Petersen Marvin, Coenen Mirthe, DeCarli Charles, De Luca Alberto, van der Lelij Ewoud, 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 M, 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
Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg 20251Germany.
Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht 3584 CX, The Netherlands.
Brain. 2024 Dec 3;147(12):4265-4279. doi: 10.1093/brain/awae315.
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 us 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: (i) LNM-informed markers surpass WMH volumes in predicting cognitive performance; and (ii) WMH contributing to cognitive impairment map to specific brain networks. We analysed cross-sectional data of 3485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in four 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 grey 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 grey 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.
推定血管源性白质高信号(WMH)与认知障碍相关,是评估脑健康的关键影像学标志物。然而,仅WMH体积并不能完全解释认知缺陷的程度,且WMH与这些缺陷之间的联系机制仍不清楚。病变网络映射(LNM)使我们能够推断脑网络是否与病变相连,可能是一种增强我们对WMH在认知障碍中作用理解的有前景的技术。我们的研究采用LNM来检验以下假设:(i)基于LNM的标志物在预测认知表现方面优于WMH体积;(ii)导致认知障碍的WMH映射到特定的脑网络。我们分析了来自Meta VCI Map联盟内10个记忆门诊队列的3485例患者的横断面数据,使用了四个认知领域的统一测试结果和WMH分割。将WMH分割注册到标准空间,并映射到现有的规范性结构和功能性脑连接组数据上。我们使用LNM来量化WMH与480个基于图谱的灰、白质感兴趣区域(ROI)的连接性,得出ROI水平的结构和功能性LNM分数。我们在嵌套交叉验证中使用岭回归模型比较了总WMH体积、区域WMH体积和LNM分数预测认知功能的能力。LNM分数在预测三个认知领域(注意力/执行功能、信息处理速度和言语记忆)的表现方面显著优于WMH体积。LNM分数对语言功能的预测没有改善。ROI水平分析显示,背侧和腹侧注意力网络的灰质和白质区域中,较高的LNM分数(代表与WMH的更大连接性)与较低的认知表现相关。与作为脑血管疾病传统影像学标志物的WMH体积相比,WMH相关脑网络连接性的测量显著改善了对记忆门诊患者当前认知表现的预测。这突出了网络完整性的关键作用,特别是在与注意力相关的脑区,增进了我们对血管对认知障碍影响的理解。展望未来,用连接性数据细化WMH信息可能有助于针对患者的治疗干预,并促进对有认知障碍风险亚组的识别。