课题基金 / 基金详情

Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression

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:
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-03-01 至 2023-02-28

项目摘要

项目成果

Amit Etkin的其他基金

相关文献

中文摘要
翻译
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。在一起, 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。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
  • 批准号:
    10009501
  • 项目类别:
  • 资助金额:
    $98.81万
  • 财政年份:
    2020
  • 负责人:
    Amit Etkin
  • 依托单位:
Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
  • 批准号:
    10366060
  • 项目类别:
  • 资助金额:
    $101.65万
  • 财政年份:
    2020
  • 负责人:
    Amit Etkin
  • 依托单位:
Assessing an electroencephalography (EEG) biomarker of response to transcranial magnetic stimulation for major depression
A "Circuits-First" Platform for Personalized Neurostimulation Treatment
  • 批准号:
    10214488
  • 项目类别:
  • 资助金额:
    $101.5万
  • 财政年份:
    2019
  • 负责人:
    Amit Etkin
  • 依托单位: