Literature DB >> 34219221

Brain network modularity predicts changes in cortical thickness in children involved in a physical activity intervention.

Laura Chaddock-Heyman1,2, Timothy B Weng3, Psyche Loui2, Caitlin Kienzler4, Robert Weisshappel1, Eric S Drollette5, Lauren B Raine2, Daniel Westfall2, Shih-Chun Kao6, Dominika M Pindus1,7,8, Pauline Baniqued9,10, Darla M Castelli11, Charles H Hillman2,12, Arthur F Kramer1,2.   

Abstract

Individual differences in brain network modularity at baseline can predict improvements in cognitive performance after cognitive and physical interventions. This study is the first to explore whether brain network modularity predicts changes in cortical brain structure in 8- to 9-year-old children involved in an after-school physical activity intervention (N = 62), relative to children randomized to a wait-list control group (N = 53). For children involved in the physical activity intervention, brain network modularity at baseline predicted greater decreases in cortical thickness in the anterior frontal cortex and parahippocampus. Further, for children involved in the physical activity intervention, greater decrease in cortical thickness was associated with improvements in cognitive efficiency. The relationships among baseline modularity, changes in cortical thickness, and changes in cognitive performance were not present in the wait-list control group. Our exploratory study has promising implications for the understanding of brain network modularity as a biomarker of intervention-related improvements with physical activity.
© 2021 Society for Psychophysiological Research.

Entities:  

Keywords:  biomarkers; brain; brain network modularity; children; cortical thickness; intervention; physical activity

Mesh:

Year:  2021        PMID: 34219221      PMCID: PMC8419073          DOI: 10.1111/psyp.13890

Source DB:  PubMed          Journal:  Psychophysiology        ISSN: 0048-5772            Impact factor:   4.348


  69 in total

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Authors:  Alicia L Fedewa; Soyeon Ahn
Journal:  Res Q Exerc Sport       Date:  2011-09       Impact factor: 2.500

2.  FIT Kids: Time in target heart zone and cognitive performance.

Authors:  Darla M Castelli; Charles H Hillman; Jennifer Hirsch; Alayna Hirsch; Eric Drollette
Journal:  Prev Med       Date:  2011-01-31       Impact factor: 4.018

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Journal:  Neuroimage       Date:  2006-03-10       Impact factor: 6.556

4.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

Authors:  Jonathan D Power; Kelly A Barnes; Abraham Z Snyder; Bradley L Schlaggar; Steven E Petersen
Journal:  Neuroimage       Date:  2011-10-14       Impact factor: 6.556

5.  Physical fitness and academic achievement in third- and fifth-grade students.

Authors:  Darla M Castelli; Charles H Hillman; Sarah M Buck; Heather E Erwin
Journal:  J Sport Exerc Psychol       Date:  2007-04       Impact factor: 3.016

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Journal:  Neuroimage       Date:  1999-02       Impact factor: 6.556

7.  ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data.

Authors:  Raimon H R Pruim; Maarten Mennes; Daan van Rooij; Alberto Llera; Jan K Buitelaar; Christian F Beckmann
Journal:  Neuroimage       Date:  2015-03-11       Impact factor: 6.556

8.  Quantitative magnetic resonance imaging of human brain development: ages 4-18.

Authors:  J N Giedd; J W Snell; N Lange; J C Rajapakse; B J Casey; P L Kozuch; A C Vaituzis; Y C Vauss; S D Hamburger; D Kaysen; J L Rapoport
Journal:  Cereb Cortex       Date:  1996 Jul-Aug       Impact factor: 5.357

9.  Accurate and robust brain image alignment using boundary-based registration.

Authors:  Douglas N Greve; Bruce Fischl
Journal:  Neuroimage       Date:  2009-06-30       Impact factor: 6.556

10.  Cross-calibration of body-composition techniques against dual-energy X-ray absorptiometry in young children.

Authors:  M I Goran; P Driscoll; R Johnson; T R Nagy; G Hunter
Journal:  Am J Clin Nutr       Date:  1996-03       Impact factor: 7.045

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