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

• 批准号: 10586155
• 负责人: BRIAN Scott CAFFO
• 金额: $ 47.75万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 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/10586155
• 关键词: Adoption; Affect; Bayesian Method; Bayesian Modeling; Biological Markers; Brain; Clinical; Cognitive; Communities; Complex; Computer software; Data; Data Set; Detection; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Disease Outcome; Disease model; Dropout; Economic Burden; Elements; Family member; Functional Magnetic Resonance Imaging; Goals; Impaired cognition; Individual; Infrastructure; Intuition; Joints; Knowledge; Learning; Measures; Medical; Meta-Analysis; Methodology; Methods; Modality; Modeling; Neurons; Neurosciences; Pathway Analysis; Patients; Pattern; Performance; Persons; Procedures; Process; Research; Rest; 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; behavioral outcome; behavioral phenotyping; biomarker discovery; clinical predictors; 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; neural; neuroimaging; neuroimaging marker; neuropsychiatric disorder; non-invasive imaging; novel; patient variability; programs; socioeconomics; software development; theories; user-friendly

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

项目成果

Multivariate associations between behavioural dimensions and white matter across children and adolescents with and without attention-deficit/hyperactivity disorder.

在有或没有注意力缺陷/多动症的儿童和青少年之间行为维度与白质之间的多元关联。

• DOI: 10.1111/jcpp.13689
• 发表时间: 2023-02
• 期刊: JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY
• 影响因子: 7.6
• 作者: Bu, Xuan;Gao, Yingxue;Liang, Kaili;Bao, Weijie;Chen, Ying;Guo, Lanting;Gong, Qiyong;Lu, Hanzhang;Caffo, Brian;Mori, Susumu;Huang, Xiaoqi
• 通讯作者: Huang, Xiaoqi