학술논문
Spatial distributions of white matter hyperintensities on brain MRI: A pooled analysis of individual participant data from 11 memory clinic cohorts
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article
Author
Coenen, Mirthe; Biessels, Geert Jan; DeCarli, Charles; Fletcher, Evan F; Maillard, Pauline M; Initiative, Alzheimer's Disease Neuroimaging; Barkhof, Frederik; Barnes, Josephine; Benke, Thomas; Boomsma, Jooske MF; Chen, Christopher PLH; Dal-Bianco, Peter; Dewenter, Anna; Duering, Marco; Enzinger, Christian; Ewers, Michael; Exalto, Lieza G; Franzmeier, Nicolai; Groeneveld, Onno; Hilal, Saima; Hofer, Edith; Koek, Huiberdina L; Maier, Andrea B; McCreary, Cheryl R; Papma, Janne M; Paterson, Ross W; Pijnenburg, Yolande AL; Rubinski, Anna; Schmidt, Reinhold; Schott, Jonathan M; Slattery, Catherine F; Smith, Eric E; Sudre, Carole H; Steketee, Rebecca ME; van den Berg, Esther; van der Flier, Wiesje M; Venketasubramanian, Narayanaswamy; Vernooij, Meike W; Wolters, Frank J; Xin, Xu; Biesbroek, J Matthijs; Kuijf, Hugo J
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Subject
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Abstract
IntroductionThe 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.MethodsIndividual 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.ResultsWMH 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.DiscussionAggregating 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.