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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

Higher-order multi-shell diffusion measures complement tensor metrics and volume in gray matter when predicting age

Hamsanandini Radhakrishnan¹, Ilana J Bennett², Craig El Stark³

¹Mathematical, Computational and Systems Biology, University of California, Postal Address: 1400 Biological Sciences III, Irvine, CA 92697, United States.

|March 10, 2022

Summary

Advanced diffusion imaging metrics like Neurite Orientation Dispersion and Density Imaging (NODDI) offer valuable insights into gray matter microstructure. Combining NODDI with standard MRI metrics significantly improves predictions of aging and cognitive performance.

Keywords:

Aging Cognition Diffusion imaging Hippocampus MRI NODDI

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Last Updated: Sep 30, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

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Published on: July 28, 2013

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Area of Science:

Neuroimaging
Human Brain Imaging
Diffusion MRI

Background:

Diffusion-weighted imaging (DWI) advances allow non-invasive gray matter microstructure analysis.
Multi-shell DWI protocols, though informative, increase scan time, limiting their use in large studies.

Purpose of the Study:

To evaluate if multi-shell diffusion metrics (NODDI) provide additional value over standard tensor and volume metrics for assessing gray matter in the aging human hippocampus.
To determine if the increased scan time for multi-shell DWI is justified by enhanced predictive capabilities for cognitive aging.

Main Methods:

Utilized Neurite Orientation Dispersion and Density Imaging (NODDI) metrics on multi-shell DWI data from 79 younger and 75 older adults.
Assessed hippocampal subfield microstructural differences related to age and memory performance.
Compared the predictive power of NODDI metrics, tensor metrics, and volume, both individually and in combination, for age and cognitive ability.

Main Results:

NODDI metrics were robust and sensitive to age and memory performance in hippocampal subfields, replicating prior findings.
Both NODDI and tensor measures independently predicted age and cognition similarly.
Integrating NODDI and tensor metrics substantially enhanced the predictive accuracy of logistic models, suggesting complementary information.

Conclusions:

Multi-shell DWI protocols, including NODDI, capture unique microstructural information missed by traditional tensor and volume metrics.
Neuroimaging consortiums should consider incorporating multi-shell DWI sequences to enrich datasets for studying gray matter, even in large-scale cognitive aging research.