The brain selectively allocates energy to functional brain networks under cognitive control
Majid Saberi1,2, Jenny R Rieck3, Shamim Golafshan4
1Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada. majidsa@umich.edu.
Scientific Reports
|December 31, 2024
Summary
Brain network energy increases during cognitive control tasks. This elevated energy is selectively allocated to sensory networks, enhancing processing flexibility and improving predictive models for cognitive tasks and age.
Area of Science:
- Neuroscience
- Complex Systems Science
- Cognitive Science
Background:
- Network energy, derived from structural balance theory in complex networks, offers a novel framework for analyzing brain function.
- Understanding how the brain allocates energy across functional networks during cognitive tasks is crucial for elucidating neural mechanisms.
Purpose of the Study:
- To assess the energy of functional brain networks under cognitive control using the network energy framework.
- To investigate the allocation of network energy across canonical functional networks during various cognitive control tasks.
- To evaluate the utility of network energy as a global network measure for predictive modeling.
Main Methods:
- Functional magnetic resonance imaging (fMRI) data were acquired from subjects performing cognitive tasks (working memory, inhibitory control, cognitive flexibility) and during a task-free resting state.
- Network energy was extracted from functional connectivity patterns derived from fMRI data.
- Network energy was employed as a global network measure to improve predictive modeling performance.
Main Results:
- Whole-brain network energy significantly increased during cognitive control tasks compared to the task-free resting state.
- Elevated network energy was selectively allocated; sensory networks received more energy for processing flexibility, while efficient cognitive networks required less.
- Network energy improved the performance of predictive models for classifying cognitive control tasks and predicting chronological age.
Conclusions:
- The network energy framework provides a robust method for quantifying brain network dynamics during cognitive control.
- Network energy serves as a valuable global network measure, enhancing predictive modeling capabilities.
- Network energy shows significant potential as a biomarker for understanding brain mechanisms and neurological conditions.
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