Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression

验证基于机器学习的脑电图治疗抑郁症生物标志物

• 批准号: 10009501
• 负责人: Amit Etkin
• 金额: $ 98.81万
• 依托单位: ALTO NEUROSCIENCE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10009501
• 关键词: Address; Antidepressive Agents; Award; Base of the Brain; Biological; Biological Factors; Biological Markers; Caring; Clinic; Clinical; Clinical Treatment; Computer Models; Computer software; Data; Data Analyses; Data Set; Development; Electroencephalography; Enrollment; Extravasation; Feedback; Funding; Intervention; Laboratories; Lead; Machine Learning; Maps; Medical Device; Mental Depression; Mental disorders; Methods; Neurosciences; Outcome; Pathway interactions; Patient Triage; Patients; Pattern; Performance; Pharmaceutical Preparations; Pharmacology; Phase; Placebos; Procedures; Psychiatry; Regulation; Research; Resistance; Resistance profile; Scientist; Seeds; Signal Transduction; Small Business Innovation Research Grant; Source; Supervision; System; Testing; Training; Training Programs; Treatment outcome; United States National Institutes of Health; Work; base; biological heterogeneity; candidate marker; clinical care; cohort; commercialization; comorbidity; computerized data processing; cost; data acquisition; depressed patient; individual patient; meetings; novel; patient stratification; patient subsets; programs; prospective; repetitive transcranial magnetic stimulation; response; software development; supervised learning; therapy resistant; tool; treatment optimization; treatment response; treatment-resistant depression; unsupervised learning

SUMMARY/ABSTRACT The overarching aim of Alto Neuroscience is to advance brain-based biomarkers for psychiatric disorders in order to both optimize treatment pathways and drive the development of novel pharmacological and non- pharmacological interventions. Alto does this by developing and applying sophisticated machine learning computational models to electroencephalography (EEG) data collected at scale in real-world clinical treatment contexts. Specifically, in this direct-to-phase II SBIR proposal we will refine, and then independently validate, two EEG-based candidate biomarkers we have identified for stratifying patients with depression in a manner that both factors biological heterogeneity and informs treatment response. One of our biomarkers was derived in a “top-down” (i.e. supervised) manner by trying to directly predict treatment outcome, while the other biomarker presents a complimentary “bottom-up” (i.e. unsupervised) approach that begins by first identifying the most biologically homogeneous subset of patients and then testing the treatment relevance of the subtyping. Together, these findings represent very robust individual patient-level treatment-relevant EEG biomarkers, and in both cases, help define a critically-important objective approach to prospectively identifying and treating treatment- resistant depressed patients. A successful outcome of the proposed work would yield the first FDA-cleared biomarkers for stratifying psychiatric conditions. It would also provide a basis for targeted development of pharmacological and non-pharmacological interventions based on the EEG biomarkers. Both outcomes hold substantial commercial value and exciting potential for transforming psychiatry.

摘要/摘要 阿尔托神经科学的首要目标是推动基于大脑的精神障碍生物标记物的发展 既要优化治疗途径，又要推动新型药物和非药物的开发 药物干预。Alto通过开发和应用复杂的机器学习来做到这一点 实际临床治疗中大规模采集脑电数据的计算模型 上下文。具体地说，在这个直接到第二阶段的SBIR提案中，我们将细化，然后独立验证， 我们已经确定了两个基于脑电的候选生物标记物，用于对抑郁症患者进行分层 这两个因素的生物异质性和通知治疗反应。我们的一个生物标志物是从一种 “自上而下”(即有监督)的方式试图直接预测治疗结果，而另一个生物标记物 提出了一种免费的“自下而上”(即无监督)方法，该方法首先确定 生物同源亚组患者，然后检验各亚型的治疗相关性。一起， 这些发现代表了非常强大的个体患者水平治疗相关的脑电生物标记物，并且在这两个 病例，有助于确定一个极其重要的客观方法，以前瞻性地识别和治疗治疗- 抵抗型抑郁症患者。拟议工作的成功结果将产生第一个通过FDA批准的 对精神疾病进行分层的生物标记物。它还将为有针对性地开发 基于脑电生物标志物的药理学和非药理学干预。这两种结果都成立 巨大的商业价值和改变精神病学的令人兴奋的潜力。