Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment.
Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment.
复制标题
DOI：
10.1038/s41386-021-01110-6
复制
发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Sheline YI
中科院分区：
文献类型：
--
作者：
Balderston NL;Beer JC;Seok D;Makhoul W;Deng ZD;Girelli T;Teferi M;Smyk N;Jaskir M;Oathes DJ;Sheline YI
静息状态功能连接（rsFC）为经颅磁刺激（TMS）治疗的个体化刺激目标提供了希望。然而，目前的目标定位方法不考虑非焦点TMS效应或大规模连接模式。为了克服这些局限性，我们提出了一种新的靶向优化方法，该方法结合了全脑rsFC和电场（e场）建模来识别单一受试者的特定TMS靶点。在这项概念验证研究中，我们招募了91名焦虑痛苦（AM）患者和25名对照。测量抑郁症状（MADRS/HAMD），记录rsFC。我们使用PCA回归来预测来自rsFC的症状并估计参数向量，以输入到我们的电场增强模型中。我们使用24个等距线圈方向对17个左侧dlPFC和7个M1部位进行建模。我们使用电场增强模型计算每个部位/方向的单个受试者预测ΔMADRS/HAMD评分，该模型包括以下元素乘积的线性组合：（1）估计的连接/症状系数，（2）部位/方向的矢量化电场模型，（3）rsFC矩阵，按比例常数缩放。在AM患者中，我们的基于连通性的模型预测BA 9附近的抑郁症显著减少，但对于垂直于皮质回的线圈方向，M1没有。在对照组中，没有网站/方向组合显示出显着的预测变化。这些结果证实了以前的工作，表明左dlPFC刺激抑郁症治疗的疗效，并预测更好的结果与个性化的目标。他们还表明，我们新的基于连通性的电场建模方法可以有效地识别潜在的TMS治疗反应者，并个性化TMS靶向，以最大限度地提高治疗效果。
Resting state functional connectivity (rsFC) offers promise for individualizing stimulation targets for transcranial magnetic stimulation (TMS) treatments. However, current targeting approaches do not account for non-focal TMS effects or large-scale connectivity patterns. To overcome these limitations, we propose a novel targeting optimization approach that combines whole-brain rsFC and electric-field (e-field) modelling to identify single-subject, symptom-specific TMS targets. In this proof of concept study, we recruited 91 anxious misery (AM) patients and 25 controls. We measured depression symptoms (MADRS/HAMD) and recorded rsFC. We used a PCA regression to predict symptoms from rsFC and estimate the parameter vector, for input into our e-field augmented model. We modeled 17 left dlPFC and 7 M1 sites using 24 equally spaced coil orientations. We computed single-subject predicted ΔMADRS/HAMD scores for each site/orientation using the e-field augmented model, which comprises a linear combination of the following elementwise products (1) the estimated connectivity/symptom coefficients, (2) a vectorized e-field model for site/orientation, (3) rsFC matrix, scaled by a proportionality constant. In AM patients, our connectivity-based model predicted a significant decrease depression for sites near BA9, but not M1 for coil orientations perpendicular to the cortical gyrus. In control subjects, no site/orientation combination showed a significant predicted change. These results corroborate previous work suggesting the efficacy of left dlPFC stimulation for depression treatment, and predict better outcomes with individualized targeting. They also suggest that our novel connectivity-based e-field modelling approach may effectively identify potential TMS treatment responders and individualize TMS targeting to maximize the therapeutic impact.
登录
查看更多内容
影响因子：
6.8
作者：
Balderston NL;Liu J;Roberson-Nay R;Ernst M;Grillon C
通讯作者：
Grillon C
DOI：
10.1176/appi.focus.16407
发表时间：
2018-10-01
期刊：
Focus (American Psychiatric Publishing)
影响因子：
--
作者：
Cipriani, Andrea;Furukawa, Toshi A;Geddes, John R
通讯作者：
Geddes, John R
影响因子：
3.3
作者：
Drevets, Wayne C.;Savitz, Jonathan
通讯作者：
Savitz, Jonathan
影响因子：
10.5
作者：
Diefenbach, Gretchen J.;Bragdon, Laura B.;Assaf, Michal
通讯作者：
Assaf, Michal
影响因子：
7.4
作者：
Carpenter, Linda L.;Janicak, Philip G.;Demitrack, Mark A.
通讯作者：
Demitrack, Mark A.