![]() By iterating this procedure across different positions in the brain it is possible to obtain a map of diagnostic accuracies for each coordinate cvi, depicting the diagnostic information contained in local patterns in decoding the clinical condition. (C) The resulting accuracy was then noted at the coordinate cvi as the local information related to the clinical condition. The success of the classifier is an estimate of the local information at that position in the brain. This leaveoneout (LOO) crossvalidation procedure was then repeated ntimes by leaving out the data of one subject at a time from the training data set. The classifier is then tested by applying it to the data from the remaining ‘test’ subject (in this example NHC). The data from all (Ntotal = NMS + NHC) but one subject (Ntotal1) are used as a ‘training dataset’ to train a classifier to distinguish between patterns from the two groups. (B) Within this cluster of voxels the spatial pattern of intensities is extracted for each subject separately. However, when the center voxel was located closer to a boundary of the search space of a given analysis the searchlight could deviate from the spherical shape and contain less voxels in order to guarantee that only voxels belonging to the supposed tissue class were contained in searchlights in a given analysis. Thus, a searchlight contained 123 spherically arranged voxels if the minimal distance to the boundary of the search space was at least three voxels. For a given ‘center’ voxel cvi in the brain the searchlight is defined as a spherical cluster with a radius of three voxels surrounding the center coordinate. (A) ‘Searchlight’ approach that searches across the brain for local tissue intensity patterns that are informative about the clinical condition (MS, healthy control ). ![]()
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December 2022
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