Deep Generative Analyses for fMRI data

fMRI 数据的深度生成分析

基本信息

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
项目类别：
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

项目摘要

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 5150 万人（五分之一的美国成年人）患有精神疾病 (MI)，据估计，严重的 MI 鉴于 MI 的高患病率和社会成本，美国人每年损失约 1,930 亿美元。人们越来越多地推动将神经科学研究的进展转化为 MI 的改进在此背景下，精神病学的重要性日益明显。疾病的出现是大脑时空动力学和网络连接异常的结果。此外，神经精神疾病的表现通常具有高度的个体差异，在本提案中，我们的目标是设计新的功能磁共振成像分析方法。能够应对上述挑战——即能够直接模拟大脑时空动态，同时还捕获个体差异更具体地说，该提案的主要目标是扩展。先前开发的深度生成功能磁共振成像分析模型（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），同时捕获非线性效应和相关性为了实现这一目标，我建议：1）通过拟合循环神经网络直接对时间动态进行建模。网络（RNN）到 VAE-GAM 潜在空间；2）使用深度混合捕获个体差异效果建模框架将各个主题图建模为组级基线图的总和以及使用学习的、主题独特的嵌入向量生成的主题独特的映射预期的结果。该提案的核心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信徒识别新的大脑将健康和疾病状态下的高级行为联系起来的活动模式我们相信这样的模型可能是一个。逐步实现提供生物合理、计算驱动和个性化健康的目标照顾数百万受精神疾病影响的患者。