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Atlas-guided parcellation: Individualized functionally-homogenous parcellation in cerebral cortex.

Yu Li¹, Aiping Liu¹, Xueyang Fu²

¹Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China; School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China.

Computers in Biology and Medicine

|September 26, 2022

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This study introduces an efficient brain parcellation method using resting-state fMRI for precise individual brain mapping. The new framework achieves high functional homogeneity and accurately predicts Parkinson's disease symptoms.

Area of Science:

Neuroimaging
Computational Neuroscience
Brain Mapping

Background:

Resting-state fMRI parcellation is crucial for understanding brain organization and individual variability.
Current individual parcellation methods face challenges with computational cost, alignment, and reliance on group data.

Purpose of the Study:

To develop an efficient and flexible framework for individual cerebral cortex parcellation.
To assess the framework's performance in functional homogeneity, individual identification, and disease symptom prediction.

Main Methods:

A region growing algorithm was employed, iteratively merging vertices based on connectivity profiles.
The framework integrates consistency with prior atlases and individualized functional homogeneity.
Applied to 100 healthy subjects for validation and 186 Parkinson's disease patients for prediction.

Keywords:

Individual identification Individualized parcellation Magnetic resonance imaging Region growing Symptom prediction

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Main Results:

The proposed framework demonstrated superior functional homogeneity compared to existing methods.
Achieved 100% accuracy in individual identification.
Default Mode Network (DMN) showed higher homogeneity and stability than sensorimotor networks.
Parkinson's disease symptom severity correlated negatively with individual parcellation similarity to healthy atlases and was predictable using machine learning.

Conclusions:

The framework offers an efficient and accurate approach to individual brain parcellation.
It effectively captures individualized brain topography and disease-related alterations.
Shows potential as a tool for exploring brain function and neurological disorders.