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Updated: Mar 29, 2026

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

Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in

Cheng Guan Koay¹, Ping-Hong Yeh², John M Ollinger³

¹National Intrepid Center of Excellence (NICoE), Bethesda, MD, USA; Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD, USA; NorthTide Group, LLC, USA.

|December 8, 2015

Summary

This summary is machine-generated.

A new framework, Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI), enables voxel-by-voxel analysis of diffusion tensor imaging (DTI) data for single subjects. TOADDI effectively identifies significant deviations in tract orientation and shape compared to control groups, aiding in neurological condition assessment.

Keywords:

DTI Elliptical cone of uncertainty Exact Wilcoxon–Mann–Whitney p-value computation FDR FNR

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Diffusion Imaging in the Rat Cervical Spinal Cord

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Published on: April 7, 2015

Area of Science:

Neuroimaging
Biostatistics
Medical Physics

Background:

Diffusion Tensor Imaging (DTI) is crucial for analyzing white matter tracts.
Single-subject analysis of DTI data presents challenges in statistical rigor.
Existing methods may lack sensitivity for detecting subtle individual differences.

Purpose of the Study:

To develop a novel statistical framework for single-subject DTI analysis.
Introduce the Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI).
Enable voxel-by-voxel comparison of individual DTI tracts against a control group.

Main Methods:

Development of two statistical tests: orientation deviation and shape deviation.
Utilizing the elliptical cone of uncertainty to model eigenvector dispersion.
Incorporation of False Discovery Rate (FDR) and False Non-discovery Rate (FNR) for statistical control.

Main Results:

TOADDI demonstrated numerical accuracy and statistical effectiveness.
The frontal superior longitudinal fasciculus showed significant differences in TBI patients.
TBI patients and one non-TBI subject were well separated using the shape deviation test at FDR 0.0005.

Conclusions:

TOADDI provides a robust, geometrically-based framework for single-subject DTI analysis.
The method is effective in identifying significant tract differences in neurological conditions like TBI.
TOADDI is applicable to single-subject, graph-theoretic, and group DTI analyses.