Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-24T22:45:44.758Z Has data issue: false hasContentIssue false

382 Unitary neural correlates of self-control in pediatric transdiagnostic psychopathology

Published online by Cambridge University Press:  03 April 2024

Adam Kaminski
Affiliation:
Georgetown-Howard Universities Center for Clinical and Translational Science
Hua Xie
Affiliation:
Children’s Research Institute, Children’s National Medical Center, Washington, DC
Brylee Hawkins
Affiliation:
Georgetown University, Washington, DC
Laura Campos
Affiliation:
Children’s Research Institute, Children’s National Medical Center, Washington, DC
Madison Berl
Affiliation:
Children’s Research Institute, Children’s National Medical Center, Washington, DC
Lauren Kenworthy
Affiliation:
Children’s Research Institute, Children’s National Medical Center, Washington, DC
Chandan J. Vaidya
Affiliation:
Georgetown University, Washington, DC Children’s Research Institute, Children’s National Medical Center, Washington, DC
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

OBJECTIVES/GOALS: Childhood psychopathology is a worsening public health crisis leading to negative life outcomes, including self-harm and suicide. Difficulty in self-control as early as 3 years old predicts psychopathology, but the mediating mechanisms of brain function are unknown. Here, we tested one mechanism: functional connectivity (FC) integration. METHODS/STUDY POPULATION: We studied a sample of 204 children [53 F/149 M/2 NC; mean age (SD)=11 years (1.7)] with diverse self-control difficulties (e.g., attention deficit disorder [n=80]; autism spectrum disorders [n=91]). We extracted a general factor of psychopathology (“p-factor”) from the parent-reported Child Behavior Checklist. For participants with high quality fMRI data on 3 self-control tasks (n=79), testing flexibility, working memory, and inhibition, we calculated FC connectomes reflecting a general self-control state, and applied connectome predictive modeling (CPM) to reveal connections predicting overall task impairment. We then measured individual variance in cross-network integration of regions with the most predictive connections and tested for association with p-factor in a multiple linear regression. RESULTS/ANTICIPATED RESULTS: We repeated CPM 1,000 times with 10-fold cross validation to generate a distribution of accuracies for predicted vs. observed task impairment scores (mean r=0.25, permutation p=0.02). Connections selected a maximum of 10,000 times (10 folds * 1,000 repetitions) were strongly predictive of task impairment (r=-0.5, p<0.001), highlighting connectivity of canonical executive networks as well as the default mode network. Regions (n=22) with the top 5% most selected connections were in lateral parietal and frontal cortices and implicated motor control. Between-network integration, operationalized with the graph theory metric participation coefficient, of one of these regions in left posterior superior frontal gyrus significantly predicted p-factor (R2=0.26, F(22,56) = 0.87; B =-0.49, p<0.05). DISCUSSION/SIGNIFICANCE: A portion of dorsolateral prefrontal cortex, associated with executive control, explained individual variance in p-factor. We plan to test alternative predictive models. Identification of such a neuro behavioral mechanism underlying psychopathology may lead to novel intervention targets.

Type
Precision Medicine/Health
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2024. The Association for Clinical and Translational Science