Resting-State Functional Connectivity and Scholastic Performance in Preadolescent Children: A Data-Driven Multivoxel Pattern Analysis (MVPA)

Scholastic performance is the key metric by which schools measure student’s academic success, and it is important to understand the neural-correlates associated with greater scholastic performance. This study examines resting-state functional connectivity (RsFc) associated with scholastic performance (reading and mathematics) in preadolescent children (7–9 years) using an unbiased whole-brain connectome-wide multi-voxel pattern analysis (MVPA). MVPA revealed four clusters associated with reading composite score, these clusters were then used for whole-brain seed-based RsFc analysis. However, no such clusters were found for mathematics composite score. Post hoc analysis found robust associations between reading and RsFc dynamics with areas involved with the somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks. These findings indicate that reading ability may be associated with a wide range of RsFc networks. Of particular interest, anticorrelations were observed between the default mode network and the somatomotor, dorsal attention, ventral attention, and frontoparietal networks. Previous research has demonstrated the importance of anticorrelations between the default mode network and frontoparietal network associated with cognition. These results extend the current literature exploring the role of network connectivity in scholastic performance of children.

[1]  Michelle Hampson,et al.  Functional connectivity between task-positive and task-negative brain areas and its relation to working memory performance. , 2010, Magnetic resonance imaging.

[2]  M. P. van den Heuvel,et al.  Exploring the brain network: a review on resting-state fMRI functional connectivity. , 2010, European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology.

[3]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[4]  Darya L. Zabelina,et al.  Dynamic network interactions supporting internally-oriented cognition , 2016, Current Opinion in Neurobiology.

[5]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[6]  S. Wasielewski.  Discrimination of brain-damaged, depressed, and normal subjects using the Woodcock-Johnson tests of cognitive ability-revised , 1998 .

[7]  Christiane S. Rohr,et al.  Pre-reading language abilities and the brain’s functional reading network in young children , 2020, NeuroImage.

[8]  Sarah A. Hezlett,et al.  Standardized Tests Predict Graduate Students' Success , 2007, Science.

[9]  Xi-Nian Zuo,et al.  Resting-State Functional Connectivity Indexes Reading Competence in Children and Adults , 2011, The Journal of Neuroscience.

[10]  Jonah E. Rockoff,et al.  Under Pressure: Job Security, Resource Allocation, and Productivity in Schools under No Child Left Behind , 2014 .

[11]  John D. E. Gabrieli,et al.  Selective Development of Anticorrelated Networks in the Intrinsic Functional Organization of the Human Brain , 2014, Journal of Cognitive Neuroscience.

[12]  B. J. Casey,et al.  Differential patterns of striatal activation in young children with and without ADHD , 2003, Biological Psychiatry.

[13]  Archana Venkataraman,et al.  Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. , 2010, Journal of neurophysiology.

[14]  David M Murray,et al.  Survey development for assessing correlates of young adolescents' eating. , 2002, American journal of health behavior.

[15]  C. Junqué,et al.  Alterations of the salience network in obesity: A resting‐state fMRI study , 2013, Human brain mapping.

[16]  Xi-Nian Zuo,et al.  Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.

[17]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[18]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[19]  Sarah A. Hezlett,et al.  Fact and Fiction in Cognitive Ability Testing for Admissions and Hiring Decisions , 2010 .

[20]  Marc Joliot,et al.  Mapping numerical processing, reading, and executive functions in the developing brain: an fMRI meta-analysis of 52 studies including 842 children. , 2010, Developmental science.

[21]  Arthur F. Kramer,et al.  Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging , 2016, NeuroImage.

[22]  Timothy B. Weng,et al.  Scholastic performance and functional connectivity of brain networks in children , 2018, PloS one.

[23]  Michael W. Cole,et al.  Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks , 2017, Proceedings of the National Academy of Sciences.

[24]  A. McIntosh,et al.  Aging Effects on Whole-Brain Functional Connectivity in Adults Free of Cognitive and Psychiatric Disorders. , 2016, Cerebral cortex.

[25]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  J D E Gabrieli,et al.  Brain connectomics predict response to treatment in social anxiety disorder , 2016, Molecular Psychiatry.

[27]  Hans Knutsson,et al.  Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates , 2016, Proceedings of the National Academy of Sciences.

[28]  P. S. Achilles.  THE PSYCHOLOGICAL CORPORATION. , 1923, Science.

[29]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[30]  Jonathan D. Power,et al.  Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..

[31]  F. Kay-Lambkin,et al.  Functional Connectivity Changes in Retired Rugby League Players: A Data-Driven, Functional Magnetic Resonance Imaging Study. , 2020, Journal of neurotrauma.

[32]  J. Gabrieli,et al.  Altered resting-state functional connectivity in young children at familial high risk for psychotic illness: A preliminary study , 2019, Schizophrenia Research.

[33]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[34]  Bharat B. Biswal,et al.  Competition between functional brain networks mediates behavioral variability , 2008, NeuroImage.

[35]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[36]  Dost Öngür,et al.  Anticorrelations in resting state networks without global signal regression , 2012, NeuroImage.

[37]  J. Ford,et al.  Default mode network activity and connectivity in psychopathology. , 2012, Annual review of clinical psychology.

[38]  Xavier Guell,et al.  Disrupted Cerebrocerebellar Intrinsic Functional Connectivity in Young Adults with High-Functioning Autism Spectrum Disorder: A Data-Driven, Whole-Brain, High-Temporal Resolution Functional Magnetic Resonance Imaging Study , 2019, Brain Connect..

[39]  Christos Davatzikos,et al.  Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.