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通过全脑建模推断中风病变的动力学影响。

Inferring the dynamical effects of stroke lesions through whole-brain modeling.

机构信息

Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, Barcelona, Catalonia 08005, Spain.

Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, Padova 35129, Italy; Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, Padova 35128, Italy.

出版信息

Neuroimage Clin. 2022;36:103233. doi: 10.1016/j.nicl.2022.103233. Epub 2022 Oct 17.

Abstract

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.

摘要

传统上,理解局灶性病变(中风)对大脑结构-功能的影响依赖于行为分析和与神经影像学数据的相关性。在这里,我们使用个体病变的结构分离图来推导出一个因果机制生成的全脑模型,该模型能够解释中风引起的功能连接改变和行为缺陷。与仅使用局部病变信息的其他模型相比,当考虑广泛的结构分离信息时,与经验 fMRI 连接的相似性增加。与其他类型的信息(例如功能连接)相比,所提出的模型能够以更高的准确性对行为损伤的严重程度进行分类。我们评估了描述损伤功能影响的拓扑度量。通过获得的结果,我们能够了解复杂大脑系统的基础中风损伤后,网络动态如何以非平凡的方式发生变化。这种类型的建模,包括结构分离信息,有助于加深我们对中风病变潜在机制的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6e23/9668672/15a28a3a9885/gr1.jpg

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