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.

摘要 大约5150万人（1/5的美国成年人）患有精神疾病（MI），据估计，严重的MI 美国人每年损失的收入约为1930亿美元。鉴于MI的高患病率和社会成本， 将神经科学研究的进展转化为MI改善的努力越来越多 预防和精神病护理服务。在这种情况下，越来越明显的是， 疾病的出现是大脑时空动态和网络连接异常的结果。 此外，神经精神疾病通常在表现上具有高度的个体差异性， 症状严重程度和治疗反应。在这个提议中，我们的目标是设计新的fMRI分析方法 能够应对上述挑战，即，能够直接模拟大脑时空 动态，同时也捕捉个体差异。更具体地说，这项建议的主要目标是扩大 先前开发的深度生成功能磁共振成像分析模型（VAE-GAM）， 每个协变量的效应图（如标准方法），同时捕获非线性效应和相关性 体素为了实现这一目标，我建议：1）通过拟合递归神经网络直接建模时间动态 网络（RNN）到VAE-GAM潜在空间;以及2）通过使用深度混合 效果建模框架，用于将单个主题图建模为组级基线图的总和 以及使用学习的对象唯一嵌入向量生成的对象唯一映射。预期结果 这项提议的一个重要部分是一个灵活的功能磁共振成像分析工具集，它将允许研究人员和临床医生识别新的大脑 活动模式将健康和疾病状态中的高级行为联系起来。我们相信这样的模型可以成为 朝着实现提供生物学上健全的、计算驱动的和个性化的健康的目标迈出了一步 为数百万精神疾病患者提供医疗服务。