Abstract
We have determined a new method for quantifying the nonlinearities of brain imaging data sets to allow comparison of measures of nonlinearity across comparable experiments. We have an efficient algorithm for finding nonlinear features of high dimensional data from variations in two- dimensional projections and slices. By computing the second-order statistics of these features, we are able to construct an analogue of the Riemann Curvature Tensor, thereby describing the nonlinearity of the data set. We demonstrate the usefulness of this technique on real MEG and fMRI data.
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