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We derived that information by encoding each patient's lesion as the proportions of anatomically defined regions of interest that the lesion appears to destroy. The cortical regions of interest were extracted from the Anatomy Toolbox (Eickhoff et al., 2005), and the white matter tracts of interest were extracted from the ICBM-DTI-81 white-matter label atlas (Mori et al., 2006) and the JHU white-matter tractography atlas (Hua, 2008). There were 232 regions in all, so each patient was associated with 232 ��lesion site�� predictors, varying in the range 0�C1. This move to atlas-based lesion coding is effectively a kind of dimensionality reduction �� the benefit being that the resultant predictors might be more interpretable than those extracted using more traditional numerical methods. By replacing the 2 lateralised lesion volume predictors with these 232 new atlas-based predictors, we improved the predictions again (R2?=?0.52, F?=?294.24, p?[http://www.selleck.co.jp/products/z-vad-fmk.html Z-VAD-FMK purchase] although the reduction in prediction [http://www.selleck.co.jp/products/Everolimus(RAD001).html Everolimus] error was not significant (Wilcoxon test: Z?=?1.34, p?=?0.18). It would be very surprising if every one of those 232 regions was equally relevant to the implementation or recovery of speech production skills. The implication is that many or most of our atlas-based predictors are irrelevant to the task at hand, and to the extent that this is true, random correlations between those irrelevant predictors and the target measure would be expected to hamper effective predictions (Oomen et al., 2008), masking the benefits of including higher resolution lesion site information. By throwing those less relevant predictors away, we expected to see at least some improvement in the overall performance of the system �� as well as a significant reduction in prediction error relative to learning with lateralised lesion volume (as in Section 3.4). To [http://en.wikipedia.org/wiki/I%CE%BAB%CE%B1 Bay 11-7085] select relevant lesion features, we used Automatic Relevance Determination (ARD) �� a Bayesian filter method driven by an initial pass of Gaussian process model learning across the whole of the dataset, in which individual hyperparameters are learned for each predictor (Mackay, 1992?and?Neal, 1996). Our implementation of the approach is adapted from the NetLab software package (Nabney, 2001). Armed with this reduced set of scores, we re-ran the validation multiple times with increasingly large subsets of the predictors, focussing on those judged most relevant by ARD (i.e. growing the predictor configuration in the order defined by their relative relevance scores). Fig.?3 displays the variance explained by the subsets containing the first 5�C65 of the predictors selected by ARD. The best subset included 37 relevant predictors (R2?=?0.59, F?=?38.38, p?