Identifying developmental changes in functional brain connectivity associated with cognitive functioning in children
Brian Pho1, Ryan Andrew Stevenson2, Sara Saljoughi3
1Program in Neuroscience, University of Western Ontario, London, ON, Canada.
Developmental Cognitive Neuroscience
|August 25, 2024
Summary
Machine learning models successfully predicted cognitive abilities in children with Attention-Deficit/Hyperactivity Disorder (ADHD) using brain functional connectivity. These predictive patterns differed between childhood and adolescence in ADHD youth.
Area of Science:
- Neuroscience
- Developmental Psychology
- Cognitive Science
Background:
- Youth with Attention-Deficit/Hyperactivity Disorder (ADHD) often exhibit executive functioning deficits.
- Differences in brain functional connectivity are linked to cognitive impairments in ADHD.
- Developmental changes in brain functional properties related to cognition in ADHD youth are not well understood.
Purpose of the Study:
- To characterize developmental changes in brain functional connectivity associated with cognitive abilities in youth with ADHD.
- To investigate how machine learning can predict cognitive performance based on functional brain networks in ADHD.
- To identify specific brain networks that predict cognitive abilities across different developmental stages in ADHD.
Main Methods:
- fMRI data from 373 youth with ADHD and 106 neurotypical (NT) individuals (ages 6-16) were analyzed.
- Machine learning models were employed to identify predictive patterns in functional network connectivity during movie-watching.
- Out-of-sample cross-validation was used to assess prediction accuracy for cognitive abilities.
Main Results:
- Machine learning models accurately predicted IQ, visual-spatial, verbal comprehension, and fluid reasoning in children (ages 6-11) with ADHD.
- Prediction accuracy was not achieved in adolescents (ages 12-16) with ADHD.
- Key predictive connections shifted from default mode, memory retrieval, and dorsal attention networks in early childhood to somatomotor, cingulo-opercular, and frontoparietal networks in middle childhood.
Conclusions:
- Machine learning can identify distinct functional connectivity profiles linked to cognitive abilities in ADHD across development.
- Brain network contributions to cognitive functioning in ADHD vary significantly between childhood and adolescence.
- This study highlights the potential of neuroimaging and machine learning to understand cognitive development in ADHD.
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