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001024693 0247_ $$2doi$$a10.1101/2024.03.28.24305007
001024693 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-02366
001024693 037__ $$aFZJ-2024-02366
001024693 1001_ $$0P:(DE-HGF)0$$aPetersen, Marvin$$b0$$eCorresponding author
001024693 245__ $$aEnhancing Cognitive Performance Prediction through White Matter Hyperintensity Disconnectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients
001024693 260__ $$c2024
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001024693 520__ $$aIntroduction: 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), enabling the inference of brain networks disconnected by lesions, represents 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: WMH-related brain network disconnectivity significantly improves 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.
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001024693 7001_ $$0P:(DE-HGF)0$$aCoenen, Mirthe$$b1
001024693 7001_ $$0P:(DE-HGF)0$$aDeCarli, Charles$$b2
001024693 7001_ $$0P:(DE-HGF)0$$aDe Luca, Alberto$$b3
001024693 7001_ $$0P:(DE-HGF)0$$avan der Lelij, Ewoud$$b4
001024693 7001_ $$0P:(DE-HGF)0$$aBarkhof, Frederik$$b5
001024693 7001_ $$0P:(DE-HGF)0$$aBenke, Thomas$$b6
001024693 7001_ $$0P:(DE-HGF)0$$aChen, Christopher P. L. H.$$b7
001024693 7001_ $$0P:(DE-HGF)0$$aDal-Bianco, Peter$$b8
001024693 7001_ $$0P:(DE-HGF)0$$aDewenter, Anna$$b9
001024693 7001_ $$0P:(DE-HGF)0$$aDuering, Marco$$b10
001024693 7001_ $$0P:(DE-HGF)0$$aEnzinger, Christian$$b11
001024693 7001_ $$0P:(DE-HGF)0$$aEwers, Michael$$b12
001024693 7001_ $$0P:(DE-HGF)0$$aExalto, Lieza G.$$b13
001024693 7001_ $$0P:(DE-HGF)0$$aFletcher, Evan F.$$b14
001024693 7001_ $$0P:(DE-HGF)0$$aFranzmeier, Nicolai$$b15
001024693 7001_ $$0P:(DE-HGF)0$$aHilal, Saima$$b16
001024693 7001_ $$0P:(DE-HGF)0$$aHofer, Edith$$b17
001024693 7001_ $$0P:(DE-HGF)0$$aKoek, Huiberdina L.$$b18
001024693 7001_ $$0P:(DE-HGF)0$$aMaier, Andrea B.$$b19
001024693 7001_ $$0P:(DE-HGF)0$$aMaillard, Pauline M.$$b20
001024693 7001_ $$0P:(DE-HGF)0$$aMcCreary, Cheryl R.$$b21
001024693 7001_ $$0P:(DE-HGF)0$$aPapma, Janne M.$$b22
001024693 7001_ $$0P:(DE-HGF)0$$aPijnenburg, Yolande A. L.$$b23
001024693 7001_ $$0P:(DE-HGF)0$$aRubinski, Anna$$b24
001024693 7001_ $$0P:(DE-HGF)0$$aSchmidt, Reinhold$$b25
001024693 7001_ $$0P:(DE-HGF)0$$aSmith, Eric E.$$b26
001024693 7001_ $$0P:(DE-HGF)0$$aSteketee, Rebecca M. E.$$b27
001024693 7001_ $$0P:(DE-HGF)0$$avan den Berg, Esther$$b28
001024693 7001_ $$0P:(DE-HGF)0$$avan der Flier, Wiesje M.$$b29
001024693 7001_ $$0P:(DE-HGF)0$$aVenkatraghavan, Vikram$$b30
001024693 7001_ $$0P:(DE-HGF)0$$aVernooij, Meike W.$$b31
001024693 7001_ $$0P:(DE-HGF)0$$aVenketasubramanian, Narayanaswamy$$b32
001024693 7001_ $$0P:(DE-HGF)0$$aWolters, Frank J.$$b33
001024693 7001_ $$0P:(DE-HGF)0$$aXin, Xu$$b34
001024693 7001_ $$0P:(DE-HGF)0$$aHorn, Andreas$$b35
001024693 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b36
001024693 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b37
001024693 7001_ $$0P:(DE-HGF)0$$aThomalla, Götz$$b38
001024693 7001_ $$0P:(DE-HGF)0$$aBiesbroek, J. Matthijs$$b39
001024693 7001_ $$0P:(DE-HGF)0$$aBiessels, Geert Jan$$b40
001024693 7001_ $$0P:(DE-HGF)0$$aCheng, Bastian$$b41
001024693 773__ $$a10.1101/2024.03.28.24305007
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