Han Ziteng, Liu Tiantian, Shi Zhongyan, Zhang Jian, Suo Dingjie, Wang Li, Chen Duanduan, Wu Jinglong, Yan Tianyi
School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
School of Mechatronical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
PNAS Nexus. 2023 Aug 23;2(9):pgad276. doi: 10.1093/pnasnexus/pgad276. eCollection 2023 Sep.
The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but is also central to many disorders. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we investigated SMN heterogeneity from multiple perspectives. To characterize the SMN substructures in more detail, we used ultra-high-field functional MRI to delineate a finer-grained cortical parcellation containing 430 parcels that is more homogenous than the state-of-the-art parcellation. We personalized the new parcellation to account for individual differences and identified multiscale individual-specific brain structures. We found that the SMN subnetworks showed distinct resting-state functional connectivity (RSFC) patterns. The Hand subnetwork was central within the SMN and exhibited stronger RSFC with the attention systems than the other subnetworks, whereas the Tongue subnetwork exhibited stronger RSFC with the default systems. This two-fold differentiation was observed in the temporal ordering patterns within the SMN. Furthermore, we characterized how the distinct attention and default streams were carried forward into the functions of the SMN using dynamic causal modeling and identified two behavioral domains associated with this SMN fractionation using meta-analytic tools. Overall, our findings provided important insights into the heterogeneous SMN organization at the system level and suggested that the Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.
体感运动网络（SMN）不仅在初级体感和运动处理中发挥重要作用，而且在许多疾病中也起着核心作用。然而，与高阶系统相关的SMN异质性仍不清楚。在此，我们从多个角度研究了SMN异质性。为了更详细地描述SMN子结构，我们使用超高场功能磁共振成像来描绘一个包含430个脑区的更精细的皮质分区，该分区比现有技术的分区更均匀。我们对新的分区进行个性化处理以考虑个体差异，并识别出多尺度个体特异性脑结构。我们发现，SMN子网显示出不同的静息态功能连接（RSFC）模式。手部子网位于SMN的中心，与其他子网相比，它与注意力系统表现出更强的RSFC，而舌部子网与默认系统表现出更强的RSFC。在SMN内的时间排序模式中观察到这种双重分化。此外，我们使用动态因果模型描述了不同的注意力和默认信息流如何向前传递到SMN的功能中，并使用元分析工具识别了与这种SMN分离相关的两个行为域。总体而言，我们的研究结果为系统层面的异质性SMN组织提供了重要见解，并表明手部子网可能优先参与外源性过程，而舌部子网在内源性过程中可能更重要。
