A group distributional ICA method for decomposing multi-subject diffusion tensor imaging

Diffusion tensor imaging (DTI) is a frequently used imaging modality to investigate white matter fiber connections of human brain. DTI provides an important tool for characterizing human brain structural organization. Common goals in DTI analysis include dimension reduction, denoising, and extractio...

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Bibliographic Details
Published in:Biometrics Vol. 81; no. 3
Main Authors: Yang, Guangming, Wu, Ben, Kang, Jian, Guo, Ying
Format: Journal Article
Language:English
Published: England 03.07.2025
Subjects:
Computer Simulation
Diffusion Tensor Imaging
Humans
Image Processing, Computer-Assisted - methods
Models, Statistical
Software Validation
White Matter - diagnostic imaging
brain imaging
diffusion MRI
independent component analysis
reliability analysis
blind source separation
structural networks
ISSN:0006-341X, 1541-0420, 1541-0420
Online Access:Get full text
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Summary:Diffusion tensor imaging (DTI) is a frequently used imaging modality to investigate white matter fiber connections of human brain. DTI provides an important tool for characterizing human brain structural organization. Common goals in DTI analysis include dimension reduction, denoising, and extraction of underlying structure networks. Blind source separation methods are often used to achieve these goals for other imaging modalities. However, there has been very limited work for multi-subject DTI data. Due to the special characteristics of the 3D diffusion tensor measured in DTI, existing methods such as standard independent component analysis (ICA) cannot be directly applied. We propose a Group Distributional ICA (G-DICA) method to fill this gap. G-DICA represents a fundamentally new blind source separation method that separates the parameters in the distribution function of the observed imaging data as a mixture of independent source signals. Decomposing multi-subject DTI using G-DICA uncovers structural networks corresponding to several major white matter fiber bundles in the brain. Through simulation studies and real data applications, the proposed G-DICA method demonstrates superior performance and improved reproducibility compared to the existing method.
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1093/biomtc/ujaf117