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保存完好的解剖旁路可预测中风后语言功能的变化。

Preserved anatomical bypasses predict variance in language functions after stroke.

机构信息

Department of Psychology, Drexel University, Philadelphia, PA, USA.

Department of Psychology, Drexel University, Philadelphia, PA, USA.

出版信息

Cortex. 2022 Oct;155:46-61. doi: 10.1016/j.cortex.2022.05.023. Epub 2022 Jul 16.

Abstract

The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain. Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than the most reliably observed connections in age-matched control networks. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a principal components analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using support vector regression, a technique that extracts robust relationships in high-dimensional data analysis. The support vector regression parameters were tuned using grid-search cross-validation. We discovered that the length of bypasses reliably predicted variance in lexical production (R = .576) and auditory comprehension scores (R = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.

摘要

中风后失语症的严重程度与白质连接的损伤有关。然而,神经信号不仅可以通过直接连接传递,还可以沿着多步网络路径传递。当大脑网络因中风而受损时,路径可以绕过损伤来恢复通讯。区域之间最短的网络路径可能是介导绕过的最有效途径。我们研究了左侧大脑半球中风后最短路径绕过与语言表现之间的关系。语言网络内和语言网络外的区域在失语症恢复中可能很重要。因此,我们创新了方法来测量整个大脑中绕过的影响。如果没有中风前的影像学检查,就很难区分绕过和所有残留的最短路径。我们通过在中风患者中找到比年龄匹配的对照组网络中观察到的最可靠连接更长的最短路径来识别绕过。我们测试了这些绕过的特征是否可以预测来自一系列语言任务的主成分分析得出的四个语言表现正交维度的得分。这些特征是每个步骤中绕过的长度,以及每个直接连接上重叠的绕过数量。我们使用支持向量回归(一种在高维数据分析中提取稳健关系的技术)将这些绕过特征与语言因素相关联。支持向量回归参数使用网格搜索交叉验证进行调整。我们发现绕过的长度可靠地预测了词汇产生(R=.576)和听觉理解得分的变化(R=.164)。绕过的重叠可靠地预测了词汇产生得分的变化(R=.247)。预测的延长特征表明,沿着背侧流和腹侧流的绕过效率与词汇产生和听觉理解分别最相关。在预测性的绕过重叠中,右侧壳核的绕过路径增加与词汇产生能力呈负相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2174/11697986/c3ee4b35bea4/nihms-2042769-f0001.jpg

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