The goal of this project is to address a great need for developing efficient brain informatics tools for the analysis and management of large collections of brain images (from various imaging modalities) and associated clinical data. These automated tools will enable interoperable brain image data representation that is easy to search while focusing on the management of the spatial regions of interest (ROIs) under a general unified framework regardless of whether these are lesions, tumors, areas of brain activation, or regions of (normal/abnormal) morphological variability of a variety of brain structures. We envision this brain informatics system as a platform for the effective and efficient analysis of a large number of epidemiological studies. Towards these ends we propose four specific aims: (a) development of efficient methods for the quantitative characterization and classification of ROIs, (b) development of fast and effective database techniques supporting efficient retrieval of similar regions of interest in large brain image databases as well as spatial data mining tools for discovering associations between anatomic and other variables such as function, pathology, or response to drugs, (c) integration of the above techniques with morphological analysis tools to correlate morphological changes to changes of other measurements such as functional, physiological, etc, (d) evaluation of the proposed techniques using real and simulated data. We will demonstrate the utility of the proposed techniques in the analysis of large data sets from a number of epidemiological studies of brain morphology and function. The data sets we propose to analyze are (a) MR spectroscopy and anatomic MRI correlation representing disease states such as multiple sclerosis, stroke, tumors and neurologic disease states (2,400 participants), (b) structural MR data on Schizophrenia (more than 500 participants), (c) structural and functional MR data from normal volunteers and patients with stroke, head trauma, and epilepsy (an ongoing study with over 150 participants), (d) structural and functional MR data on Alzheimer disease (an ongoing study with over 30 participants), (e) structural and functional MR data from a study on aging (an ongoing study with over 45 participants) and (f) an Alzheimer structural MR data from a diverse group of 40 participants. Through large-scale data analysis we will provide new insight into the relation of brain structure and function.
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