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: 10366060
• 负责人: Amit Etkin
• 金额: $ 101.65万
• 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 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10366060
• 关键词: Address; Antidepressive Agents; Award; Base of the Brain; 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.

总结/摘要 Alto Neuroscience的首要目标是推进基于大脑的精神疾病生物标志物， 为了优化治疗途径，并推动新的药理学和非药理学的发展， 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 在现实世界的临床治疗中大规模收集脑电图（EEG）数据的计算模型 contexts.具体来说，在这个直接进入第二阶段的SBIR提案中，我们将完善，然后独立验证， 我们已经确定了两种基于EEG的候选生物标志物，用于对抑郁症患者进行分层， 这两个因素都是生物异质性的因素，并为治疗反应提供信息。我们的一个生物标志物是从一个 通过尝试直接预测治疗结果，以“自上而下”（即监督）的方式，而其他生物标志物 提出了一个互补的“自下而上”（即无监督）的方法，首先确定最 生物学同质的患者子集，然后测试亚型的治疗相关性。我们一起努力， 这些发现代表了非常强大的个体患者水平的治疗相关EEG生物标志物， 病例，帮助定义一个至关重要的客观方法，以前瞻性地识别和治疗治疗- 顽固的抑郁症患者拟议工作的成功结果将产生第一个FDA批准的 用于对精神疾病进行分层的生物标志物。它还将为有针对性地发展 基于EEG生物标志物的药理学和非药理学干预。两种结果都成立 巨大的商业价值和令人兴奋的潜力，改变精神病学。