Ding R01 Administrative Supplement

项目摘要：本补充申请的母合同（R 01 MH 125615）旨在调查 通过恐惧条件反射和恐惧消退学习和不学习恐惧反应的神经机制。 我们的目标是推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。 学习以及告知各种精神疾病的临床干预和诊断程序 恐惧是一种跨诊断病理学大型多模态/多尺度神经成像数据集，其中包括 同步EEG-fMRI，生理测量，如心率和皮肤电导，以及行为 和自我报告数据，正在根据拟议的时间轴获取。 AI/ML的最新进展开始彻底改变神经成像和神经数据分析。我们寻求 利用这些进步来实现对我们假设的创新测试。准备我们的多式联运/ 然而，AI/ML的多尺度数据面临挑战。这一行政补充的目的是 汇集数据管理、数据处理、AI/ML和神经科学/实验 心理学来应对这些挑战。 将追求两个目标。目标1的目标是建立一个基础设施， 用于AI/ML分析和共享的多模态/多尺度数据。具体来说，我们将制定数据协议， 去噪、插补、预处理、偏差校正、伪影去除、归一化和协调， 建立管道，将来自不同数据源的数据整合到统一的存储库中， 根据公平原则进行分析和分享。Aim 2的目标是设计一种新的AI驱动的 多模式/多尺度数据分析平台。具体来说，我们将开发一个基于transformer的平台， 从不同的数据源进行多模式学习，并将学习结果与建议的 认知/神经生理学模型，以实现对父母奖励中假设的创新测试。

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.