Deep Generative Analyses for fMRI data

fMRI 数据的深度生成分析

• 批准号: 10820636
• 负责人: Daniela Frank De Albuquerque
• 金额: $ 4.05万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10820636
• 关键词: Address; Adult; American; Behavior; Biological; Biological Markers; Brain; Brain region; Code; Cognitive; Computer software; Cost of Illness; Data; Data Analyses; Data Set; Dependence; Disease; Emotions; Experimental Designs; Explosion; Functional Magnetic Resonance Imaging; Functional disorder; Genetic Markers; Goals; Health; High Prevalence; Impairment; Individual; Individual Differences; Knowledge; Learning; Link; Major Depressive Disorder; Maps; Mental disorders; Methods; Modality; Modeling; Morphology; Neurosciences; Neurosciences Research; Outcome; Participant; Patients; Pattern; Persons; Prevention; Process; Psychiatric therapeutic procedure; Psychiatrist; Psychiatry; Research Personnel; Series; Severities; Structure; Sum; Symptoms; System; Time; Training; Translating; Work; associated symptom; autoencoder; biomarker identification; brain abnormalities; brain based; brain morphology; brain volume; care delivery; cognitive function; cohort; comparative; cost; design; emotion regulation; experimental study; flexibility; high dimensionality; improved; individual variation; interest; lost earning; machine learning model; multimodal neuroimaging; neural network; neuroimaging; neuropsychiatric disorder; novel; personalized health care; personalized intervention; recurrent neural network; response; severe mental illness; social; sound; spatiotemporal; stem; tool; treatment response; vector

ABSTRACT About 51.5 million people (1 in 5 US adults) lives with a mental illness (MI) and it is estimated that serious MI costs Americans about $193 billion in lost earnings, yearly. Given the high prevalence and social cost of MI, there has been a growing push for translating advances in neuroscience research into improvements in MI prevention and psychiatry care delivery. In this context, it has become increasingly evident that psychiatric diseases emerge as result of abnormalities in brain spatiotemporal dynamics and network connectivity. Furthermore, neuropsychiatric diseases typically have a high degree of individual variability in presentation, symptom severity, and treatment response. In this proposal, we aim to design new fMRI analysis methods capable of tackling the abovementioned challenges – i.e., capable of directly modeling brain spatiotemporal dynamics, while also capturing individual variability. More specifically, the main goal of this proposal is to extend a previously developed deep-generative fMRI analysis model (VAE-GAM) that produces interpretable spatial effect maps for each covariate (as in standard methods) while capturing nonlinear effects and correlations across voxels. To accomplish this goal, I propose to: 1) Model temporal dynamics directly by fitting a Recurrent Neural Network (RNN) to the VAE-GAM latent space; and 2) Capture individual differences by using a deep Mixed Effects Modeling framework to model individual subject maps as being the sum of a group-level baseline map and a subject-unique map, generated using a learned, subject-unique embedding vector. The expected outcome of this proposal is a flexible fMRI analysis toolset that will allow researchers and clinicians to identify new brain activity patterns linking high-level behavior in health and disease states. We believe such a model could be a step towards fulfilling the goal of delivering biologically-sound, computationally driven, and personalized health care for millions of patients afflicted by mental illness.

抽象的 大约有5150万人（美国5分之一的成年人）患有精神疾病（MI），据估计严重MI 每年，美国人的收入损失约为1930亿美元。考虑到MI的高流行和社会成本， 将神经科学研究的进步转化为MI的改进，越来越多的推动力 预防和精神病护理。在这种情况下，越来越多的证据表明精神病学 由于脑时空动力学和网络连通性异常而出现疾病。 此外，神经精神疾病通常具有高度的个体变异性， 症状严重程度和治疗反应。在此建议中，我们旨在设计新的fMRI分析方法 能够应对上述挑战 - 即能够直接建模大脑时空 动态，同时还可以捕获个人变异性。更具体地说，该提议的主要目标是扩展 先前开发的深入FMRI分析模型（VAE-GAM），可产生可解释的空间 每个协变量的效应图（如标准方法），同时捕获跨越非线性效应和相关性 体素。为了实现这一目标，我建议：1）直接拟合复发神经直接模型临时动态 网络（RNN）到VAE-GAM潜在空间； 2）通过使用深层混合来捕获个体差异 效应建模框架以建模单个主题图作为组级基线图的总和 以及使用学习的，唯一的嵌入向量生成的主题唯一图。预期的结果 该提案是一种灵活的fMRI分析工具集，它将允许研究人员和临床医生识别新的大脑 与健康和疾病状态中高级行为联系起来的活动模式。我们认为这样的模型可能是 迈向实现实现生物学，计算驱动和个性化健康的目标 照顾数百万患有精神疾病的患者。