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一种用于临床缓解抑郁症的实时功能磁共振成像神经反馈系统,具有与受试者无关的脑状态分类:原理验证研究。

A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study.

作者信息

Pereira Jaime A, Ray Andreas, Rana Mohit, Silva Claudio, Salinas Cesar, Zamorano Francisco, Irani Martin, Opazo Patricia, Sitaram Ranganatha, Ruiz Sergio

机构信息

Departamento de Psiquiatría, Facultad de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile.

Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.

出版信息

Front Hum Neurosci. 2022 Aug 25;16:933559. doi: 10.3389/fnhum.2022.933559. eCollection 2022.

Abstract

Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the "happiness emotional brain state" of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4-1 Median = 6.563%; Range = 4.10-27.34; Wilcoxon Test (0), 2-tailed = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1-15; Wilcoxon Test (0), 2-tailed = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed = 0.50/ HDRS CE3-2 Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.

摘要

大多数基于功能磁共振成像的临床神经反馈研究都将患者自身的神经活动用作反馈。本研究的目的是创建一种独立于个体的脑状态分类器,作为实时功能磁共振成像神经反馈(rt-fMRI NF)系统的一部分,该系统可指导抑郁症患者实现健康的脑状态,然后检查随后的临床变化。第一步,基于支持向量机(SVM)的脑分类器根据在一次磁共振成像(MRI) session期间从一名健康女性参与者接收的快乐自传体意象和运动意象模块的神经信息进行训练。第二步,对7名有轻度或中度抑郁症状的右利手女性患者进行训练,使其自身神经活动与对应于健康参与者“快乐情绪脑状态”的神经活动相匹配。训练(在2周内进行4次训练session)使用我们创建的脑状态分类器指导的rt-fMRI NF系统进行。因此,在第一步中先前使用支持向量机分类和效应映射获得的信息性体素被用于对患者的血氧水平依赖(BOLD)活动进行分类,并在神经反馈训练过程中转换为实时视觉反馈。在所有患者中均观察到训练接近尾声时分类器准确性的提高[第4-1次session中位数 = 6.563%;范围 = 4.10 - 27.34;威尔科克森检验(0),双侧 = 0.031]。在一项盲法标准化临床评估[汉密尔顿抑郁量表(HDRS)CE2-1中位数 = 7;范围2至15;威尔科克森检验(0),双侧 = 0.016]以及自我报告评估[贝克抑郁量表第二版(BDI-II)CE2-1中位数 = 8;范围1 - 15;威尔科克森检验(0),双侧 = 0.031]中也观察到了临床改善。此外,干预后10天临床改善仍然存在[BDI-II CE3-2中位数 = 0;范围 -1至2;威尔科克森检验(0),双侧 = 0.50/ HDRS CE3-2中位数 = 0;范围 -1至2;威尔科克森检验(0),双侧 = 0.625]。尽管需要增加参与者数量并纳入对照组以证实这些发现,但结果表明使用非侵入性刺激技术在抑郁症的神经调节和临床缓解方面有一个新的选择。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b176/9452730/e6a330625831/fnhum-16-933559-g0001.jpg

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