Sun Wendy, Billot Anne, Du Jingnan, Wei Xiangyu, Lemley Rachel A, Daneshzand Mohammad, Nummenmaa Aapo, Buckner Randy L, Eldaief Mark C
Division of Medical Sciences, Harvard Medical School, Boston, MA 02115.
Dept. of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138.
medRxiv. 2024 Sep 23:2024.08.15.24311994. doi: 10.1101/2024.08.15.24311994.
Higher-order cognitive and affective functions are supported by large-scale networks in the brain. Dysfunction in different networks is proposed to associate with distinct symptoms in neuropsychiatric disorders. However, the specific networks targeted by current clinical transcranial magnetic stimulation (TMS) approaches are unclear. While standard-of-care TMS relies on scalp-based landmarks, recent FDA-approved TMS protocols use individualized functional connectivity with the subgenual anterior cingulate cortex (sgACC) to optimize TMS targeting. Leveraging previous work on precision network estimation and recent advances in network-level TMS targeting, we demonstrate that clinical TMS approaches target different functional networks between individuals. Homotopic scalp positions (left F3 and right F4) target different networks within and across individuals, and right F4 generally favors a right-lateralized control network. We also modeled the impact of targeting the dorsolateral prefrontal cortex (dlPFC) zone anticorrelated with the sgACC and found that the individual-specific anticorrelated region variably targets a network coupled to reward circuitry. Combining individualized, precision network mapping and electric field (E-field) modeling, we further illustrate how modeling can be deployed to prospectively target distinct closely localized association networks in the dlPFC with meaningful spatial selectivity and E-field intensity and retrospectively assess network engagement. Critically, we demonstrate the feasibility and reliability of this approach in an independent cohort of participants (including those with Major Depressive Disorder) who underwent repeated sessions of TMS to distinct networks, with precise targeting derived from a low-burden single session of data. Lastly, our findings emphasize differences between selectivity and maximal intensity, highlighting the need to consider both metrics in precision TMS efforts.
大脑中的大规模网络支持高阶认知和情感功能。不同网络的功能障碍被认为与神经精神疾病的不同症状相关。然而，目前临床经颅磁刺激（TMS）方法所针对的具体网络尚不清楚。虽然标准护理TMS依赖于基于头皮的标志点，但最近美国食品药品监督管理局（FDA）批准的TMS方案使用与膝下前扣带回皮质（sgACC）的个体化功能连接来优化TMS靶点。利用先前在精确网络估计方面的工作以及网络水平TMS靶点的最新进展，我们证明临床TMS方法在个体之间针对不同的功能网络。同位头皮位置（左F3和右F4）在个体内部和个体之间针对不同的网络，并且右F4通常倾向于右侧化的控制网络。我们还模拟了靶向与sgACC反相关的背外侧前额叶皮质（dlPFC）区域的影响，发现个体特异性反相关区域可变地靶向与奖赏回路耦合的网络。结合个体化、精确的网络映射和电场（E场）建模，我们进一步说明了如何利用建模以前瞻性地靶向dlPFC中不同的紧密定位的关联网络，具有有意义的空间选择性和E场强度，并回顾性地评估网络参与情况。至关重要的是，我们在一组独立的参与者（包括患有重度抑郁症的参与者）中证明了这种方法的可行性和可靠性，这些参与者接受了针对不同网络的重复TMS治疗，精确靶点来自低负荷的单次数据采集。最后，我们的研究结果强调了选择性和最大强度之间的差异，突出了在精确TMS研究中需要同时考虑这两个指标。
