Laboratory of Systems Neuroscience and Imaging in Psychiatry (SNIP-Lab), Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Von-Siebold Str. 5, 37075, Göttingen, Germany.
Functional Imaging Laboratory, German Primate Center - Leibniz Institute for Primate Research, Göttingen, Germany.
Sci Rep. 2023 Aug 3;13(1):12615. doi: 10.1038/s41598-023-38946-5.
Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of neuropsychiatric disorders, including major depression. However, it is not yet understood how rTMS alters brain's functional connectivity. Here we report changes in functional connectivity captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10 Hz rTMS. We apply subject-specific parcellation schemes to detect changes (1) in network nodes, where the strongest functional connectivity of regions is observed, and (2) in network boundaries, where functional transitions between regions occur. We use support vector machine (SVM), a widely used machine learning algorithm that is robust and effective, for the classification and characterization of time intervals of changes in node and boundary maps. Our results reveal that changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. These results were strongest in the posterior cingulate cortex and precuneus. As network boundaries are indeed under-investigated in comparison to nodes in connectomics research, our results highlight their contribution to functional adjustments to rTMS.
重复经颅磁刺激（rTMS）在治疗神经精神疾病方面具有重要意义，包括重度抑郁症。然而，目前尚不清楚 rTMS 如何改变大脑的功能连接。在这里，我们报告了在接受 10Hz rTMS 刺激后的第一个小时内，静息态功能磁共振成像（rsfMRI）捕捉到的功能连接变化。我们应用基于个体的分割方案来检测网络节点（观察到区域最强功能连接的位置）和网络边界（区域之间的功能转换位置）的变化。我们使用支持向量机（SVM），这是一种广泛使用的机器学习算法，具有强大和有效的特点，用于分类和描述节点和边界图的变化时间间隔。我们的结果表明，边界处的连接变化比节点处的变化更慢且更复杂，但根据准确性置信区间，其变化幅度相似。这些结果在后扣带回和楔前叶最为明显。由于网络边界在连接组学研究中与节点相比研究较少，因此我们的结果强调了它们对 rTMS 功能调整的贡献。
