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Connectome-based prediction modeling of cognitive control using functional and structural connectivity.

Qiuyu Lv1, Xuanyi Wang2, Xiang Wang3

  • 1Center for Mind & Brain Sciences and Institute of Interdisciplinary Studies, Hunan Normal University, Hunan, Changsha, 410081, China; Medical Psychological Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; China National Clinical Research Center for Mental Disorders (Xiangya), Changsha, Hunan, China.

Brain and Cognition
|September 9, 2024
PubMed
Summary

Brain structure and function networks both predict cognitive control. Combining both provides a fuller picture of how the brain supports goal-directed actions and behavior adjustment.

Keywords:
Cognitive controlConnectome-based predictive modeling (CPM)Machine learningStructural and functional connectome

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Brain Imaging

Background:

  • Cognitive control enables flexible resource allocation and goal-directed behavior.
  • The neural basis of cognitive control involves coordinated brain network activity.
  • Predicting cognitive control from brain networks remains an active research area.

Purpose of the Study:

  • To investigate the predictive power of structural and functional brain networks for cognitive control.
  • To compare the contributions of structural and functional connectomes to cognitive control prediction.
  • To identify overlapping and distinct network patterns underlying cognitive control subcomponents.

Main Methods:

  • Utilized Connectome-Based Predictive Modeling (CPM) on a dataset of 102 healthy adults.
  • Analyzed both structural and functional brain connectivity data.
  • Compared predictive accuracy and network characteristics of structural versus functional connectomes.

Main Results:

  • Both structural (r=0.263-0.375) and functional (r=0.336-0.503) connectomes significantly predicted cognitive control subcomponents.
  • Overlapping functional and structural network patterns were observed, particularly in frontoparietal and motor networks.
  • Unique predictive network weights were identified for each cognitive control subcomponent, indicating distinct spatial patterns.

Conclusions:

  • Structural and functional connectomes offer complementary insights into cognitive control prediction.
  • Integrating both structural and functional connectivity data enhances the understanding of cognitive control's neural underpinnings.