Abstract
One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (”the functional fingerprint”) is closely related to its anatomical connections (”the connectional fingerprint”) and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Existing clustering techniques in the context of connectivity-based cortex parcellation are usually exploratory. We therefore advocate an information-theoretic framework for connectivity-based cortex parcellation which avoids many assumptions imposed by previous methods. Clustering is based upon maximizing connectivity information while allowing noise in the data to vote for the optimal number of cortical subunits. The automatic parcellation of the inferior frontal gyrus together with the precentral gyrus reveals cortical subunits consistent with previous studies.
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Gorbach, N.S., Siep, S., Jitsev, J., Melzer, C., Tittgemeyer, M. (2012). Information-Theoretic Connectivity-Based Cortex Parcellation. In: Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science(), vol 7263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34713-9_24
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DOI: https://doi.org/10.1007/978-3-642-34713-9_24
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