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Statistical methods for structural and functional integration in multi-modal neuroimaging data

多模态神经影像数据结构和功能整合的统计方法

基本信息

批准号：
10445053
负责人：
BRIAN Scott CAFFO
金额：
$ 48.44万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-05 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10445053
关键词：
Adoption Affect Bayesian Method Bayesian Modeling Biological Markers Brain Clinical Cognitive Communities Complex Computer software Data Data Set Detection Diffusion Diffusion Magnetic Resonance Imaging Dimensions Disease Disease Outcome Disease model Dropout Elements Family member Functional Magnetic Resonance Imaging Goals Impaired cognition Individual Infrastructure Intuition Joints Knowledge Learning Magnetic Resonance Imaging Measures Medical Meta-Analysis Methodology Methods Modality Modeling Neurons Neurosciences Pathway Analysis Patients Pattern Performance Persons Procedures Process Research Rest Route Schizophrenia Selection Criteria Services Severities Signal Transduction Specific qualifier value Statistical Data Interpretation Statistical Methods Structure Students System Techniques Training Weight Work application programming interface autism spectrum disorder base behavioral outcome behavioral phenotyping biomarker discovery cloud platform cost data framework deep learning deep neural network density developmental disease direct application feature extraction flexibility imaging modality interest model building motor impairment multimodal neuroimaging multimodality network models neuroimaging neuroimaging marker neuropsychiatric disorder non-invasive imaging novel patient variability programs relating to nervous system socioeconomics software development theories user-friendly

项目摘要

Abstract Neuropsychiatric disorders, such as autism and schizophrenia, affect millions of people worldwide and place a considerable burden on both patients and family members. Existing treatments for these disorders have limited efficacy, in part due the varied clinical manifestations, and to our narrow understanding of the impacted neural processes, particularly at the system (i.e., network) level. Two key elements of networks are the underlying infrastructure or physical connections between elements and the functional signaling between entities that rides on top of this infrastructure. Recent advancements in noninvasive imaging have given us the ability to quantify structural and functional relationships in the brain via diffusion MRI, resting-state functional MRI, respectively. The size and scope of datasets measuring network structure and function are increasing in neuroimaging, and other domains, which heightens the need for new statistical frameworks that make full use of the data. Our goal is to develop frameworks for the analysis of structure-function integration in large-scale and complex networks, applied to neuroimaging studies, but also broadly applicable. This proposal will introduce three analytic paradigms: Bayesian network modeling that uses a priori structure-function knowledge for simultaneous network anomaly detection and clinical severity prediction; density regression using optimal transport theory; and end-to-end prediction using deep neural networks. In our application, infrastructure will be measured via dMRI, while function will be measured rs-fMRI. Each of our frameworks will provide a unique means to integrate these distinct imaging modalities, while also respecting the unique information provided by each data type. We also propose a unique software development effort that creates an application program interface to core software and implementations as software as as a service hosted on cloud platforms.

摘要