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.

中文翻译：

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通过白质高信号连接评估增强认知能力预测


推测血管起源的白质高信号 （WMH） 与认知障碍相关，是评估大脑健康的关键影像学标志物。然而，单独的 WMH 体积并不能完全解释认知缺陷的程度，并且将 WMH 与这些缺陷联系起来的机制仍不清楚。病变网络映射 （LNM） 能够推断大脑网络是否与病变相连，并且可能是一种很有前途的技术，可以增强我们对 WMH 在认知障碍中的作用的理解。我们的研究使用 LNM 来检验以下假设：（1） LNM 知情标志物在预测认知能力方面超过 WMH 体积，以及 （2） WMH 导致认知障碍映射到特定大脑网络。我们分析了来自 Meta VCI Map Consortium 内 10 个记忆诊所队列的 3,485 名患者的横断面数据，在 4 个认知领域和 WMH 分割中使用统一的测试结果。将 WMH 分割注册到标准空间并映射到现有的规范结构和功能脑连接组数据上。我们采用 LNM 量化 WMH 与 480 个基于图谱的灰质和白质感兴趣区域 （ROI） 的连通性，从而得到 ROI 级别的结构和功能 LNM 评分。我们在嵌套交叉验证中使用岭回归模型比较了总和区域 WMH 体积以及 LNM 评分预测认知功能的能力。LNM 评分预测三个认知领域（注意力/执行功能、信息处理速度和语言记忆）的表现明显优于 WMH 卷。LNM 分数并没有改善对语言功能的预测。 ROI 水平分析显示，在背侧和腹侧注意力网络的灰质和白质区域，较高的 LNM 分数（代表与 WMH 的联系更紧密）与较低的认知能力相关。与 WMH 体积作为脑血管疾病的传统成像标志物相比，WMH 相关脑网络连接的测量显着改善了对记忆诊所患者当前认知能力的预测。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大脑区域，提高了我们对血管对认知障碍影响的理解。展望未来，使用连接数据提炼 WMH 信息可能有助于为患者量身定制的治疗干预，并有助于识别有认知障碍风险的亚组。