Computational Neuroimaging Group, Biocruces-Bizkaia Health Research Institute, Biocruces Bizkaia, Plaza de Cruces S/N, 48903, Barakaldo, Spain.
Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain.
Sci Rep. 2022 Dec 27;12(1):22400. doi: 10.1038/s41598-022-26945-x.
Beyond the characteristics of a brain lesion, such as its etiology, size or location, lesion network mapping (LNM) has shown that similar symptoms after a lesion reflects similar dis-connectivity patterns, thereby linking symptoms to brain networks. Here, we extend LNM by using a multimodal strategy, combining functional and structural networks from 1000 healthy participants in the Human Connectome Project. We apply multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests. Results are two-fold. First, multimodal LNM reveals that the functional modality contributes more than the structural one in the prediction of sensorimotor behavior. Second, when looking at each modality individually, the performance of the structural networks strongly depended on whether sensorimotor performance was corrected for lesion size, thereby eliminating the effect that larger lesions generally produce more severe sensorimotor impairment. In contrast, functional networks provided similar performance regardless of whether or not the effect of lesion size was removed. Overall, these results support the extension of LNM to its multimodal form, highlighting the synergistic and additive nature of different types of network modalities, and their corresponding influence on behavioral performance after brain injury.
除了脑损伤的特征，如病因、大小或位置外，损伤网络映射（LNM）已经表明，损伤后类似的症状反映了类似的不连通模式，从而将症状与大脑网络联系起来。在这里，我们通过使用一种多模态策略来扩展 LNM，该策略结合了来自人类连接组计划的 1000 名健康参与者的功能和结构网络。我们将多模态 LNM 应用于 54 名中风患者的队列中，目的是预测通过运动和感觉测试组合评估的感觉运动行为。结果有两个方面。首先，多模态 LNM 表明，在预测感觉运动行为方面，功能模态的贡献大于结构模态。其次，当分别观察每个模态时，结构网络的性能强烈依赖于是否校正了感觉运动表现对损伤大小的影响，从而消除了较大损伤通常会导致更严重的感觉运动障碍的影响。相比之下，功能网络提供了相似的性能，无论是否去除损伤大小的影响。总的来说，这些结果支持将 LNM 扩展到其多模态形式，突出了不同类型网络模态的协同和附加性质，以及它们对脑损伤后行为表现的相应影响。
