COMBINED AEROBIC AND COGNITIVE TRAINING EFFECTS ON COMPLEX BRAIN NETWORKS ...

76 Pages·2014·1.03 MB·English

COMBINED AEROBIC AND COGNITIVE TRAINING EFFECTS ON
COMPLEX BRAIN NETWORKS IN OLDER ADULTS

BY

RYAN S. HAMPTON

A Thesis Submitted to the Graduate Faculty of

WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES

in Partial Fulfillment of the Requirements

for the Degree of

MASTER OF ARTS

Psychology

May 2014

Winston-Salem, North Carolina

Approved By:

Dale Dagenbach, Ph.D., Advisor

Deborah L. Best, Ph.D., Chair

Janine M. Jennings, Ph.D.

Paul Laurienti, MD, Ph.D.
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ACKNOWLEDGMENTS

I must, of course, thank my advisor, Dale Dagenbach, for helping me along
through this exciting experience. He has helped me to think more critically and
introduced me to the wonderful world of network science. I’ve learned that while Dale
can be a man of few words, those words are often full of either wisdom or outright
hilarity; without both of these it would’ve been a much more difficult time.
I would also like to thank my Psychology committee members, Debbie Best and
Janine Jennings, for all of their invaluable input throughout this process. I know that I
have become twice the writer I was before thanks to your feedback and guidance.
My roommate Stephen Martin has been a great colleague and friend, helping
think through ideas and programming R scripts at the last minute to help with my thesis.
We’ve had some great conversations about Psychology and Science.
Finally, I would like to thank everyone at the Lab for Complex Brain Networks
for helping me enter this field of network science and teaching me the ways of the
complex. I must thank Paul Laurienti for constantly challenging me and guiding my
reasoning in complex problems as well as Robert Lyday for the countless times he turned
my half-thought ideas into reality, writing scripts and being patient with me from day one.
Without the help of all those mentioned, none of what follows would have been
possible.
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TABLE OF CONTENTS

LIST OF ILLUSTRATIONS AND TABLES..………………………………………..…iv
LIST OF ABBREVIATIONS……………………………………………………………..v
ABSTRACT……………………………………………………………………………..vii
INTRODUCTION………………………………………………………………………...1
METHOD………………………………………………………………………………..21
RESULTS………………………………………………………………………………..32
DISCUSSION……………………………………………………………………………47
REFERENCES…………………………………………………………………………..58
APPENDICES…………………………………………………………………………...66
CURRICULUM VITA..…………………………………………………………………67
iv

LIST OF ILLUSTRATIONS AND TABLES

TABLES Page
Table 1. Demographics and Pre-/Post-training Outcome Scores............................25
Table 2. Means and Pattern Ranks of Network Metrics..........................................36
Table 3. Qualifying Permutations for Relevant Contrasts.......................................39
Table 4. Regression Results for ROI Metrics on Outcome Tasks...........................43

FIGURES
Figure 1. Generation of a voxel-based network........................................................27
Figure 2. Degree distributions of individual networks.............................................29
Figure 3. Overlay of Ki images.................................................................................32
Figure 4. Mediation model for Theme Score............................................................45
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LIST OF ABBREVIATIONS

AAL Automated Anatomical Labeling Atlas
ACC Anterior Cingulate Cortex
ADAS Alzheimer’s Disease Assessment Scale
C Clustering Coefficient
CT Cognitive Training
E Local Efficiency
loc
E Global Efficiency
glob
fMRI Functional Magnetic Resonance Imaging
HAE Healthy Aging Education
HVLT Hopkins Verbal Learning Test
k Degree
Ki Average Degree
L Path Length
MMSE Mini-Mental State Examination
MNI Montréal Neurological Institute
MRI Magnetic Resonance Imaging
N Number of Nodes in a Network
PA Physical Aerobic
PACT Combined Physical Aerobic and Cognitive Training
ROI Region of Interest
RT Response Times
SHARP-P Seniors Health and Activity Research Program-Pilot
vi

SPA Statistical Pattern Analaysis
TBI Traumatic Brain Injury
VO Maximal Oxygen Uptake
2
WHM-III Wechsler Memory Scale 3rd Edition
vii

ABSTRACT

Network science has become a powerful tool with which we can model the human brain
as a complex network. Since the start of application of network science to the brain,
many advances have been made. Due to this development of new metrics and network
formation algorithms, the current study aimed to re-examine brain networks of a subset
from the Seniors Health and Activity Research Program Pilot (Burdette et al., 2010). In
addition, the present study sought to relate network metrics to participants’ performance
on cognitive and memory tasks. With past research implicating the anterior cingulate
cortex and the hippocampus in changes associated with training programs in older adults,
these were used as focal regions of interest. Of the original 73 participants, a subset of 29
participants were scanned using fMRI and had these images transformed into complex
networks based on time-series correlations. Metric comparisons showed non-significant
trends towards high-degree orientation in the training groups and especially those
involving physical activity. Regression of performance outcomes revealed a consistent
negative association between the local efficiency of the anterior cingulate cortex and of
the network and task outcomes. Implications for underlying group differences and
consideration of network metrics for the brain are discussed.
1

INTRODUCTION

Recent studies of human brain functioning have been informed by advances in the
field of network science that enable researchers to examine and characterize functional
connectivity across the entire brain. In this study, network science was applied to
functional magnetic resonance imaging data (fMRI) obtained from a sample of older
adults. These individuals were part of a study contrasting various cognitive and physical
activity interventions for cognitive aging. The present analyses have two goals: One is
to determine whether the different interventions had effects on functional connectivity
within the participants. The second goal is to examine the relationship between network
science metrics and cognitive behavioral performance irrespective of intervention.
Network Science
Network science is based in graph theory which describes and analyzes graphs of
information represented by nodes or vertices and links or edges. By applying graph
theory to real world systems one can characterize the connectivity of the system in a
theoretically grounded and relatively consistent manner. Network science already has
been applied widely from studies of the internet (Barabási, 2013) to an assortment of
social and biological systems (Girvan & Newman, 2002; Newman & Girvan, 2004) and
even complex metabolic networks (Guimerà & Amaral, 2005).
Years of research in network science have led to identifying a set of
characteristics that appear to be common in naturally occurring networks. One of the
most important characteristics of complex networks that have been studied is how the
nodes of the network connect to each other. Two fundamental forms of networks are
random graphs, in which all of the connections between nodes are assigned at random,
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and lattices, in which all of the nodes connect with some regular tiling. Many of the
complex networks studied to date fall somewhere in between these two extremes.
Though it was originally assumed that the internet was similar to a random graph,
Barabási (2013) summarized the internet as a “scale-free” network. Instead of a random
graph which should have a fairly uniform distribution of degree, or the number of
connections a single node has, the internet showed a power law distribution of a few
highly connected nodes and a vast majority of lower degree nodes. The high degree
nodes were often several orders of magnitude higher than the low degree nodes and thus
the network was termed scale-free. Other social and biological networks also tended to
show varying degrees of this scale-free connectivity (Girvan & Newman, 2002; Newman
& Girvan, 2004).
Another key aspect of naturally occurring networks is known as the small-world
phenomenon. This is perhaps most well-known because of Travers and Milgram's (1969)
classic experiment in which they had participants in Nebraska and Boston try to get a
letter through acquaintances to a target person in Massachusetts. They noted that the
number of intermediaries required was surprisingly low at an average of 5.2 and that
those in Boston, who were geographically closer to the target person, required fewer
intermediaries than the more distant Nebraskans. The important concept derived from
this experiment is that despite the scale and complexity of social networks in the entire
United States, the shortest path from any one node in a these networks to any other node
is relatively short. Similarly, these networks are highly clustered so that those
participants already in Massachusetts, who were more geographically neighbors, took
fewer steps to reach a social neighbor through others. In these ways, small-world
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networks can be characterized as having the high clustering of lattices and the short
typical path lengths of random networks (Watts & Strogatz, 1998).
Description and Classification of Networks
At present, there is a wide array of metrics used to describe and classify networks.
These metrics are grounded both in the mathematical properties of graph theory and in
successful application to known networks such as those mentioned previously. Network
science is a field in which new measures are created and updated as our understanding of
networks changes.
Perhaps the simplest characteristic of a node is its number of connections, or
degree (k). A node i with k = 20 is one which connects to 20 other nodes within the
i
network. The clustering coefficient (C) is a measure of interconnectedness of nodes in a
cluster. It describes the proportion of extant connections between neighbors of a node to
the potential number of connections between those same neighbors. The path length (L)
of a node is the average number of edges that must be used to travel the shortest
functional distance to any other node in the network. Related to the concepts of C and L
are those of local and global efficiency, respectively (Latora & Marchiori, 2001). The
efficiency measures are meant to give a representation of complex networks that can be
directly compared and characterize the degree to which a parallel network efficiently
transfers information. Local efficiency (E ) is meant to describe the overall efficiency
loc
of clusters in a network and, as Latora and Marchiori describe, reveal fault tolerance, or
how efficient communication between the neighbors of node i is when i is removed.
Global efficiency (E ) describes the overall efficiency of communication for the whole
glob