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

基本信息

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
项目类别：
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

项目摘要

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/摘要中音神经科学的总体目的是推进基于大脑的生物标志物的精神疾病为了优化治疗途径并推动新型药理和非 - 非 - 药理干预措施。 Alto通过开发和应用复杂的机器学习来做到这一点脑电图（EEG）数据的计算模型在现实世界中的临床处理中收集的数据 contextss.ceppers.cepplys，在这个直接到基础的II SBIR提案中，我们将完善，然后独立验证，我们已经确定了两个基于脑电图的候选生物标志物，用于以某种方式对抑郁症进行分层这两种因素生物异质性并为治疗反应提供了信息。我们的生物标志物之一是在一个通过试图直接预测治疗结果，而其他生物标志物，“自上而下”（即监督）方式提出一种免费的“自下而上”（即无监督的）方法，首先要识别最多患者的生物学均质子集，然后测试亚型的治疗相关性。一起，这些发现代表了非常强大的个体患者级治疗与脑电图生物标志物，并且在这两种情况下案例，有助于定义一种至关重要的客观方法，用于识别和治疗治疗抵抗抑郁症患者。拟议工作的成功结果将产生第一个FDA清除分层精神病疾病的生物标志物。它还将为有针对性发展的基础基于脑电图生物标志物的药理和非药理学干预措施。两个结果都保持巨大的商业价值和令人兴奋的转变精神病学潜力。