Nico S. Gorbach

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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(More)
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(More)
One of the most promising avenues for compiling anatomical brain connectivity data arises from diffusion magnetic resonance imaging (dMRI). dMRI provides a rather novel family of medical imaging techniques with broad application in clinical as well as basic neu-roscience as it offers an estimate of the brain's fiber structure completely non-invasively and(More)
Gaussian processes are powerful, yet analytically tractable models for supervised learning. A Gaussian process is characterized by a mean function and a covariance function (kernel), which are determined by a model selection criterion. The functions to be compared do not just differ in their parametrization but in their fundamental structure. It is often(More)
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