Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment

认知 高强度 睡眠剥夺对认知功能的影响 白质 心理学 连接体 认知测验 医学 磁共振成像 神经科学 功能连接 放射科
作者
Marvin Petersen,Mirthe Coenen,Charles DeCarli,Alberto De Luca,Ewoud van der Lelij,Frederik Barkhof,Thomas Benke,Christopher Chen,Peter Dal‐Bianco,Anna Dewenter,Marco Duering,Christian Enzinger,Michael Ewers,Lieza G. Exalto,Evan Fletcher,Nicolai Franzmeier,Saima Hilal,Edith Hofer,Huiberdina L. Koek,Andrea B. Maier,Pauline Maillard,Cheryl R. McCreary,Janne M. Papma,Yolande A.L. Pijnenburg,Reinhold Schmidt,Eric E. Smith,Rebecca M. E. Steketee,Esther van den Berg,Vikram Venkatraghavan,Vikram Venkatraghavan,Narayanaswamy Venketasubramanian,Meike W. Vernooij,Frank J. Wolters,Xin Xu,Andreas Horn,Kaustubh R. Patil,Simon B. Eickhoff,Götz Thomalla,J. Matthijs Biesbroek,Geert Jan Biessels,Bastian Cheng
出处
期刊:Brain [Oxford University Press]
标识
DOI:10.1093/brain/awae315
摘要

Abstract White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain 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. 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 LNM 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. 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 to 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/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 connectivity to WMH, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. 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 integrity, 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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