Mapping Language Networks Using the Structural and Dynamic Brain Connectomes
Mapping Language Networks Using the Structural and Dynamic Brain Connectomes
复制标题
DOI：
10.1523/eneuro.0204-17.2017
复制
发表时间：
2017-09-01
期刊：
影响因子：
3.4
通讯作者：
Bonilha, Leonardo
中科院分区：
文献类型：
--
作者：
Del Gaizo, John;Fridriksson, Julius;Bonilha, Leonardo
病变-症状映射通常用于定义对人类行为至关重要的大脑结构。尽管脑卒中后的功能障碍是由灰质损伤和继发性白色物质丢失引起的，但传统的病变-症状映射忽略了结构性断开的影响，因为它不能测量脑卒中病变以外的连通性丢失。本研究描述了如何将传统的病变定位与结构连接体病变症状定位（CLSM）和连接体动力学病变症状定位（CDLSM）相结合，将残留白色网络与行为联系起来。使用来自一个大的中风幸存者失语症队列的数据，我们观察到当传统的病变症状映射与CLSM和CDLSM相结合时，失语症严重程度的预测得到改善。此外，只有CLSM和CDLSM揭示了颞顶连接在失语症严重程度中的重要性。总之，连接体测量可以独特地揭示功能所必需的大脑网络，改进传统的病变症状映射方法。
Lesion-symptom mapping is often employed to define brain structures that are crucial for human behavior. Even though poststroke deficits result from gray matter damage as well as secondary white matter loss, the impact of structural disconnection is overlooked by conventional lesion-symptom mapping because it does not measure loss of connectivity beyond the stroke lesion. This study describes how traditional lesion mapping can be combined with structural connectome lesion symptom mapping (CLSM) and connectome dynamics lesion symptom mapping (CDLSM) to relate residual white matter networks to behavior. Using data from a large cohort of stroke survivors with aphasia, we observed improved prediction of aphasia severity when traditional lesion symptom mapping was combined with CLSM and CDLSM. Moreover, only CLSM and CDLSM disclosed the importance of temporal-parietal junction connections in aphasia severity. In summary, connectome measures can uniquely reveal brain networks that are necessary for function, improving the traditional lesion symptom mapping approach.