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Updated: Sep 11, 2025
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI
Ying Liao1,2, Santiago Coelho1,2, Jenny Chen1,2
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States.
Abstract:
Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here, we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation, and integrity of axons. We demonstrate that using a machine learning-based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia, and multiple sclerosis (total N = 821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging, and pathology.
Insights
This study developed a new diffusion MRI method to map brain white matter microstructure. The advanced model accurately captures axonal changes in development and neurological disorders like multiple sclerosis.
Area of Science:
- Neuroimaging
- Biophysics
- Machine Learning
Background:
- Diffusion MRI provides in vivo insights into brain white matter microstructure.
- Understanding microstructural changes is crucial for development, aging, and neurological disorders.
- Current challenges include developing specific biomarkers from short MRI scans.
Purpose of the Study:
- To quantify the sensitivity and specificity of a multicompartment diffusion modeling framework.
- To assess the model's ability to detect axonal density, orientation, and integrity.
- To validate the model's performance in capturing morphological changes in various neurological conditions.
Main Methods:
- Utilized a multicompartment diffusion modeling framework.
- Employed a machine learning-based estimator for microstructure mapping.
- Applied the methodology to human MRI data from healthy individuals and patients with neurological disorders (N=821).
Main Results:
- Demonstrated high sensitivity and specificity of the diffusion modeling framework.
- Successfully captured axonal morphological changes during early development.
- Identified microstructural alterations in acute ischemia and multiple sclerosis.
Conclusions:
- The developed biophysical model accurately maps axonal microstructure using diffusion MRI.
- This methodology is sensitive to key microstructural features and applicable in clinical settings.
- The approach holds promise for studying brain development, aging, and neurological pathologies in large datasets.
