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: 10116492
• 负责人: Amit Etkin
• 金额: $ 118.41万
• 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/10116492
• 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/文摘