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人类高级运动控制的网络基础:基于机器学习的模仿性失用症病灶-行为映射的见解。

A network underlying human higher-order motor control: Insights from machine learning-based lesion-behaviour mapping in apraxia of pantomime.

机构信息

Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.

Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.

出版信息

Cortex. 2019 Dec;121:308-321. doi: 10.1016/j.cortex.2019.08.023. Epub 2019 Oct 4.

DOI:10.1016/j.cortex.2019.08.023
PMID:31669979
Abstract

Neurological patients with apraxia of pantomime provide us with a unique opportunity to study the neural correlates of higher-order motor function. Previous studies using lesion-behaviour mapping methods led to inconsistent anatomical results, reporting various lesion locations to induce this symptom. We hypothesised that the inconsistencies might arise from limitations of mass-univariate lesion-behaviour mapping approaches if our ability to pantomime the use of objects is organised in a brain network. Thus, we investigated apraxia of pantomime by using multivariate lesion behaviour mapping based both on support vector regression and sparse canonical correlations in a sample of 130 left-hemisphere stroke patients. Both multivariate methods identified multiple areas to underlie high-order motor control, including inferior parietal lobule, precentral gyrus, posterior parts of middle temporal cortex, and insula. Further, long association fibres were affected, such as the superior longitudinal fascicle, inferior occipito-frontal fascicle, uncinated fascicle, and superior occipito-frontal fascicle. The findings underline the benefits of multivariate lesion-behaviour mapping in brain networks and provide new insights into the brain networks underlying higher-order motor control.

摘要

失用症患者为我们研究高级运动功能的神经相关性提供了独特的机会。先前使用病变-行为映射方法的研究得出了不一致的解剖结果,报告了各种病变位置会导致这种症状。我们假设,如果我们模仿使用物体的能力是在大脑网络中组织的,那么基于支持向量回归和稀疏典型相关的多元病变行为映射方法的局限性可能会导致不一致。因此,我们在 130 名左侧半球中风患者的样本中,通过使用基于支持向量回归和稀疏典型相关的多元病变行为映射方法,研究了失用症。这两种多元方法都确定了多个区域来作为高级运动控制的基础,包括下顶叶、中央前回、中颞叶后部和脑岛。此外,还影响了长的联合纤维,如上纵束、下额枕束、钩束和上额枕束。这些发现强调了多元病变行为映射在大脑网络中的优势,并为高级运动控制的大脑网络提供了新的见解。

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