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Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices

Tomoki Tokuda¹, Okito Yamashita², Junichiro Yoshimoto³

¹Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan; Okinawa Institute of Science and Technology Graduate University, 1919-1 Tancha, Okinawa 904-0495, Japan.

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|May 30, 2021

Summary

This summary is machine-generated.

This study introduces a novel multiple clustering method for functional magnetic resonance imaging (fMRI) data. The method preserves brain region correlation structures, improving the identification of subject subtypes and brain network associations.

Keywords:

Clustering Functional connectivity Multiple clustering Wishart mixture

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Area of Science:

Neuroscience
Brain Imaging
Statistical Analysis

Background:

Functional magnetic resonance imaging (fMRI) is crucial for non-invasively assessing brain activity and inferring functional connectivity (FC).
Clustering subjects based on FC is a promising clinical application for identifying heterogeneity, such as psychiatric disorder subtypes.
Existing multiple clustering methods often require simplifying fMRI data structures (vectorization), potentially distorting results.

Purpose of the Study:

To propose a novel multiple clustering method for fMRI data that preserves the correlation matrix structure.
To identify multiple associations between subject clusters and brain region subnetworks in a data-driven manner.
To overcome the limitations of data vectorization in existing clustering techniques.

Main Methods:

Developed a novel multiple clustering method utilizing Wishart mixture models.
Preserved the integrity of the correlation matrix structure by avoiding vectorization.
Optimized clustering based on specific brain networks (regions of interest, ROIs) in a data-driven approach.
Addressed the independence assumption among subnetworks through whitening correlation matrices.

Main Results:

The proposed method successfully preserved the correlation matrix structure without vectorization.
Demonstrated the ability to identify multiple underlying associations between subject clusters and ROI subnetworks.
Validated the method's effectiveness on both synthetic and real fMRI data.

Conclusions:

The novel Wishart mixture model-based multiple clustering method offers a powerful approach for analyzing fMRI data.
This method enhances the identification of subject heterogeneity and brain network relationships.
The data-driven network optimization and preservation of correlation structure represent significant advancements in neuroimaging analysis.