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mkda [2012/02/28 17:08]
jochen added description of algorithm
mkda [2012/02/28 17:17] (current)
jochen [Algorithm description]
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   * 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)   * 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)   * 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 +  * 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 ​at/around the peak coordinate 
-  * eventually, these volumes are combined using the weights for each of the statistical units (weighted sum) +  * eventually, these volumes are combined using the weights for each of the statistical units (weighted sum along the dimension of the statistical unit, resulting in a 3-dimensional spatial map
-  * 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 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 ​if the reported peaks didn't carry any information on the actual spatial location/​distribution of a neuropsychological state/mode) or by scrambling the labels of peaks across units (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; **Note: in case a differential contrast, e.g. task A > task B, is to be examined, it is recommended to also establish that any locations showing a significant difference are also spatially selective of the tasks, i.e. having a value outside of the spatial-scrambling null distribution for task A + task B!**)
   * 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   * 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
mkda.txt · Last modified: 2012/02/28 17:17 by jochen