Disentangling poststroke cognitive deficits and their neuroanatomical correlates through combined multivariable and multioutcome lesion-symptom mapping

通过结合多变量和多结局的病灶-症状映射,解析卒中后认知缺陷及其神经解剖学相关性。

阅读:1

Abstract

Studies in patients with brain lesions play a fundamental role in unraveling the brain's functional anatomy. Lesion-symptom mapping (LSM) techniques can relate lesion location to cognitive performance. However, a limitation of current LSM approaches is that they can only evaluate one cognitive outcome at a time, without considering interdependencies between different cognitive tests. To overcome this challenge, we implemented canonical correlation analysis (CCA) as combined multivariable and multioutcome LSM approach. We performed a proof-of-concept study on 1075 patients with acute ischemic stroke to explore whether addition of CCA to a multivariable single-outcome LSM approach (support vector regression) could identify infarct locations associated with deficits in three well-defined verbal memory functions (encoding, consolidation, retrieval) based on four verbal memory subscores derived from the Seoul Verbal Learning Test (immediate recall, delayed recall, recognition, learning ability). We evaluated whether CCA could extract cognitive score patterns that matched prior knowledge of these verbal memory functions, and if these patterns could be linked to more specific infarct locations than through single-outcome LSM alone. Two of the canonical modes identified with CCA showed distinct cognitive patterns that matched prior knowledge on encoding and consolidation. In addition, CCA revealed that each canonical mode was linked to a distinct infarct pattern, while with multivariable single-outcome LSM individual verbal memory subscores were associated with largely overlapping patterns. In conclusion, our findings demonstrate that CCA can complement single-outcome LSM techniques to help disentangle cognitive functions and their neuroanatomical correlates.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。