Ding R01 Administrative Supplement

丁R01行政补遗

• 批准号: 10842657
• 负责人: MINGZHOU DING
• 金额: $ 19.48万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10842657
• 关键词: Administrative Supplement; Artificial Intelligence; Association Learning; Attention; Award; Behavioral; Clinical; Cognitive; Data; Data Analyses; Data Reporting; Data Set; Data Sources; Diagnostic; Diagnostic Procedure; Excision; Experimental Psychology; Extinction; FAIR principles; Face; Fright; Galvanic Skin Response; Goals; Heart Rate; Infrastructure; Intervention; Learning; Link; Machine Learning; Measures; Mental disorders; Methods; Modeling; Morphologic artifacts; Neurosciences; Parents; Pathology; Patient Self-Report; Physiological; Protocols documentation; Source; Testing; Visual; computerized data processing; conditioned fear; data management; denoising; design; functional magnetic resonance imaging/electroencephalography; innovation; insight; learning outcome; multimodality; multiscale data; neural; neuroimaging; neuromechanism; neurophysiology; novel; repository; response; timeline

Project Summary: The parent award (R01MH125615) of this supplement application seeks to investigate the neural mechanisms of learning and un-learning of a fear response through fear conditioning and fear extinction. The goal is to advance our understanding of how visual and attention networks interact during associative learning as well as to inform clinical intervention and diagnostic procedures in a variety of psychiatric disorders where fear is a transdiagnostic pathology. A large multimodal/multiscale neuroimaging dataset, which includes simultaneous EEG-fMRI, physiological measures such as heart rate and skin conductance, as well as behavioral and self-report data, is being acquired according to the proposed timeline. Recent advances in AI/ML are beginning to revolutionize neuroimaging and neural data analysis. We seek to leverage these advances to enable innovative testing of our hypotheses. Readying our multimodal/ multiscale data for AI/ML, however, faces challenges. The goal of this administrative supplement is to bring together expertise in data management, data processing, AI/ML, and neuroscience/experimental psychology to meet these challenges. Two aims will be pursued. The objective of Aim 1 is to build an infrastructure for preparing the multimodal/multiscale data for AI/ML analysis and sharing. Specifically, we will develop protocols for data denoising, imputation, pre-processing, bias correction, artifact removal, normalization, and harmonization and establish pipelines to integrate and consolidate data from different data sources into a unifying repository for analysis and sharing according to the FAIR principle. The objective of Aim 2 is to design a novel AI-driven platform for analyzing multimodal/multiscale data. Specifically, we will develop a transformer-based platform to enable multimodal learning from diverse sources of data and link the outcomes of learning with the proposed cognitive/neurophysiological model to enable the innovative testing of the hypotheses in the parent award.

项目摘要：本补充申请的家长奖（R01MH125615）旨在调查 通过恐惧调节和恐惧消退来学习和取消学习恐惧反应的神经机制。 目标是增进我们对视觉和注意力网络在联想过程中如何相互作用的理解 学习并为各种精神疾病的临床干预和诊断程序提供信息 恐惧是一种跨诊断的病理学。大型多模态/多尺度神经影像数据集，其中包括 同时进行 EEG-fMRI、心率和皮肤电导等生理测量以及行为测量 和自我报告数据，正在根据建议的时间表获取。 人工智能/机器学习的最新进展正在开始彻底改变神经成像和神经数据分析。我们寻求 利用这些进步来对我们的假设进行创新测试。准备我们的多式联运/ 然而，人工智能/机器学习的多尺度数据面临挑战。本行政补充的目的是 汇集数据管理、数据处理、AI/ML 和神经科学/实验方面的专业知识 心理学来应对这些挑战。 我们将追求两个目标。目标 1 的目标是建立一个基础设施，为 用于 AI/ML 分析和共享的多模式/多尺度数据。具体来说，我们将开发数据协议 去噪、插补、预处理、偏差校正、伪影去除、标准化和协调 建立管道以将来自不同数据源的数据集成和整合到统一的存储库中 按照公平原则进行分析和共享。 Aim 2 的目标是设计一种新颖的人工智能驱动的 用于分析多模式/多尺度数据的平台。具体来说，我们将开发一个基于变压器的平台 实现来自不同数据源的多模式学习，并将学习结果与建议的内容联系起来 认知/神经生理学模型，能够对家长奖中的假设进行创新测试。