Related Experiment Video
Updated: Aug 4, 2026
12:21
Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
Published on: September 12, 2011
25.3K
Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter
Andrea Mendez Colmenares1,2, Michelle B Hefner1, Vince D Calhoun3
1BRAiN Laboratory, Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO, United States.
Frontiers in Neurology
|May 4, 2023
Summary
This study introduces a new method, mCCA+jICA, to analyze white matter aging using all diffusion tensor imaging (DTI) parameters simultaneously. This approach reveals cognitive links in aging white matter previously missed by individual DTI analysis.
Area of Science:
- Neuroimaging
- White Matter Microstructure Analysis
- Aging Brain Research
Background:
- Diffusion Tensor Imaging (DTI) is widely used for white matter (WM) studies, with fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) showing changes in aging and disease.
- Current DTI analysis often examines parameters individually, limiting insights into WM pathology and cognitive correlations.
- A simultaneous, data-driven approach is needed to fully leverage DTI data for understanding WM changes.
Purpose of the Study:
- To present the first application of symmetric fusion, specifically multiset canonical correlation analysis with joint independent component analysis (mCCA+jICA), for studying age-related white matter microstructure.
- To simultaneously examine age differences across multiple DTI parameters.
- To identify cognitively relevant multimodal components in white matter.
Main Methods:
- Application of a novel data-driven approach, mCCA+jICA, to diffusion tensor imaging (DTI) data.
- Analysis of cognitively healthy adults across two age groups (20-33 and 60-79 years).
- Simultaneous examination of four DTI parameters (FA, MD, RD, Axial Diffusivity - AD) to identify modality-shared components.
Main Results:
- A high-stability component revealed co-variant age differences in radial diffusivity (RD) and axial diffusivity (AD) in key white matter tracts (corpus callosum, internal capsule, prefrontal WM).
- The identified component's mixing coefficients correlated with processing speed and fluid abilities, surpassing the sensitivity of unimodal DTI analyses.
- This multimodal approach successfully identified cognitively relevant patterns in white matter microstructure.
Conclusions:
- mCCA+jICA offers a powerful data-driven method for identifying cognitively relevant multimodal components within white matter.
- This approach enhances the understanding of white matter aging by integrating information across multiple DTI parameters.
- Future extensions to clinical populations and other MRI techniques could improve diagnostic classification of white matter diseases.
