Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC V6T 2A1, Canada.
Department of Psychiatry, University of Toronto, Toronto, ON, Canada; MRI-Guided rTMS Clinic, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
Prog Neuropsychopharmacol Biol Psychiatry. 2019 Jun 8;92:217-225. doi: 10.1016/j.pnpbp.2019.01.012. Epub 2019 Jan 25.
Repetitive transcranial magnetic stimulation (rTMS) is a first-line option for treatment-resistant depression (TRD), but prediction of treatment outcome remains a clinical challenge. The present study aimed to compare structural and functional covariance networks (SCNs and FCNs) between remitters and nonremitters. We determined the predictive capacities of SCNs and FCNs to discriminate the two groups. Fifty TRD patients underwent a course of rTMS to the left dorsolateral prefrontal cortex. They were categorized into remitters (n = 22) and nonremitters (n = 28) based on HDRS≤7 at the end of treatment. Baseline structural and functional magnetic imaging (sMRI and fMRI) of the patients and 42 healthy controls were collected. SCNs and FCNs were defined based on structural and functional covariance of gray mater volume (GMV) and fractional amplitude of low-frequency fluctuations (fALFF) from sMRI and fMRI, respectively. Structural/functional network integrity of these networks (default mode network [DMN], central executive network [CEN] and salience network [SN]) were compared between the three groups. In patients, associations between SCNs and FCNs with clinical improvements were studied using linear correlation analysis. Receiver-operating characteristic (ROC) analysis was conducted to confirm the utility of the SCNs and FCNs in classifying clinical sub-groups. Nonremitters exhibited lower structural integrity in CEN than remitters and controls. Higher structural integrity of CEN was related to clinical improvement (r = 0.423, p = .002), and structural integrity distinguished remitters and nonremitters with a fairly high accuracy (AUC = 0.71, p = .008). No group differences or correlation with clinical changes were found in FCNs. Results suggest the CEN may play a role mediating clinical improvement in rTMS for depression. Structural covariance networks may be features to consider in prediction of clinical improvement.
重复经颅磁刺激（rTMS）是治疗抵抗性抑郁症（TRD）的一线选择，但治疗效果的预测仍然是临床挑战。本研究旨在比较缓解者和未缓解者之间的结构和功能协变网络（SCN 和 FCN）。我们确定了 SCN 和 FCN 区分两组的预测能力。50 名 TRD 患者接受了左背外侧前额叶皮层的 rTMS 治疗。根据治疗结束时 HDRS≤7，他们分为缓解者（n=22）和未缓解者（n=28）。收集了患者和 42 名健康对照者的基线结构和功能磁共振成像（sMRI 和 fMRI）。基于 sMRI 和 fMRI 中灰质体积（GMV）和低频振幅（fALFF）的结构协变，分别定义了 SCN 和 FCN。比较了三组之间这些网络（默认模式网络[DMN]、中央执行网络[CEN]和突显网络[SN]）的结构/功能网络完整性。在患者中，使用线性相关分析研究了 SCN 和 FCN 与临床改善的相关性。进行了接收器工作特征（ROC）分析，以确认 SCN 和 FCN 在分类临床亚组中的效用。未缓解者的 CEN 结构完整性低于缓解者和对照组。CEN 的结构完整性与临床改善相关（r=0.423，p=0.002），结构完整性以相当高的准确性区分了缓解者和未缓解者（AUC=0.71，p=0.008）。FCN 中未发现组间差异或与临床变化的相关性。结果表明，CEN 可能在 rTMS 治疗抑郁症的临床改善中发挥作用。结构协变网络可能是预测临床改善的特征。
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