Related Experiment Video
Updated: Mar 5, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Dissociable diffusion MRI patterns of white matter microstructure and connectivity in Alzheimer's disease spectrum
Nhat Trung Doan1, Andreas Engvig1,2, Karin Persson3,4
1NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital &Institute of Clinical Medicine, University of Oslo, Norway.
Abstract:
Recent efforts using diffusion tensor imaging (DTI) have documented white matter (WM) alterations in Alzheimer's disease (AD). The full potential of whole-brain DTI, however, has not been fully exploited as studies have focused on individual microstructural indices independently. In patients with AD (n = 79), mild (MCI, n = 55) and subjective (SCI, n = 30) cognitive impairment, we applied linked independent component analysis (LICA) to model inter-subject variability across five complementary DTI measures (fractional anisotropy (FA), axial/radial/mean diffusivity, diffusion tensor mode), two crossing fiber measures estimated using a multi-compartment crossing-fiber model reflecting the volume fraction of the dominant (f1) and non-dominant (f2) diffusion orientation, and finally, connectivity density obtained from full-brain probabilistic tractography. The LICA component explaining the largest data variance was highly sensitive to disease severity (AD < MCI < SCI) and revealed widespread coordinated decreases in FA and f1 with increases in all diffusivity measures in AD. Additionally, it reflected regional coordinated decreases and increases in f2, mode and connectivity density, implicating bidirectional alterations of crossing fibers in the fornix, uncinate fasciculi, corpus callosum and major sensorimotor pathways. LICA yielded improved diagnostic classification performance compared to univariate region-of-interest features. Our results document coordinated WM microstructural and connectivity alterations in line with disease severity across the AD continuum.
Insights
White matter alterations in Alzheimer's disease (AD) continuum are linked to disease severity. Linked independent component analysis (LICA) revealed coordinated microstructural and connectivity changes, improving diagnostic classification.
Area of Science:
- Neuroimaging
- Neurology
- Biomedical Engineering
Background:
- White matter (WM) alterations are documented in Alzheimer's disease (AD) using diffusion tensor imaging (DTI).
- Previous studies often analyzed microstructural indices independently, limiting the full potential of whole-brain DTI.
- Understanding WM changes across the AD continuum (AD, mild cognitive impairment (MCI), subjective cognitive impairment (SCI)) is crucial.
Purpose of the Study:
- To apply linked independent component analysis (LICA) to model inter-subject variability across multiple DTI measures.
- To investigate coordinated white matter microstructural and connectivity alterations across the AD continuum.
- To assess the diagnostic classification performance of LICA compared to univariate methods.
Main Methods:
- Utilized linked independent component analysis (LICA) on DTI data from patients with AD (n=79), MCI (n=55), and SCI (n=30).
- Analyzed five DTI measures (FA, axial/radial/mean diffusivity, diffusion tensor mode) and two crossing fiber measures (f1, f2) from a multi-compartment model.
- Incorporated connectivity density from full-brain probabilistic tractography.
Main Results:
- The primary LICA component showed high sensitivity to disease severity (AD < MCI < SCI).
- Observed coordinated decreases in fractional anisotropy (FA) and dominant crossing fiber fraction (f1), with increases in diffusivity measures in AD.
- Identified regional coordinated alterations in non-dominant crossing fiber fraction (f2), mode, and connectivity density, indicating bidirectional changes in crossing fibers.
Conclusions:
- LICA effectively models coordinated white matter microstructural and connectivity alterations across the AD continuum.
- These findings highlight bidirectional changes in crossing fibers and their correlation with disease severity.
- LICA demonstrated improved diagnostic classification performance compared to traditional univariate approaches.
