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Discriminating VCID subgroups: A diffusion MRI multi-model fusion approach.

Rajikha Raja¹, Arvind Caprihan², Gary A Rosenberg³

¹The Mind Research Network, Albuquerque, NM 87106, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, USA.

Journal of Neuroscience Methods

|February 1, 2020

Summary

This summary is machine-generated.

A novel data fusion approach using multiple diffusion MRI models significantly improves the detection of white matter changes in vascular cognitive impairment and dementia (VCID) subgroups. This enhanced sensitivity aids in differentiating between disease types, offering better diagnostic potential.

Keywords:

Constrained spherical deconvolution model Diffusion kurtosis imaging Diffusion tensor imaging Fusion Tractography Vascular cognitive impairment and dementia

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Area of Science:

Neuroimaging
Medical Physics
Biomedical Engineering

Background:

Vascular cognitive impairment and dementia (VCID) and Alzheimer's disease are leading causes of cognitive decline in aging populations.
Diffusion weighted MRI (DW-MRI) shows promise for diagnosing these conditions, but metric interpretation varies with the diffusion model used.
Previous studies often rely on single diffusion models, potentially limiting diagnostic accuracy.

Purpose of the Study:

To develop and evaluate a data fusion framework for DW-MRI to improve the detection of white matter microstructural changes in VCID.
To compare the diagnostic performance of individual diffusion models against a multi-model fusion approach.

Main Methods:

Employed a data fusion framework combining diffusion metrics from diffusion tensor imaging, diffusion kurtosis imaging, and constrained spherical deconvolution models.
Utilized a blind source separation approach to fuse data from different diffusion models.
Performed group comparisons and prediction analyses between controls and VCID subgroups using individual and fused diffusion features.

Main Results:

Individual diffusion models distinguished controls from disease groups but failed to differentiate between VCID subgroups.
The proposed multi-model fusion approach successfully differentiated between VCID subgroups.
Significant white matter tract differences were identified in the superior longitudinal fasciculus, anterior thalamic radiation, arcuate fasciculus, optic radiation, and corticospinal tract.

Conclusions:

Multi-model fusion of DW-MRI data enhances sensitivity in discriminating between VCID subgroups.
The fusion approach demonstrated superior diagnostic performance (AUC = 0.913) compared to single-model features (AUC = 0.77).
This method offers improved diagnostic capabilities for neurodegenerative diseases characterized by white matter abnormalities.