dense breakfast, NB +
AE = nutrient dense breakfast plus aerobic exercise, R = right, A = anterior, S =
superior.

146

Appendix 18: MRI BOLD percent signal change in congruent ﬂanker
condition in middle frontal gyrus / Brodmann’s Area 6

Figure 13: BOLD percent signal change in MFG / BA6

91

w.
l
l
l
l
l
|
i
l
l
l
l
l
l
i
l
l
|

0.25 F 1

Percent Signal Change

 

SB NB NB+AE

Treatment Condition

 

 

Values are mean 1 standard deviation of percent signal change in the brain BOLD
response for the congruent condition compared to baseline in a 437 mm3 cluster at
the right middle frontal gyrus, Brodmann’s Area 6 (centroid at R29, P1, SS4)
(n=11). SB = standard breakfast, NB = nutrient dense breakfast, NB + AB = nutrient
dense breakfast plus aerobic exercise, R = right, A = anterior, S = superior.

147

Appendix 19: Impulse-response functions for individual participants
Figure 14: Individual impulse-response functions (A-K, pages 149-159)

These impulse-response functions show brain BOLD responses for each study
treatment (SB, NB, and NB + AE). Brain activation is shown as the percent signal
change in brain BOLD response from baseline during incongruent ﬂanker tasks for
each individual subject (n=11). The region shown is a 849 mm3 cluster at the right
inferior frontal gyrus, middle frontal gyrus, precentral gyrus (centroid at
R39,A3,S32) (n=11). SB = standard breakfast, NB = nutrient dense breakfast, NB +
AB = nutrient dense breakfast plus aerobic exercise, BOLD = blood oxygen level-
dependent, R = right, A = anterior, S = superior, TR = repetition time.

148

A. Subject 101

 

 

 

 

- - - Congruent

SB

lncongruent

4
Time, TR (TR = 2.5s)

 

 

 

 

 

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mag—.9 53.3:— Bani

 

 

NB

4
Time, TR (TR = 2.58)

 

 

 

 

 

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cup—5.9 36:35 .2.me

 

 

NB+AE

4
Time, TR (TR = 2.55)

 

 

 

 

 

4. 2 0 2
0 0 nw

as 6:283 Eon.—
owEEo 53:35 :3me

 

 

 

 

149

B. Subject 104

 

 

 

 

- - - Congruent

SB

lncongruent

Time, TR (TR = 2.5s)

 

 

 

 

 

 

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owns-.9 .3652: 3&5

 

 

I

 

4
Time, TR (TR = 2.58)

 

 

 

 

 

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magic bum—=55 .555

 

 

NB+AE

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es .2532 :5:
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150

c. Subject 105

 

 

- - - Congruent

SB

lncongruent

 

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7

= 2.58)

Time, TR (TR

 

 

 

 

4.202

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es Jam—emu: 59¢
3.35 £835 355

 

 

 

 

NB+AE

---
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4
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151

D. Subject 106

 

 

 

 

- - - Congruent

SB

— lncongruent

2.58)

Time, TR (TR

 

 

 

 

 

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0 0 ﬂ

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$559 56:35 _aw5m

 

 

NB

 

2.58)

Time, TR (TR

 

 

 

 

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NB+AE

 

4
Time, TR (TR = 2.58)

 

 

 

 

 

 

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152

E. Subject 107

 

 

 

 

- - - Congruent

SB

— lncongruent

4
Time, TR (TR = 2.58)

.l.

 

 

 

 

 

 

 

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5559 56:35 555

 

 

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5:36 56:35 555

 

 

NB+AE

4
Time, TR (TR = 2.58)

 

 

 

 

 

 

am» 65.85 59:
maﬁa—o 56:35 3:55

 

 

 

 

153

F. Subject 113

 

 

Signal intensity change
from baseline, %

SB - - - Congruent

—— lncongruent

 

 

 

 

Time, TR (TR = 2.58)

 

 

 

Signal intensity change
from baseline, %

0.4

0.2

 

 

 

 

Time, TR (TR = 2.58)

 

 

 

Signal intensity change
from baseline, %

0.4

0.2

NB+AE

 

 

 

Time, TR (TR = 2.58)

 

154

 

 

 

G. Subject 1 15

 

 

 

 

- - - Congruent

SB

—— lncongruent

‘—

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4
Time, TR (TR = 2.55)

 

 

 

 

 

95 65.82. :85
mug—.9 56:35 .855

 

 

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Time, TR (TR = 2.5s)

 

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155

H. Subject 116

 

 

 

 

- - - Congruent

SB

-— lncongruent

 

 

 

 

 

 

 

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156

1. Subject 121

 

 

 

 

- - - Congruent

SB

lncongruent

 

-

2.55)

7

Time, TR (TR

3

 

 

 

 

 

ck. .2583 88..
$52.9 33:35 _aswmm

 

 

NB

2.53)

Time, TR (TR

 

 

 

 

 

 

«NA. .2583 88m
cup—«:9 mam—~85 _anwmm

 

 

NB+AE

4
Time, TR (TR = 2.5s)

 

 

 

 

 

 

4.202

ck. 65.3.3 :5...
$559 mam—~85 _anwmm

 

 

 

 

157

J. Subject 122

 

 

 

 

- - - Congruent

SB

lncongruent

  

 

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4
Time, TR (TR = 2.58)

 

 

 

 

 

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4
Time, TR (TR = 2.5s)

 

 

 

 

 

 

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$525 ~35:35 _3:u_m

 

 

NB+AE

  

Time, TR (TR = 2.5s)

 

 

 

 

 

on. 6553: E9:
$35 35:85 3:5m

 

 

 

 

158

K. Subject 126

 

 

 

 

- - - Congruent

SB

lncongruent

2.53)

Time, TR (TR

 

 

 

 

 

4. 2 O 2

O 0 n.u
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I

 

 

 

 

 

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$5.5 35:85 3:»5

 

 

 

 

159

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