The overall objective of this research project is the development and validation of a computer-based method that helps in the analysis of functional patterns occurring in 3-dimensional human brain images produced by PET or SPECT cameras. This method is related to health problems since it (1) characterizes patterns of brain dysfunction occurring in mental illness; (2) relates patterns of metabolic activity caused by medication, drug abuse or other stimuli to anatomical areas of the brain; (3) detects pattern differences between two groups; (4) detects subgroups within a heterogeneous group helping in the clinical classification of a single brain. Heretofore, most methods have relied on a priori definitions of anatomical Regions of Interest (ROI) to reduce the amount of functional information to be analyzed, but in the process lose the functional patterns that span the whole brain or fall outside the selected ROI. Throughout the years scientists have steadily increased the number of predefined ROI's until they cover the whole brain. However, due to the small number of subjects in a PET study and the large inter- subject variability, statistical limitations on the significance of the findings come into play. The objective method we seek has several advantages over existing methods. (1) It will be designed to extract salient geometric information from each whole 3-dimensional PET image, such as the brain's geometric centroid and the brain's principal geometric axis, providing a reference frame which is insensitive to the positioning of the subject's head within the PET camera without requiring additional X-ray or MRI images; (2) it will use this reference frame to analyze the information contained in the whole functional image rather than just the functional information contained in a priori selected anatomical regions of interest; (3) it will use descriptive features of the functional image which enhance its signal to noise ratio, both for deterministic, random noise and photon scatter, i.e., partial volume effect, typical of functional images obtained from PET or SPECT cameras; thus, a good intersubject averaging can be obtained with a smaller number of subjects; (4) it will be able to map the detected functional pattern to a Brain Atlas or MRI scan thus selecting a pattern of anatomical regions of interest a posteriori. The method will be validated both with phantom brains and with groups of PET images including resting, motor-activated and optically activated normals. It will be tested with groups of PET images including normals and schizophrenics.
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