Analyzing functional brain images in a probabilistic atlas: A validation of subvolume thresholding

Purpose: The development of structural probabilistic brain atlases provides the framework for new analytic methods capable of combining anatomic information with the statistical mapping of functional brain data. Approaches for statistical mapping that utilize information about the anatomic variability and registration errors of a population within the Talairach atlas space will enhance our understanding of the interplay between human brain structure and function. Method: We present a subvolume thresholding (SVT) method far analyzing positron emission tomography (PET) and single photon emission CT data and determining separately the statistical significance of the effects of motor stimulation on brain perfusion. Incorporation of a priori anatomical information into the functional SVT model is achieved by selecting a proper anatomically partitioned probabilistic atlas for the data. We use a general Gaussian random field model to account for the intrinsic differences in intensity distribution across brain regions related to the physiology of brain activation, attenuation effects, dead time, and other corrections in PET imaging and data reconstruction Results: (H2O)-O-15 PET scans were acquired from six normal subjects under two different activation paradigms: left-hand and right-hand finger-tracking task with visual stimulus. Regional region-of-interest and local (voxel) group differences between the left and right motor tasks were obtained using nonparametric stochastic variance estimates. As expected from our simple finger movement paradigm, significant activation (z = 6.7) was identified in the left motor cortex for the right movement task and significant activation (z = 6.3) for the left movement task in the right motor cortex. Conclusion: We propose, test, and validate a probabilistic SVT method for mapping statistical variability between groups in subtraction paradigm studies of functional brain data. This method incorporates knowledge of, and controls for, anatomic variability contained in modern human brain probabilistic atlases in functional statistical mapping of the brain.