A Unified Framework for Flexible Brain Image Analysis

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DESCRIPTION (provided by applicant): Data driven methods are being increasingly used to analyze brain imaging data. FMRI analyses can be put on an analytic spectrum with heavily model-based approaches (like the general linear model (GLM) implemented in the SPM software) on one end and flexible data-driven approaches like independent component analysis (ICA), principal component analysis (PCA), or clustering on the other end. In between there is a gap, which we and others have been trying to fill. In particular, methods such as ICA are particularly useful for reducing the multivariate fMRI problem down to one that is both tractable and also enables the incorporation of prior information. In the first period of this competing renewal, we focused our efforts upon developing ICA of fMRI methods which would be suitable for making group inferences, and which would allow the incorporation of prior information, hence moving from a 'blind' ICA approach to a semi-blind ICA approach. Despite the progress we have made, there is still considerable work to be done in the analysis of fMRI data with ICA. In this competing renewal, we propose to continue and significantly expand this work. First, we will extend our semi-blind ICA (sbICA) framework to provide a general framework for incorporating prior information from multiple spatial and temporal sources. In the second aim we will focus upon statistical inference and develop a framework for integrating the relevant functional components. In the third aim, we will validate the algorithms in aims 1 and 2, including using fMRI data collected on multiple days from a variety of paradigms. In this aim we develop a decision mechanism for selecting the best combination of methods given a particular problem. For the fourth aim, we will apply our methods to data collected during four well-studied paradigms in healthy controls and patients with schizophrenia. Our final aim involves the continuing development of our GIFT toolbox, and incorporation of the above algorithms, constraint selection mechanisms, and visual interfaces into the software. The successful completion of this research will provide a powerful set of tools for the research community to increase the sensitivity and specificity of BOLD analysis methods by drawing upon the strengths of both model-based and data-driven approaches. These tools will also provide a way to study the inter-relationship among functional networks in a flexible manner. This has application not only in schizophrenia but in many other diseases such as Alzheimer's, attention deficit hyperactivity, and psychopathy.