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Inter-modal Coupling Image Analytics

模态间耦合图像分析

基本信息

批准号：
9918452
负责人：
Theodore Satterthwaite
金额：
$ 44.37万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-05-10 至 2022-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9918452
关键词：
Adolescence Benchmarking Biological Markers Brain Brain Pathology Brain imaging Brain region Clinical Collaborations Communities Complex Coupling Data Data Set Development Diffusion Magnetic Resonance Imaging Dimensions Evaluation Exhibits Familiarity Female Functional Magnetic Resonance Imaging Goals Image Individual Differences Magnetic Resonance Imaging Maps Measurement Measures Mental disorders Methods Modality Modeling Multimodal Imaging Neighborhoods Neurosciences Noise Participant Pathologic Pattern Perfusion Phenotype Philadelphia Population Psychopathology Psychotic Disorders Reproducibility Resources Rest Risk Sampling Short-Term Memory Spin Labels Statistical Methods Structure Structure-Activity Relationship Surface Symptoms Techniques Testing Time Validation Work Youth adolescent brain development base bioimaging brain health clinical phenotype clinical risk cohort dimensional analysis follow-up high dimensionality imaging modality imaging study insight longitudinal analysis male multimodality neuroimaging novel novel marker specific biomarkers statistics tool

项目摘要

PROJECT SUMMARY We propose to develop statistical methods for the analysis of the local covariance structure between measurements in multimodal biomedical imaging studies. Despite the rise of multi-modal imaging studies and the proliferation of derived measures available from each modality, very little work has explored relationships between modalities. Notably, brain structure and function evolve dramatically in the context of adolescent brain development, but typical analyses usually evaluate each imaging phenotype separately. We propose to develop tools for measuring relationships between brain imaging phenotypes provided by disparate imaging modalities. Covariance structures at the subject level will allow identification of local relationships between measures, through weighted regressions and singular value decomposition-based techniques. These techniques will be extended to accommodate any number of imaging modalities and incorporate repeated measures. We will use extensive test-retest data provided by the Consortium of Reproducibility and Reliability (CORR) for validation, and to identify coupling measures that are particularly reliable. The rich multi-modal imaging data and associated clinical phenotypes from the Philadelphia Neurodevelopmental Cohort (PNC) will allow us to delineate how inter- modal coupling evolves in normal brain development, and also establish how such relationships are altered in association with psychosis-spectrum symptoms. This proposal builds upon established collaboration between the PIs and capitalizes on their complimentary expertise in imaging statistics, developmental neuroimaging, psychopathology, and intimate familiarity with the PNC dataset. Upon completion, this project will provide powerful new tools for the neuroscience community, as well as novel insights regarding brain development and early psychosis-spectrum symptoms.

项目总结