Abstract
Defining brain structures of interest is an important preliminary step in brain-connectivity analysis. Researchers interested in connectivity patterns among brain structures typically employ manually delineated volumes of interest, or regions in a readily available atlas, to limit the scope of connectivity analysis to relevant regions. However, most structural brain atlases, and manually delineated volumes of interest, do not take voxel-wise connectivity patterns into consideration, and therefore may not be ideal for anatomic connectivity analysis. We herein propose a method to parcellate the brain into regions of interest based on connectivity. We formulate connectivity-based parcellation as a graph-cut problem, which we solve approximately using a novel multi-class Hopfield network algorithm. We demonstrate the application of this approach using diffusion tensor imaging data from an ongoing study of schizophrenia. Compared to a standard anatomic atlas, the connectivity-based atlas supports better classification performance when distinguishing schizophrenic from normal subjects. Comparing connectivity patterns averaged across the normal and schizophrenic subjects, we note significant systematic differences between the two atlases.
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Acknowledgments
This work was supported by the National Institutes of Health (R01MH085646, P50MH103222, and R01DA027680 to LEH) and by the University of Maryland’s Center for Health Informatics and Bioimaging, and the State of Maryland MPower initiative (to EHH and JJ).
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Wang, Q., Chen, R., JaJa, J. et al. Connectivity-Based Brain Parcellation. Neuroinform 14, 83–97 (2016). https://doi.org/10.1007/s12021-015-9280-7
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DOI: https://doi.org/10.1007/s12021-015-9280-7