![Effect size profile of group (TC, M, A, and S) and age in years for key cognitive and physiological variables, from [21].](https://www.researchgate.net/profile/Trevor-Bihl/publication/309034829/figure/fig4/AS:416512654495744@1476315763047/Effect-size-profile-of-group-TC-M-A-and-S-and-age-in-years-for-key-cognitive-and_Q320.jpg)
Content uploaded by Trevor J. Bihl
Author content
All content in this area was uploaded by Trevor J. Bihl on Oct 13, 2016
Content may be subject to copyright.
Content uploaded by Teresa Hawkes
Author content
All content in this area was uploaded by Teresa Hawkes on Oct 12, 2016
Content may be subject to copyright.
Proceedings of the 2016 Cincinnati-Dayton INFORMS Symposium 32
Abstract-P2
Berry 3
Interacting Systems Approach to Investigation of Human Cognitive Capacity
Teresa D. Hawkes (University of Oklahoma)
Trevor J. Bihl (Wright State University)
Marjorie H. Woollacott (University of Oregon)
Poster and Paper
Abstract Human cognitive capacity is mediated by synergistic interactions between multiple
physiological variables: including genetic and epigenetic potential, nutrition status, gender,
race, age, cardiovascular capacity, neural function, and physical activity type and amount.
These affect attention, memory, decision-making, educational outcomes,
personality/temperament, and socio-economic potential. This suggests cognitive capacity arises
from our physiology interacting with exogenous and endogenous stimuli. Since cognition
originates from physiology, which consists of an integrated set of systems operating at nested
time scales with spatial relationships consistent across subjects, research into human cognition
requires multivariate experimental designs which take this into account.
Keyword: Biostatistics/Biomedical
1. Introduction
Human cognitive capacity is dependent on nested, interacting physiological components
beginning at the genetic/epigenetic level. Gene transcription by allele [1] [2] and epigenetic
regulation of such instantiate neuron structure and function [3] [4] and interact with nutritive
factors (e.g. iron status) and physical activity [5] to up- or downregulate [6] brain structure and
function in humans [7] and rat [8]. At the next system level, neurons are organized into over 200
specialized groups called microcircuits [9] that assemble dynamically in response to task
demands [10] [11] [12] [13] [14] [15] [16] [17] [18] (see Figure 1). Microcircuits that participate
in transient task-specific networks include the dorsolateral prefrontal cortex, anterior and
posterior cingulated cortices, orbitofrontal cortex, insula, perirhinal cortex and hippocampus.
VO2Max (a gold standard proxy for cardiovascular capacity) [19] has been associated with better
cognitive capacity in the study described herein [20] [21] and in a recent fMRI study of brain
activation during an executive function task in older adults [22].
The study described here was specifically designed to assess differential effects of
chronic exercise-type on executive function and took into account the integrated systems
subserving cognitive output (see Figure 2). Subjects were recruited based on their exercise type
and were required to have practiced or been sedentary for five or more years: Tai Chi (TC in
charts), Meditation + Exercise (M in charts), Aerobic Fitness (A in charts), and Sedentary (S in
charts). Requirements for group membership are presented in Figure 2 and were based on three
categories: exercise exertion, attentional focus required, and complexity of motor output
required. The Aerobic Fitness (A) group served as an active control and the Sedentary (S) group
served as an overall control with the Tai Chi (TC) and Meditation + Exercise (M) regimes,
permitting isolation of attention, exertion, and motor output effects on complex executive
function.
This paper further explains the structure of the study presented in [21] and further
analyzes the collected data. This paper is organized as follows: Section 2 presents the overall
Proceedings of the 2016 Cincinnati-Dayton INFORMS Symposium 33
experimental design structure, Section 3 presents results and interpretation, Section 4 provides a
summary and conclusion.
Figure 1. Major physiological processing components instantiating cognitive output.
Arrows indicate flow of information between components .
2. The Design Structure
The three exercise groups, TC, A, and M, each required 4-8 metabolic equivalents
(METs) of exertion, with METs indexing energy expenditure relative to resting metabolic rate.
Tai Chi is a martial art which requires 1) complex gross motor coordination, 2) shifts of visual
focus, 3) control of breath relative to movement, and 4) memorization of complex movement
sequences. Aerobic Fitness subjects were those exerting 4-8 METs through various exercises
requiring only relaxed attention. The Meditation + Exercise group consisted of subjects whose
regime required 4-8 METs exertion plus sustained, exclusive mental focus on a specific
sensation, image, goal, or syllable(s).
Proceedings of the 2016 Cincinnati-Dayton INFORMS Symposium 34
Figure 2. Integrated system requirements for execution of each exercise type.
This approach leveraged multivariate statistical procedures to go beyond traditional
yes/no null hypothesis tests and investigate sets of associations between system indexes (e.g.
electroencephalographic event-related potentials) and proxies (e.g. VO2Max). For this research,
the following steps were performed [21]. These steps are suggested as a template for human
studies research into cognitive capacity.
Step one: Select a key evidence-based group to compare (IV or quasi-IV). Inclusion of
one key covariate is permitted, but it must be a covariate known to explain significant
variance on any included dependent variable (DV). In the study presented, since age is
known to strongly affect VO2 Max [24] and cognitive capacity [25], age was included as
a covariate. The literature behind any cognitive hypothesis can provide enough guidance
to permit such winnowing of possible IVs and covariates. Always include an active
control. The active control in this case was the aerobics group (exercise with no
consistent focused attention requirement). The passive control was the sedentary group
(no exercise, no activities requiring exercise exertion accompanied by an attentional
requirement).
Step two: Identify key physiological systems affected by group membership. The study
presented illustrates this approach [20] [21]. Each type of exercise placed different
requirements on key systems, thus based on the literature could be expected to produce
different cognitive outcomes.
Step Three: State integrated systems hypotheses. In the exercise study presented here, we
had two hypotheses: 1) All exercise groups would outperform sedentary controls on
estimated VO2 Max, and thus outperform Sedentary subjects on the complex executive
function test (including ERPs). 2) Exercise requiring attentional focus was hypothesized
to produce better cognitive outcomes compared to exercise permitting relaxed attentional
focus (active control), thus we expected the TC and M groups to outperform A and S
groups on neural and behavioral indices of complex executive capacity.
Step Four: Do the research.
Proceedings of the 2016 Cincinnati-Dayton INFORMS Symposium 35
Step Five: Perform multivariate analyses to obtain p-values, effect size profiles,
correlations, and z-score profiles.
2.1 Data Collection
Besides demographic and exercise regimen data, participants were tested with the
Rockport 1-Mile Walk (a well-validated estimated VO2 Max test), body-mass index, and dense-
array EEG P300 event-related potential during a demanding task switch test of complex
executive function (see Methods in [20] [21]).
2.2 Subject Characteristics
For the study, 54 subjects completed all tests and were included in this analysis.
Descriptive statistics for subject physiological measures by group are presented in Table 1 and
Figure 3. While similar groups would be ideal, it was not possible to ensure that each group was
identical. Figure 3 presents boxplots for age based on pairs of Gender and Lifetime Exercise
Discipline, Figure 3a, Education Years versus Gender and Lifetime Education Discipline, Figure
3b, BMI (kg/m2) vs Gender and Lifetime Exercise Discipline, Figure 3c, and VO2 Max
(mL/kg/min O2) versus Gender and Lifetime Exercise Discipline, Figure 3d. Noticeably, for age,
paired groupings differ across both Gender and Lifetime Exercise Discipline types; however,
overlap is largely seen with the exception being Males who are Sedentary. The Education Years
versus Gender and Lifetime Exercise Discipline shows more homogenous groups, with the
exception of Sedentary Females and Meditation + Exercise Females. In this cross-sectional
study, the effects of different ages between groups worked to our advantage, as it allowed us to
see that the TC and M groups, though older than the A and S groups, outperformed them on our
key cognitive and physiological variables (complex executive attention and ERP neural indices
of brain activity during such, estimated VO2 Max and body-mass index).
Table 1. Subject Physiological Scores, from [21]
Group
N
Females
Age, years
VO2 Max, mL/kg/min
O2
BMI, kg/m2
Mean
S.D.
Mean
S.D.
Mean
S.D.
TC
10
3
55.4
12.99
34.14
6.34
29.3
3.77
M
16
6
48.63
15.00
41.83
9.04
23.3
3.53
A
16
8
44.09
16.2
45.66
9.67
23.78
2.62
S
12
10
46.92
12.81
28.68
5.76
27.93
6.37
Proceedings of the 2016 Cincinnati-Dayton INFORMS Symposium 36
a) Age vs Gender and Lifetime Exercise
Discipline
b) Education Years vs Gender and Lifetime
Exercise Discipline
c) BMI (kg/m2) vs Gender and Lifetime Exercise
Discipline
d) VO2 Max (mL/kg/min O2) versus Gender and
Lifetime Exercise Discipline
Figure 3. General Characteristics of Subjects
2.3 Key Results
Figure 4 presents the
effect size profile for
variance explained by group
and age for the significant
variables as determined in
[21].
3. Results Interpretation.
While p-values shed
light on differential
distributions associated with
IV effects on each DV, effect
size profiles can tease apart
the differential effects of IV
and covariate on systems
Figure 4. Effect size profile of group (TC, M, A, and S) and
age in years for key cognitive and physiological variables,
from [21].
Proceedings of the 2016 Cincinnati-Dayton INFORMS Symposium 37
represented by each DV. Normalized system profiles, Figure 5, by DV allow quick identification
of individual performance within group performance. In the study illustrated here, we see in
Figure 5 that Group has more effect on key DVs than age, even though age is known to strongly
affect human physiological and cognitive capacity.
Figure 5. Z-score profiles for each key variable show the distribution of scores by exercise
group
4. Conclusions.
This simple interacting systems study showed that exercise group had more effect on key
outcome variables than age. Further, TC and M groups, though older than the S or A groups, out-
performed them. But, this study did not include key epigenetic or genetic variables that may
explain some of these effects. A more complete variable panel should include such in follow-on
studies. Additionally, this cross-sectional study could not establish causation. Future longitudinal
training studies could shed more light on the differential effects of exercise + attention on
cognitive outcomes. More importantly, this approach can tease apart the interaction between
endogenous (physiological) and exogenous (training or other types of experiences) variables on
human cognitive capacity. This should yield valuable date for use in clinical, training and
research settings.
5. Bibliography
1. M. Egan, M. Kojima and J. e. a. Callicott, "The BDNF val66met polymorphism affects
activity-dependent secretion of BDNF and human memory and hippocampal function," Cell,
vol. 112, pp. 257-269, 2003.
2. K. Erickson, S. Banducci and A. e. a. Weinstein, "The brain-derived factor val66met
polymorphism moderates an effect of physical activity on working memory performance,"
Psychological Science, vol. 24, no. 9, pp. 1770-9, 2013.
Proceedings of the 2016 Cincinnati-Dayton INFORMS Symposium 38
3. A. Graves, S. Moore, N. Spruston, A. Tryba and C. Kaczorowski, "Brain derived
neurotrophic factor differentially modulates excitability of two classes of hippocampal output
neurons," Journal of Neurophysiology, no. May 4, p. doi:10.1152/jn.00186.2016, 2016.
4. H.-Y. Wey, T. M. Gilbert, N. R. Zurcher, A. She, A. Bhanot, B. D. Taillon, F. A. Schroeder,
C. Wang, S. J. Haggarty and J. M. Hooker, "Insights into neuroepigenetics through human
histone deacetylase PET imaging," Science Translational Medicine, vol. 8, no. 351, p.
351ra106, 2016.
5. A. Bryan, K. E. Hutchison, D. R. Seals and D. L. Allen, "A transdisciplinary model
integrating genetic, physiological, and psychological correlates of voluntary exercise,"
Health Psychology, vol. 26, no. 1, pp. 30-39, 2007.
6. J. R. Gilbert, M. Symmonds, M. G. Hanna, R. J. Dolan, K. J. Friston and R. J. Moran,
"Profiling neuronal ion channelopathies with non-invasive brain imaging and dynamic causal
models: case studies of single gene mutations," NeuroImage, vol. 124, pp. 43-53, 2016.
7. N. Jahanshad, O. Kohannim, D. P. Hibar, J. L. Stein, K. L. McMahon, G. I. de Zubicaray, S.
E. Medland, G. W. Montgomery, J. B. Whitfield, N. G. Martin, M. J. Wright, A. W. Toga
and P. M. Thompson, "Brain structure in healthy adults is related to serum transferrin and the
H63D polymorphism in the HFE gene," Proceedings of the National Academy of Sciences,
no. January 9, p. doi:/10.1073/pnas.1105543109, 2012.
8. P. V. Tran, E. S. Carlson, S. J. Fretham and M. K. Georgieff, "Early-life iron deficiency
anemia alters neurotrophic factor expression and hippocampal neuron differentiation in male
rats," The Journal of Nutrition: Ingestive Behavior and Neurosciences, vol. 138, pp. 2495-
2501, 2008.
9. M. F. Glasser, T. S. Coalson, E. C. Robinson, C. D. Hacker, J. Harwell, E. Yacoub, K.
Urgurbil, J. Andersson, C. F. Beckmann, M. Jenkinson and S. M. V. E. D. C. Smith, "A
multi-modal parcellation of human cerebral cortex," Nature, vol. 536, p.
doi:10.1038/nature18933, 2016.
10. L. Carillo-Reid, F. Tecuapetla, D. Tapia, A. Hernandez-Cruz, E. Calarraga, R. Drucker-Colin
and J. Bargas, "Encoding network states by striatal cell assemblies," Journal of
Neurophysiology, pp. 1435-1450, 2008.
11. M. Cole and W. Schneider, "The cognitive cotrol network: integrated cortical regions with
dissociable functions," NeuroImage, vol. 37, pp. 343-360, 2007.
12. L. Finkel and G. Edelman, "Integration of distributed cortical systems by reentry: a computer
simulation of interactive functionally segregated visual areas.," The Journal of Neuroscience,
vol. 9, no. 9, pp. 3188-3288, 1989.
13. Y.-Y. Tang, M. K. Rothbart and M. I. Posner, "Neural correlates of establishing, maintainng,
and switching brain states," Trends in Cognitive Science, vol. 16, no. 6, pp. 330-337, 2012.
14. J. Jiang, K. Bailey, L. Xiang and Q. Zhang, "Comparing the neural correlates of conscious
and unconscious conflict control in a masked stroop priming task," Frontiers in Human
Neuroscience, vol. 10, p. doi:10.3389/fnhum.2016.00297, 2016.
15. C. Ranganath and M. Ritchey, "Two cortical systems for memory-guided behavior," Nature
Reviews Neuroscience, vol. 13, pp. 713-726, 2012.
Proceedings of the 2016 Cincinnati-Dayton INFORMS Symposium 39
16. A. Talati and J. Hirsch, "Functional specialization within the medial frontal gyrus for
perceptual go/no-go decisions based on "what," "when," and "where" related information: an
fMRI study," Journal of Cognitive Neuroscience, vol. 17, no. 7, pp. 981-993, 2005.
17. R. Kanai, Y. Komura, S. Shipp and K. Friston, "Cerebral hierarchies: predictive processing,
precision and the pulvinar," Philosophical Transactions of the Royal Society B, vol. 370, p.
doi:10.1098/rstb.2014.0169, 2014.
18. T. Shiner, M. Symmonds, M. Guitart-Masip, S. Fleming, K. Friston and R. Dolan,
"Dopamine, salience, and response set shifting in prefrontal cortex," Cerebral Cortex, vol.
25, pp. 3629-3639, 2015.
19. E. Ferrannini, "The theoretical bases of indirect calorimetry," Metabolism, vol. 37, no. 3, pp.
287-301, 1988.
20. T. Hawkes, W. Manselle and M. Woollacott, "Cross-sectional comparison of executive
attention funciton in normally-aging long-term Tai Chi, meditation, and aerobic fitness
pracctitioners vs. sedentary adults," The Journal of Alternative and Complementary
Medicine, vol. 20, no. 3, pp. 178-184, 2014.
21. T. Hawkes, W. Manselle and M. Woollacott, "Tai Chi and meditation-plus-exercise benefit
neural substrates of executive function A cross-sectional, controlled study," Journal of
Complementary and Integrative Medicine, vol. 11, no. 4, pp. 279-288, 2014.
22. C. Wong, L. Chaddock-Heyman, M. Voss, A. Burzynska, C. Basak, K. Erickson, R. Prakash,
A. Szabo-Reed, S. Phillips, T. Wojcicki, E. Mailey, E. McAuley and A. Kramer, "Brain
activation during dual-task processing is associated with cardiorespiratory fitness and
performance in older adults," Frontiers in Aging Neuroscience, vol. 7, p. doi:
10.3389/fnagi.2015.00154, 2015.
23. S. Horowitz, "T’ai Chi and Qigong: Validated Health Benefits.," Alternative and
Complementary Therapies, vol. 20, no. 5, pp. 263-269, 2014.
24. A. C. Betik and R. T. Hepple, "Determinants of VO2Max decline with aging: an integrated
perspective," Applied Physiology, Nutrition and Metabolism, vol. 33, pp. 130-140, 2008.
25. E. Glisky, "Chapter 1. Changes in Cognitive Function in Human Aging," in Brain Aging:
Models, Methods, and Mechanisms, Boca Raton, FL, CRC Press/Taylor & Francis, 2007.
26. M. Silver, D. Ress and D. Heeger, "Topographic maps of visual spatial attention in human
parietal cortex," Journal of Neurophysiology, vol. 94, pp. 1358-1371, 2005.
