Li Mingyang, Song Luping, Zhang Yumei, Han Zaizhu
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
Shenzhen University General Hospital, Department of Rehabilitation Medicine, Shenzhen 518055, China.
iScience. 2021 Jul 16;24(8):102862. doi: 10.1016/j.isci.2021.102862. eCollection 2021 Aug 20.
Oral word reading is supported by a neural subnetwork that includes gray matter regions and white matter tracts connected by the regions. Traditional methods typically determine the reading-relevant focal gray matter regions or white matter tracts rather than the reading-relevant global subnetwork. The present study developed a network-based lesion-symptom mapping (NLSM) method to identify the reading-relevant global white matter subnetwork in 84 brain-damaged patients. The global subnetwork was selected among all possible subnetworks because its global efficiency exhibited the best explanatory power for patients' reading scores. This reading subnetwork was left lateralized and included 7 gray matter regions and 15 white matter tracts. Moreover, the reading subnetwork had additional explanatory power for the patients' reading performance after eliminating the effects of reading-related local regions and tracts. These findings refine the reading neuroanatomical architecture and indicate that the NLSM can be a better method for revealing behavior-specific subnetworks.
口语阅读由一个神经子网支持，该子网包括灰质区域以及由这些区域连接的白质束。传统方法通常确定与阅读相关的局部灰质区域或白质束，而非与阅读相关的全局子网。本研究开发了一种基于网络的损伤-症状映射（NLSM）方法，以识别84名脑损伤患者中与阅读相关的全局白质子网。该全局子网是从所有可能的子网中挑选出来的，因为其全局效率对患者的阅读分数具有最佳解释力。这个阅读子网位于左侧，包括7个灰质区域和15条白质束。此外，在消除与阅读相关的局部区域和束的影响后，该阅读子网对患者的阅读表现具有额外的解释力。这些发现完善了阅读神经解剖结构，并表明NLSM可能是揭示特定行为子网的更好方法。
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