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Task-based co-activation patterns reliably predict resting state canonical network engagement during development.

Fengdan Ye1, Robert Kohler1, Bianca Serio1

  • 1Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA.

Developmental Cognitive Neuroscience
|October 21, 2022
PubMed
Summary
This summary is machine-generated.

This study shows that brain activity patterns during tasks can predict how large-scale neural networks, like the default mode network, function over time in adolescents. These findings suggest a common brain architecture underlies diverse cognitive functions.

Keywords:
AdolescenceCo-activationCognitionDevelopmentPredictive modelingResting-state connectivity

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Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Cognitive Neuroscience

Background:

  • Traditional neurodevelopmental research often focuses on individual brain structures.
  • Emerging evidence suggests parallel development of large-scale neural systems, such as canonical networks (e.g., default mode, frontoparietal).
  • The relationship between regional and network-level brain development is not well understood.

Purpose of the Study:

  • To evaluate a novel multi-task coactivation matrix approach for predicting canonical resting-state network engagement.
  • To assess the predictive ability of this approach at baseline and a two-year follow-up in young adolescents.
  • To explore the functional significance of task-based neural features in predicting network-level connectivity.

Main Methods:

  • Utilized pre-processed neuroimaging data from the Adolescent Brain and Cognitive Development (ABCD) study (N=6073 at baseline, N=3539 at follow-up).
  • Constructed individual multi-task coactivation matrices from task contrast beta weights (stop signal, monetary incentive delay, emotional N-back tasks).
  • Employed activation-based predictive modeling (cross-validated machine learning) to forecast resting-state network engagement from coactivation matrices.

Main Results:

  • The model successfully predicted connectivity within the default mode network (DMN) across participants (rho=0.179, p<0.001).
  • Key predictors of DMN connectivity were coactivations within parietal and occipital brain regions, indicating shared functional architecture.
  • Predictive features for DMN connectivity at baseline also accurately predicted DMN connectivity at the two-year follow-up (rho=0.258).

Conclusions:

  • Multi-task coactivation matrices are functionally meaningful and can predict resting-state network connectivity.
  • Task-based neural features can predict longitudinal changes in resting-state network connectivity, suggesting developmental stability.
  • Future research should use consistent parcellations and longer developmental trajectories to further validate these findings.