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Updated: Jul 9, 2025
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Spatial distributions of white matter hyperintensities on brain MRI: A pooled analysis of individual participant data
Mirthe Coenen1, Geert Jan Biessels1, Charles DeCarli2
1Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, Utrecht, the Netherlands.
Introduction:
The spatial distribution of white matter hyperintensities (WMH) on MRI is often considered in the diagnostic evaluation of patients with cognitive problems. In some patients, clinicians may classify WMH patterns as "unusual", but this is largely based on expert opinion, because detailed quantitative information about WMH distribution frequencies in a memory clinic setting is lacking. Here we report voxel wise 3D WMH distribution frequencies in a large multicenter dataset and also aimed to identify individuals with unusual WMH patterns.
Methods:
Individual participant data (N = 3525, including 777 participants with subjective cognitive decline, 1389 participants with mild cognitive impairment and 1359 patients with dementia) from eleven memory clinic cohorts, recruited through the Meta VCI Map Consortium, were used. WMH segmentations were provided by participating centers or performed in Utrecht and registered to the Montreal Neurological Institute (MNI)-152 brain template for spatial normalization. To determine WMH distribution frequencies, we calculated WMH probability maps at voxel level. To identify individuals with unusual WMH patterns, region-of-interest (ROI) based WMH probability maps, rule-based scores, and a machine learning method (Local Outlier Factor (LOF)), were implemented.
Results:
WMH occurred in 82% of voxels from the white matter template with large variation between subjects. Only a small proportion of the white matter (1.7%), mainly in the periventricular areas, was affected by WMH in at least 20% of participants. A large portion of the total white matter was affected infrequently. Nevertheless, 93.8% of individual participants had lesions in voxels that were affected in less than 2% of the population, mainly located in subcortical areas. Only the machine learning method effectively identified individuals with unusual patterns, in particular subjects with asymmetric WMH distribution or with WMH at relatively rarely affected locations despite common locations not being affected.
Discussion:
Aggregating data from several memory clinic cohorts, we provide a detailed 3D map of WMH lesion distribution frequencies, that informs on common as well as rare localizations. The use of data-driven analysis with LOF can be used to identify unusual patterns, which might serve as an alert that rare causes of WMH should be considered.
Insights
This study mapped white matter hyperintensities (WMH) distribution in memory clinic patients. A machine learning approach identified unusual WMH patterns, potentially indicating rare underlying causes.
Area of Science:
- Neuroimaging
- Neurology
- Medical Informatics
Background:
- Spatial distribution of white matter hyperintensities (WMH) is crucial for diagnosing cognitive impairment.
- Current classification of WMH patterns relies on subjective expert opinion due to lack of quantitative data.
- This study addresses the need for detailed, quantitative WMH distribution frequencies in memory clinic populations.
Purpose of the Study:
- To create detailed 3D voxel-wise WMH distribution frequency maps from a large, multicenter memory clinic dataset.
- To identify individuals with unusual WMH patterns using quantitative methods.
- To provide a data-driven foundation for interpreting WMH patterns in clinical settings.
Main Methods:
- Utilized individual participant data from 3525 individuals across eleven memory clinic cohorts (Meta VCI Map Consortium).
- Performed spatial normalization of WMH segmentations to the MNI-152 brain template.
- Employed voxel-level WMH probability maps, region-of-interest (ROI) based probability maps, rule-based scores, and Local Outlier Factor (LOF) machine learning for pattern identification.
Main Results:
- WMH were present in 82% of white matter voxels, with significant inter-subject variation.
- Only 1.7% of white matter, primarily periventricular, showed WMH in over 20% of participants.
- The LOF machine learning method effectively identified individuals with unusual WMH patterns, including asymmetry and rare lesion locations.
Conclusions:
- The study provides a comprehensive 3D map of WMH distribution frequencies, highlighting common and rare localizations.
- Data-driven analysis using LOF can reliably detect unusual WMH patterns.
- Identification of unusual WMH patterns may prompt consideration of rare underlying etiologies.
