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Updated: Jul 17, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Characterizing patterns of diffusion tensor imaging variance in aging brains.

Chenyu Gao¹, Qi Yang², Michael E Kim²

¹Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States.

Medrxiv : the Preprint Server for Health Sciences

|September 4, 2023

Summary

This summary is machine-generated.

Understanding diffusion tensor imaging (DTI) variability is crucial for large studies. We analyzed DTI data from 1035 subjects, finding that factors like age, sex, and motion influence DTI metric variance across brain regions.

Keywords:

DTI aging brain motion variance

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Area of Science:

Neuroimaging
Biostatistics
Medical Physics

Background:

Large-scale neuroimaging analyses require understanding data variance.
Diffusion Tensor Imaging (DTI) noise varies spatially, necessitating careful statistical assumptions.
Physiology, subject compliance, and scanner interactions impact DTI variability.

Purpose of the Study:

To characterize the role of physiology, subject compliance, and scanner interaction in DTI variability.
To model DTI variability using spatial variance of derived metrics in homogeneous regions.
To investigate how covariates influence DTI metric variance across different brain regions.

Main Methods:

Analysis of DTI data from 1035 subjects (ages 22.4-103) in the Baltimore Longitudinal Study of Aging (BLSA).
Assessment of DTI scalar variance within regions of interest (ROIs) defined by four segmentation methods.
Investigation of relationships between variance and covariates: age, time interval, motion, sex, and scan session.

Main Results:

Covariate effects on DTI variance are heterogeneous and bilaterally symmetric across ROIs.
Inter-session interval, sex, and motion significantly relate to fractional anisotropy (FA) variance in specific brain regions.
Head motion increases during DTI rescans.

Conclusions:

The influence of covariates on DTI variance is complex and region-dependent.
Researchers should report variance estimates and consider heteroscedasticity models in DTI analyses.
This study provides a basis for planning studies that account for regional DTI metric variance.