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Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data

多维数据分析与整合的统计ICA方法

基本信息

批准号：
10264896
负责人：
Ying Guo
金额：
$ 51.58万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-25 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10264896
关键词：
Address Advanced Development Award Behavior Behavior assessment Biological Markers Brain Brain imaging Clinical Clinical assessments Communities Computational Technique Data Data Analyses Data Set Development Diagnosis Diffusion Magnetic Resonance Imaging Dimensions Disease Progression Feeling suicidal Fingerprint Functional Magnetic Resonance Imaging Funding Goals Human Hybrids Image Individual Investigation Joints Lead Libraries Link Longitudinal Studies Major Depressive Disorder Measures Mental Depression Mental Health Mental disorders Methods Modeling National Institute of Mental Health Neural Pathways Neurosciences Research Patients Phenotype Physiological Prediction of Response to Therapy Psychiatry Pythons Regimen Relapse Research Sample Size Selection for Treatments Statistical Methods Strategic Planning Structure Symptoms Time Treatment outcome United States National Institutes of Health Validation analytical tool base behavior change brain behavior clinical biomarkers cohort complex data computer framework denoising depressive symptoms disorder subtype effective therapy endophenotype feature extraction graphical user interface imaging modality imaging study improved independent component analysis individualized medicine innovation insight learning strategy magnetic resonance imaging/electroencephalography method development multidimensional data multimodality neural circuit neural network neurobiological mechanism neuroimaging neuroimaging marker novel predict clinical outcome predictive marker predictive modeling predictive tools relating to nervous system tool treatment planning treatment response user friendly software

项目摘要

Project Summary/Abstract Recent mental health studies have led to an expanded depth of multimodal brain imaging data, clinical assessments and physiological data. In addition, longitudinal studies have become increasingly important to capture the trajectory of disease progression, treatment response and relapse. This wealth of datasets provides an unprecedented opportunity for crosscutting investigations. However, much-needed statistical methods for exploring discoveries are lacking. In particular, there has been very limited development of advanced statistical methods for several important objectives: decompose observed brain connectivity measures to reveal underlying neural circuits which are key biomarkers for mental disorders, effectively extract low dimensional neural features from imaging to reliably predict clinical outcomes such as treatment response, and analyze longitudinal multidimensional data including neuroimaging, clinical and behavioral assessments to study the dynamic interplay between brain and behavior changes due to treatments. In this competing renewal proposal, we will build upon the theoretical and computational framework established in our previous award to develop rigorous and computationally efficient statistical methods to address the aforementioned objectives. Specifically, we plan to develop 1) a sparse and low rank ICA (SLR- ICA) framework for reliable and parsimonious decomposition of brain connectivity measures to reveal underlying neural circuits associated with specific clinical symptoms in mental disorders; 2) an ICA-Neural Network (ICA-NN) predictive model that effectively extracts relevant low dimensional linear and non-linear neural features to predict clinical outcomes; and (3) longitudinal multidimensional data analysis tools for investigating heterogeneous changes in neural circuits due to different treatments and disease subtypes, and disentangle the relationship between changes in neuroimaging phenotypes and clinical symptoms. The statistical methods will be applied to a major NIH funded longitudinal study of major depressive disorder (MDD) to help discover neural circuits underlying specific depressive symptoms (e.g. suicidal thoughts) and differential treatment response, and ultimately help lead to more effective treatment for individual MDD patients based on his/her own neural circuitry fingerprints and behavior. We plan to replicate the findings using an independent validation cohort from an R01 study of MDD. User-friendly software will be made available to general research communities. Our proposed method developments will directly benefit mental health research by providing innovative statistical tools to effectively extract reliable and highly relevant low dimensional features from neuroimaging to deepen mechanistic understanding and improve treatment of MDD and other mental disorders.

项目总结/文摘