Structural and Functional differences between physically active and sedentary individuals.
Poster No:
736
Submission Type:
Abstract Submission
Authors:
Patricio Carvajal-Paredes1, Patricia Soto-Icaza1, María Paz Martínez1, Alejandra Figueroa1, Carla Manterola2, Cesar Salinas2, Ximena Stecher2, Francisco Zamorano3, Pablo Billeke1
Institutions:
1Universidad del Desarrollo, Santiago, Chile, 2Clínica Alemana, Santiago, Chile, 3Universidad San Sebastián, Santiago, Chile
First Author:
Co-Author(s):
Introduction:
Sedentary lifestyles are a growing global concern with significant health implications. Sedentarism, defined as low-energy activities (<1.5 METs) such as prolonged sitting, is linked to increased risks of cardiovascular diseases, type 2 diabetes, obesity, and cognitive decline (Booth et al., 2017). Despite its prevalence, sedentarism is often distinguished from physical inactivity, which refers to insufficient levels of moderate to vigorous activity (WHO, 2010).
Neuroscientific evidence suggests that physical activity enhances brain function and structure, particularly in regions responsible for cognitive control and emotional regulation. For instance, regular exercise increases hippocampal volume (Erickson et al., 2011) and promotes functional connectivity within fronto-parietal networks associated with attention and decision-making (Stillman et al., 2018). In contrast, sedentarism correlates with reduced white matter integrity, diminished cognitive flexibility, and heightened decision-making thresholds (Voss et al., 2014).
Given the reciprocal relationships between cognition, personality traits, and lifestyle, this study investigates whether differences in brain structure, cognitive performance, and neural connectivity exist between physically active and sedentary individuals during a cognitive control task.
Neuroscientific evidence suggests that physical activity enhances brain function and structure, particularly in regions responsible for cognitive control and emotional regulation. For instance, regular exercise increases hippocampal volume (Erickson et al., 2011) and promotes functional connectivity within fronto-parietal networks associated with attention and decision-making (Stillman et al., 2018). In contrast, sedentarism correlates with reduced white matter integrity, diminished cognitive flexibility, and heightened decision-making thresholds (Voss et al., 2014).
Given the reciprocal relationships between cognition, personality traits, and lifestyle, this study investigates whether differences in brain structure, cognitive performance, and neural connectivity exist between physically active and sedentary individuals during a cognitive control task.
Methods:
Participants:
114 healthy adults (45 women; M = 27.5, SD = 6.22) participated, with 71 undergoing fMRI. The IPAQ questionnaire categorized participants as active (n = 43) or sedentary (n = 28). All provided informed consent, and the study was approved by the Ethical Committee at Universidad del Desarrollo.
Experimental Task:
Participants completed the Multi-Source Interference Task (MSIT) during fMRI scanning. The MSIT measures cognitive control by presenting congruent (easy) and incongruent (difficult) number sequences requiring selective attention and conflict resolution.
Statistical Analyses:
Frequentist Analysis: Reaction Time (RT) and Accuracy were analyzed using t-tests or non-parametric tests.
Bayesian Drift Diffusion Model (DDM): The DDM decomposed RT and Accuracy into Drift Rate (evidence accumulation speed), Boundary (decision threshold), and Non-Decision Time. Covariates included Condition (active/sedentary), Sex, and Stimulus type.
Structural (DTI, FreeSurfer) and functional analyses examined brain regions and connectivity related to DDM parameters.
114 healthy adults (45 women; M = 27.5, SD = 6.22) participated, with 71 undergoing fMRI. The IPAQ questionnaire categorized participants as active (n = 43) or sedentary (n = 28). All provided informed consent, and the study was approved by the Ethical Committee at Universidad del Desarrollo.
Experimental Task:
Participants completed the Multi-Source Interference Task (MSIT) during fMRI scanning. The MSIT measures cognitive control by presenting congruent (easy) and incongruent (difficult) number sequences requiring selective attention and conflict resolution.
Statistical Analyses:
Frequentist Analysis: Reaction Time (RT) and Accuracy were analyzed using t-tests or non-parametric tests.
Bayesian Drift Diffusion Model (DDM): The DDM decomposed RT and Accuracy into Drift Rate (evidence accumulation speed), Boundary (decision threshold), and Non-Decision Time. Covariates included Condition (active/sedentary), Sex, and Stimulus type.
Structural (DTI, FreeSurfer) and functional analyses examined brain regions and connectivity related to DDM parameters.
Results:
Psychometric Findings:
Active participants scored higher on Extraversion and Physical Aggression, while sedentary individuals exhibited higher Responsibility and Neuroticism.
Behavioral Performance (MSIT):
Reaction Time (RT): Active participants were faster (M = 554.87 ms) but less accurate (80%) than sedentary participants (M = 631.67 ms; Accuracy = 92%).
Bayesian DDM Results:
Drift Rate (Processing Speed): Males exhibited significantly faster evidence accumulation than females.
Boundary (Decision Threshold): Sedentary participants required higher evidence thresholds, indicating slower, more cautious decision-making.
Neuroimaging Results:
White Matter Integrity: Active individuals displayed higher fractional anisotropy (FA) in 11 tracts, including the parahippocampal cingulum and uncinate fasciculus.
Hippocampal Volume and Shape: Active participants showed greater left hippocampal volumes and structural differences in CA3, CA4, and molecular layers.
Functional Activation: During the MSIT, active individuals exhibited efficient activation in fronto-parietal regions. Sedentary individuals showed higher threshold-related activity in areas requiring compensatory cognitive effort.
Connectivity: Active participants demonstrated stronger fronto-parietal connectivity, while sedentary individuals displayed enhanced connections with regions linked to cognitive demand (e.g., anterior cingulate).
Active participants scored higher on Extraversion and Physical Aggression, while sedentary individuals exhibited higher Responsibility and Neuroticism.
Behavioral Performance (MSIT):
Reaction Time (RT): Active participants were faster (M = 554.87 ms) but less accurate (80%) than sedentary participants (M = 631.67 ms; Accuracy = 92%).
Bayesian DDM Results:
Drift Rate (Processing Speed): Males exhibited significantly faster evidence accumulation than females.
Boundary (Decision Threshold): Sedentary participants required higher evidence thresholds, indicating slower, more cautious decision-making.
Neuroimaging Results:
White Matter Integrity: Active individuals displayed higher fractional anisotropy (FA) in 11 tracts, including the parahippocampal cingulum and uncinate fasciculus.
Hippocampal Volume and Shape: Active participants showed greater left hippocampal volumes and structural differences in CA3, CA4, and molecular layers.
Functional Activation: During the MSIT, active individuals exhibited efficient activation in fronto-parietal regions. Sedentary individuals showed higher threshold-related activity in areas requiring compensatory cognitive effort.
Connectivity: Active participants demonstrated stronger fronto-parietal connectivity, while sedentary individuals displayed enhanced connections with regions linked to cognitive demand (e.g., anterior cingulate).
·To high boundary level, less cortical thickness
·Contrast of activity associated with boundary: active > sedentary.
Conclusions:
Active participants exhibited faster cognitive processing, greater white matter integrity, and more efficient neural activation patterns
Higher Cognitive Functions:
Decision Making
Executive Function, Cognitive Control and Decision Making 1
Modeling and Analysis Methods:
Bayesian Modeling 2
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Keywords:
Cognition
FUNCTIONAL MRI
Modeling
MRI
Plasticity
Other - physical activity, sedentarism
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Bush, G., Shin, L. M., Holmes, J., Rosen, B. R., & Vogt, B. A. (2003). The Multi-Source Interference Task: Validation study with fMRI in individual subjects. Molecular Psychiatry, 8(1), 60-70. https://doi.org/10.1038/sj.mp.4001217
Erickson, K. I., Prakash, R. S., Voss, M. W., Chaddock, L., Hu, L., Morris, K. S., ... & Kramer, A. F. (2011). Aerobic fitness is associated with hippocampal volume in elderly humans. Proceedings of the National Academy of Sciences, 108(7), 3017-3022. https://doi.org/10.1073/pnas.1015950108
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127-138. https://doi.org/10.1038/nrn2787
Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20(4), 873-922. https://doi.org/10.1162/neco.2008.12-06-420
Stillman, C. M., Cohen, J., Lehman, M. E., & Erickson, K. I. (2018). Mediators of physical activity on neurocognitive function: A review at multiple levels of analysis. Frontiers in Human Neuroscience, 12, 62. https://doi.org/10.3389/fnhum.2018.00062
Voss, M. W., Nagamatsu, L. S., Liu-Ambrose, T., & Kramer, A. F. (2014). Exercise, brain, and cognition across the lifespan. Journal of Applied Physiology, 116(9), 1505-1513. https://doi.org/10.1152/japplphysiol.00210.2013
Wiecki, T. V., Sofer, I., & Frank, M. J. (2013). HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. Frontiers in Neuroinformatics, 7, 14. https://doi.org/10.3389/fninf.2013.00014