Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients
Authors: Petersen Marvin , Coenen Mirthe , DeCarli Charles , De Luca Alberto , van der Lelij Ewoud ,   Alzheimer’s Disease Neuroimaging Initiative, Barkhof Frederik , Benke Thomas , Chen Christopher P. L. H. , Dal-Bianco Peter , Dewenter Anna , Duering Marco , Enzinger Christian , Ewers Michael , Exalto Lieza G. , Fletcher Evan F. , Franzmeier Nicolai , Hilal Saima , Hofer Edith , Koek Huiberdina L. , Maier Andrea B. , Maillard Pauline M. , McCreary Cheryl R. , Papma Janne M. , Pijnenburg Yolande A. L. , Schmidt Reinhold , Smith Eric E. , Steketee Rebecca M. E. , van den Berg Esther , van der Flier Wiesje M. , Venkatraghavan Vikram , Venketasubramanian Narayanaswamy , Vernooij Meike W. , Wolters Frank J. , Xu Xin , Horn Andreas , Patil Kaustubh R. , Eickhoff Simon B. , Thomalla Götz , Biesbroek J. Matthijs , Biessels Geert Jan , Cheng Bastian 
Published: March 2024

Introduction

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks.

Methods & results

We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance.

Conclusion

Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.