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Functional Parcellation of Individual Cerebral Cortex Based on Functional MRI.
Jiajia Zhao1, Chao Tang1, Jingxin Nie2
1School of Psychology, Center for Studies of Psychological Application, Institute of Cognitive Neuroscience, South China Normal University, Guangzhou, 510631, China.
Neuroinformatics
|December 6, 2019
Summary
This study introduces a novel method for individual brain mapping using resting-state functional MRI (rs-fMRI). The approach accurately delineates brain regions, improving understanding of individual functional differences.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Brain Mapping
Background:
- Traditional brain atlases based on morphometry lack consistency and fail to capture individual functional variations, particularly in the cerebral cortex.
- Functional atlases are crucial for understanding unique brain organization and behavioral variations across individuals.
- Existing methods struggle to provide precise, individualized functional parcellation of the cerebral cortex.
Purpose of the Study:
- To develop and validate a novel approach for accurate, individual-level cerebral cortex parcellation using resting-state functional MRI (rs-fMRI).
- To introduce and utilize a new metric, the similarity of cluster (SC) coefficient, for evaluating functional homogeneity in parcellation.
- To assess the consistency and accuracy of the proposed functional parcellation method across different sessions and conditions.
Main Methods:
- Utilized resting-state functional magnetic resonance imaging (rs-fMRI) data to parcellate the entire cerebral cortex at an individual level.
- Developed a novel evaluation criterion, the similarity of cluster (SC) coefficient, to quantify functional homogeneity within parcellated regions.
- Compared parcellation results across different resting-state sessions and between resting and task-based fMRI sessions.
Main Results:
- Achieved high consistency between two resting-state sessions, with Dice coefficients greater than 0.72.
- Identified the frontal cortex as having the most consistent parcellation and the occipital cortex as the least consistent.
- Determined an optimal clustering number of approximately 1600 regions per hemisphere based on SC values.
- Demonstrated high functional homogeneity in the frontal cortex and insula, and lower homogeneity in the precentral gyrus.
- Attained 100% identification accuracy between two rs-fMRI sessions and above 0.97 for rest-task and task-task comparisons.
Conclusions:
- The proposed rs-fMRI based method enables accurate and consistent individual-level cerebral cortex parcellation.
- The similarity of cluster (SC) coefficient is an effective metric for evaluating functional homogeneity in brain parcellation.
- This approach holds significant potential for advancing personalized neuroscience research and understanding individual brain function.
