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Statistical Analysis of Graph-Theoretic Indices to Study EEG-TMS Connectivity in Patients with Depression
Frontiers in Neuroinformatics ( IF 2.5 ) Pub Date : 2021-03-18 , DOI: 10.3389/fninf.2021.651082
Elzbieta Olejarczyk 1 , Adam Jozwik 2 , Vladas Valiulis 3, 4 , Kastytis Dapsys 3, 4 , Giedrius Gerulskis 3, 4 , Arunas Germanavicius 3, 4
Affiliation  

Abstract Aim: The objective of this work was to demonstrate the usefulness of a novel statistical method to study the impact of transcranial magnetic stimulation (TMS) on brain connectivity in patients with depression using different stimulation protocols, i.e., 1 Hz repetitive TMS over the right dorsolateral prefrontal cortex (DLPFC) (protocol G1), 10 Hz repetitive TMS over the left DLPFC (G2), and intermittent theta burst stimulation (iTBS) consisting of three 50 Hz burst bundle repeated at 5 Hz frequency (G3). Methods: Electroencephalography (EEG) connectivity analysis was performed using Directed Transfer Function (DTF) and a set of 21 indices based on graph theory. The statistical analysis of graph-theoretic indices consisted of a combination of the k-NN rule, the leave-one-out method, and a statistical test using a 2 × 2 contingency table. Results: Our new statistical approach allowed for selection of the best set of graph-based indices derived from DTF, and for differentiation between conditions (i.e., before and after TMS) and between TMS protocols. The effects of TMS was found to differ based on frequency band. Conclusions: A set of four brain asymmetry measures were particularly useful to study protocol- and frequency-dependent effects of TMS on brain connectivity. Significance: The new approach would allow for better evaluation of the therapeutic effects of TMS and choice of the most appropriate stimulation protocol.

中文翻译:

研究抑郁症患者 EEG-TMS 连通性的图形理论指标的统计分析

摘要目的:这项工作的目的是证明一种新的统计方法的有用性,以研究经颅磁刺激 (TMS) 对使用不同刺激方案的抑郁症患者大脑连接的影响,即 1 Hz 重复 TMS 在右侧背外侧前额叶皮层 (DLPFC) (协议 G1)、左侧 DLPFC (G2) 上的 10 Hz 重复 TMS 和由三个 50 Hz 脉冲束以 5 Hz 频率 (G3) 重复的间歇性 theta 脉冲刺激 (iTBS)。方法:使用定向传递函数 (DTF) 和一组基于图论的 21 个指标进行脑电图 (EEG) 连通性分析。图论指标的统计分析包括 k-NN 规则、留一法和使用 2 × 2 列联表的统计检验的组合。结果:我们的新统计方法允许选择从 DTF 派生的基于图形的最佳指标集,以及区分条件(即 TMS 之前和之后)和 TMS 协议之间的差异。发现 TMS 的效果因频带而异。结论:一组四种大脑不对称性测量对于研究 TMS 对大脑连接的协议和频率依赖性影响特别有用。意义:新方法将允许更好地评估 TMS 的治疗效果并选择最合适的刺激方案。发现 TMS 的效果因频带而异。结论:一组四种大脑不对称性测量对于研究 TMS 对大脑连接的协议和频率依赖性影响特别有用。意义:新方法将允许更好地评估 TMS 的治疗效果并选择最合适的刺激方案。发现 TMS 的效果因频带而异。结论:一组四种大脑不对称性测量对于研究 TMS 对大脑连接的协议和频率依赖性影响特别有用。意义:新方法将允许更好地评估 TMS 的治疗效果并选择最合适的刺激方案。
更新日期:2021-03-18
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