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Journal of Data Science


Multi-Dimensional Clustering Based on Restricted Distance-Dependent Mixture Dirichlet Process for Diffusion Tensor Imaging
Volume 22, Issue 4 (2024), pp. 537–557
Pub. online: 2 April 2024      Type: Statistical Data Science      Open accessOpen Access
Received
22 September 2023
Accepted
12 March 2024
Published
2 April 2024

Abstract

Brain imaging research poses challenges due to the intricate structure of the brain and the absence of clearly discernible features in the images. In this study, we propose a technique for analyzing brain image data identifying crucial regions relevant to patients’ conditions, specifically focusing on Diffusion Tensor Imaging data. Our method utilizes the Bayesian Dirichlet process prior incorporating generalized linear models, that enhances clustering performance while it benefits from the flexibility of accommodating varying numbers of clusters. Our approach improves the performance of identifying potential classes utilizing locational information by considering the proximity between locations as clustering constraints. We apply our technique to a dataset from Transforming Research and Clinical Knowledge in Traumatic Brain Injury study, aiming to identify important regions in the brain’s gray matter, white matter, and overall brain tissue that differentiate between young and old age groups. Additionally, we explore a link between our discoveries and the existing outcomes in the field of brain network research.

Supplementary material

 Supplementary Material
Supplementary Materials include a MCMC algorithm for RDMDP method, a simulation study for 100 replications, explanation of the connection between identified brain clusters and the region of interest, and the statement regarding R code for the RDMDP method.

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2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Keywords
adjacency matrix Bayesian Dirichlet process prior brain imaging clustering pattern recognition

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