Transcranial Magnetic Stimulation Indices of Cortical Excitability Enhance the Prediction of Response to Pharmacotherapy in Late-Life Depression.

Transcranial Magnetic Stimulation Indices of Cortical Excitability Enhance the Prediction of Response to Pharmacotherapy in Late-Life Depression.
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DOI:
10.1016/j.bpsc.2021.07.005
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发表时间:
2022-03
期刊:
Biological psychiatry. Cognitive neuroscience and neuroimaging
影响因子:
--
通讯作者:
Blumberger DM
Blumberger DM
中科院分区:
其他
文献类型:
--
作者:
Lissemore JI;Mulsant BH;Bonner AJ;Butters MA;Chen R;Downar J;Karp JF;Lenze EJ;Rajji TK;Reynolds CF 3rd;Zomorrodi R;Daskalakis ZJ;Blumberger DM

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患有晚年抑郁症 (LLD) 的老年人通常对一线药物治疗不完全或缺乏反应。使用客观的生物学测量来预测反应可以改善 LLD 的治疗。经颅磁刺激 (TMS) 可用于测量皮层兴奋性、抑制性和可塑性,这与 LLD 病理生理学有关,并与年轻抑郁症患者的脑刺激治疗结果相关。 TMS 测量尚未被研究作为 LLD 治疗结果或任何年龄成人抑郁症药物治疗结果的预测因素。我们评估了治疗前单脉冲和配对脉冲 TMS 测量,结合临床和人口统计学测量,是否可以预测 76 名 LLD 门诊患者的文拉法辛治疗反应。我们比较了包含或不包含 TMS 预测器的机器学习模型的预测性能。两个单脉冲 TMS 测量预测文拉法辛反应:皮质兴奋性(神经元膜兴奋性)和皮质兴奋性的变异性(兴奋性水平的动态波动)。在交叉验证中,模型结合使用这些 TMS 预测因子、治疗抵抗的临床标志物和年龄,以 73±11% 的平衡准确度对患者进行分类(有反应者和无反应者的平均正确分类率;排列测试,p<0.005);这些模型显着优于仅使用临床和人口预测因子的模型(校正 t 检验,p=0.025)(60±10% 平衡准确度)。这些初步研究结果表明,皮层兴奋性的单脉冲 TMS 测量可能是 LLD 药物治疗反应的有用预测因子。需要未来的研究来证实这些发现,并确定将 TMS 预测因子与其他生物标志物相结合是否可以进一步提高预测 LLD 治疗结果的准确性。
Older adults with late-life depression (LLD) often experience incomplete or lack of response to first-line pharmacotherapy. The treatment of LLD could be improved using objective biological measures to predict response. Transcranial magnetic stimulation (TMS) can be used to measure cortical excitability, inhibition, and plasticity, which have been implicated in LLD pathophysiology, and associated with brain stimulation treatment outcomes in younger adults with depression. TMS measures have not yet been investigated as predictors of treatment outcomes in LLD, or pharmacotherapy outcomes in adults of any age with depression. We assessed whether pre-treatment single-pulse and paired-pulse TMS measures, combined with clinical and demographic measures, predict venlafaxine treatment response in 76 outpatients with LLD. We compared the predictive performance of machine learning models including or excluding TMS predictors. Two single-pulse TMS measures predicted venlafaxine response: cortical excitability (neuronal membrane excitability), and the variability of cortical excitability (dynamic fluctuations in excitability levels). In cross-validation, models using a combination of these TMS predictors, clinical markers of treatment resistance, and age, classified patients with 73±11% balanced accuracy (average correct classification rate of responders and non-responders; permutation testing, p<0.005); these models significantly outperformed (corrected t-test, p=0.025) models using clinical and demographic predictors alone (60±10% balanced accuracy). These preliminary findings suggest that single-pulse TMS measures of cortical excitability may be useful predictors of response to pharmacotherapy in LLD. Future studies are needed to confirm these findings and determine whether combining TMS predictors with other biomarkers further improves the accuracy of predicting LLD treatment outcome.
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