识别阿尔茨海默病患者经颅直流电刺激治疗反应的生物标志物：一种使用静息态脑电图分类的机器学习方法。
Identifying biomarkers for tDCS treatment response in Alzheimer's disease patients: a machine learning approach using resting-state EEG classification.
作者信息
Andrade Suellen Marinho, da Silva-Sauer Leandro, de Carvalho Carolina Dias, de Araújo Elidianne Layanne Medeiros, Lima Eloise de Oliveira, Fernandes Fernanda Maria Lima, Moreira Karen Lúcia de Araújo Freitas, Camilo Maria Eduarda, Andrade Lisieux Marie Marinho Dos Santos, Borges Daniel Tezoni, da Silva Filho Edson Meneses, Lindquist Ana Raquel, Pegado Rodrigo, Morya Edgard, Yamauti Seidi Yonamine, Alves Nelson Torro, Fernández-Calvo Bernardino, de Souza Neto José Maurício Ramos
机构信息
Aging and Neuroscience Laboratory, Federal University of Paraíba, João Pessoa, Paraíba, Brazil.
Center for Alternative and Renewable Energies (CEAR), Department of Electrical Engineering, Federal University of Paraíba, João Pessoa, Paraíba, Brazil.
出版信息
Front Hum Neurosci. 2023 Oct 4;17:1234168. doi: 10.3389/fnhum.2023.1234168. eCollection 2023.
BACKGROUND
Transcranial direct current stimulation (tDCS) is a promising treatment for Alzheimer's Disease (AD). However, identifying objective biomarkers that can predict brain stimulation efficacy, remains a challenge. The primary aim of this investigation is to delineate the cerebral regions implicated in AD, taking into account the existing lacuna in comprehension of these regions. In pursuit of this objective, we have employed a supervised machine learning algorithm to prognosticate the neurophysiological outcomes resultant from the confluence of tDCS therapy plus cognitive intervention within both the cohort of responders and non-responders to antecedent tDCS treatment, stratified on the basis of antecedent cognitive outcomes.
METHODS
The data were obtained through an interventional trial. The study recorded high-resolution electroencephalography (EEG) in 70 AD patients and analyzed spectral power density during a 6 min resting period with eyes open focusing on a fixed point. The cognitive response was assessed using the AD Assessment Scale-Cognitive Subscale. The training process was carried out through a Random Forest classifier, and the dataset was partitioned into equally-partitioned subsamples. The model was iterated times using K-1 subsamples as the training bench and the remaining subsample as validation data for testing the model.
RESULTS
A clinical discriminating EEG biomarkers (features) was found. The ML model identified four brain regions that best predict the response to tDCS associated with cognitive intervention in AD patients. These regions included the channels: FC1, F8, CP5, Oz, and F7.
CONCLUSION
These findings suggest that resting-state EEG features can provide valuable information on the likelihood of cognitive response to tDCS plus cognitive intervention in AD patients. The identified brain regions may serve as potential biomarkers for predicting treatment response and maybe guide a patient-centered strategy.
CLINICAL TRIAL REGISTRATION
https://classic.clinicaltrials.gov/ct2/show/NCT02772185?term=NCT02772185&draw=2&rank=1, identifier ID: NCT02772185.
背景
经颅直流电刺激（tDCS）是治疗阿尔茨海默病（AD）的一种很有前景的方法。然而，识别能够预测脑刺激疗效的客观生物标志物仍然是一项挑战。本研究的主要目的是在考虑到对这些脑区理解存在的空白的情况下，描绘出与AD相关的脑区。为了实现这一目标，我们采用了一种监督机器学习算法，对在先前tDCS治疗的有反应者和无反应者队列中，由tDCS治疗与认知干预相结合所产生的神经生理结果进行预测，该队列根据先前的认知结果进行分层。
方法
数据通过一项干预性试验获得。该研究记录了70名AD患者的高分辨率脑电图（EEG），并在6分钟睁眼静息期专注于一个固定点时分析频谱功率密度。使用AD评估量表 - 认知分量表评估认知反应。训练过程通过随机森林分类器进行，数据集被划分为等分的子样本。该模型使用K - 1个子样本作为训练平台进行迭代，并将剩余的子样本作为验证数据来测试模型。
结果
发现了一种具有临床鉴别能力的脑电图生物标志物（特征）。机器学习模型确定了四个最能预测AD患者对与认知干预相关的tDCS反应的脑区。这些脑区包括通道：FC1、F8、CP5、Oz和F7。
结论
这些发现表明，静息态脑电图特征可以提供有关AD患者对tDCS加认知干预的认知反应可能性的有价值信息。所确定的脑区可能作为预测治疗反应的潜在生物标志物，并可能指导以患者为中心的策略。
临床试验注册
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