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Intrinsic Patch-Based Cortical Anatomical Parcellation Using Graph Convolutional Neural Network on Surface Manifold.

Zhengwang Wu¹, Fenqiang Zhao¹, Jing Xia¹

¹Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.

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Summary

This study introduces a novel graph convolutional neural network method for direct brain cortical surface parcellation, eliminating the need for spherical mapping and improving accuracy on impaired brains.

Keywords:

Cortical surface parcellation Graph CNN

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Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

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Published on: January 10, 2015

Area of Science:

Neuroimaging
Computational Neuroscience
Medical Image Analysis

Background:

Automatic parcellation of cortical surfaces into regions of interest (ROIs) is crucial for brain analysis.
Conventional methods rely on spherical mapping, which is sensitive to topological noise and fails with impaired brains.

Purpose of the Study:

To develop a direct cortical surface parcellation method that bypasses spherical mapping.
To address limitations of existing methods in handling topological noise and brain impairments.

Main Methods:

Utilizing graph convolutional neural networks (GCNs) for direct parcellation on the original cortical surface manifold.
Extending surface manifold convolution using a kernel strategy to handle inter-subject shape variations.
Learning nonlinear mapping between local cortical attribute patterns and parcellation labels.

Main Results:

The proposed GCN method achieves comparable accuracy to spherical mapping techniques on normal cortical surfaces.
The method demonstrates robust performance on synthetic datasets with impaired brains, successfully parcellating surfaces violating spherical topology.

Conclusions:

Direct cortical surface parcellation using GCNs offers a robust alternative to traditional spherical mapping methods.
This approach enhances brain analysis by accommodating complex cortical shapes and topological abnormalities.