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--- mkda [2011/12/08 22:47] – updated jochen
+++ mkda [2012/02/28 16:08] – added description of algorithm jochen
@@ Line 79: / Line 79: @@
 For the actual procedure of running the MKDA, please refer to the [[neuroelf_gui - MKDA UI|MKDA UI]] article.
+===== Algorithm description =====
+The general algorithm works as follows:
+  * potentially, a sub-selection of peak coordinates is made (based on conditional statements, e.g. to remove points that do not adhere to specific criteria)
+  * for each study or contrast (whatever is used as statistical unit) included in a given analysis a weight is computed (i.e. normally this weight is equal across peaks within a study/statistical unit, and the weight is based on an expression that may contain, for instance, the number of subjects that was included in the study for which the peaks are reported and included)
+  * next, a voxel based volume is initialized (filled with zeros) for each of the statistical units, and for each point in any given study a blob (with configurable size and value distribution, e.g. a 0-or-1 indicator sphere or a gaussian kernel) is added to the corresponding volume
+  * eventually, these volumes are combined using the weights for each of the statistical units (weighted sum)
+  * to draw inferences, an empirical null distribution is derived by either spatially scrambling coordinates within a reasonable mask, such as a grey matter volume in the same space (in which case the null hypothesis tests whether the observed summary statistic of the weighted sum of blobs in any given location is higher than warranted by chance for that particular location) or by scrambling the labels of peaks (i.e. in a differential contrast where two sets of peaks reported for different activation states/modes are to be compared, and the inferential test determines whether for any given spatial location the observed summary statistic is significantly outside the empirical null distribution under the assumption that labels for reported points do not carry information about the activation state/mode)
+  * to allow fMRI-typical inference (uncorrected thresholding, FWE-thresholding and cluster-size based thresholding), each summary statistical map (under the null hypothesis) is added to an overall null-distribution (uncorrected thresholding), its most extreme value is recorded (FWE thresholding), and is clustered at various uncorrected thresholds to determine the cluster size threshold to get to a FWE corrected threshold using both height and cluster size thresholds