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--- neuroelf_methods [2013/02/01 18:30] – jochen
+++ neuroelf_methods [2013/02/01 21:30] – jochen
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 ===== List of methods (overview) =====
 The following list gives an overview on what methods of analysis and parameter estimation are implemented in NeuroElf (as far as they exceed basic operations, such as for example plain averaging across a dimension):
+==== Cluster size threshold estimation (alphasim) ====
+[[alphasim|Cluster size threshold estimation]] is a method that can be used to account for the fact that a regular whole-brain map is made up of multiple (partially) independent tests. One common way is to simply adapt the statistical threshold by dividing the desired false-positive rate (i.e. typically 5 per cent = 0.05) by (an estimate of) the number of independent tests. However, this can be too stringent in some cases where larger swaths of cortex (neurocomputational network nodes) respond to an experimental manipulation below the then required detection threshold. Instead of ensuring significance of results solely by applying a voxel-wise corrected statistical threshold it is possible to estimate **how large clusters are, given the smoothness of the residual, that appear in a given search space at random**. I.e. the alpha-rate (false positives among performed tests) can be estimated by simulating statistical maps of the desired kind and then selecting the appropriate cluster size threshold to ensure that at most 5 per cent of maps (with the residual exhibiting the same smoothness) would show a false positive cluster. The resulting **pair of uncorrected statistical threshold and cluster size threshold together** then correct a whole-brain map to a family-wise-error corrected threshold of desired strength (again usually 0.05). This algorithm is
+  * implemented in function **''[[alphasim|alphasim.m]]''**
+  * accepts a mask (sub-space specification)
+  * can be applied to surface statistics (given the mesh vertices and topology, as well as an estimate of the smoothness)
+  * allows to estimate the cluster size threshold for fully independent components of a conjunction analysis
+  * as a still experimental feature allows to apply a shift in the Z-distribution to account for shifts in the observed distribution of a statistical map (e.g. by-chance global "signal" in a covariate regression)
+==== Conjunction analysis (minimum t-statistic) ====
+A [[Conjunction analysis|conjunction analysis]] can be informative when, across the brain, the overlap of two statistical tests is of interest. The most stringent test that can be applied is that of requiring that, in each considered voxel, both tests must be significant at the desired level. This functionality is
+  * implemented in the function **''[[conjval]]''** for statistics of the same kind and with the same D.F. parameter, i.e. higher value means greater significance
+  * implemented in the function **''[[conjvalp]]''** for p-values (and possibly other statistics for which lower values mean greater significance; also accepts negative values)
 ==== Mediation analysis ====
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   * implemented as a method for [[xff - PLP format|PLP objects]], [[plp.MKDA|PLP::MKDA]]
   * available via the [[NeuroElf GUI - MKDA UI|Meta Analysis interface]]
+==== Ordinary least-squares (OLS) regression ====
+[[OLS|Ordinary least-squares (OLS) regression]] is the most generic way of applying the General Linear Model (GLM) so as to estimate "effect sizes". Given the different applications, there are several functions implementing forms of this regression:
+  * the most general implementation is done in the **''[[calcbetas|calcbetas.m]]''** function
+    * to assess the significance of the regression (single beta or computed contrasts), the **''[[glmtstat|glmtstat.m]]''** function must be used
+  * a special implementation is contained in the **''[[rbalign|rbalign.m (rigid-body alignment)]]''** function that uses the GLM framework to estimate motion parameters
+An additional small number of function files also perform some flavor of linear regression, but those are not applied to functional imaging data (e.g. the function **''[[regress_coords|regress_coords.m]]''** can be used to determine the transformation required to minimize the error between two sets of coordinates after a rigid-body transform).
 ==== Robust regression ====
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   * a special case is for when, in a correlation, both "dependent" and "independent" variables may contain outliers, in which case **''[[robcorrcoef|robcorrcoef.m]]''** should be used, which uses both as the explanatory variable as quick check
   * and to compare means between groups, the two simplified functions **''[[robustnsamplet|robustnsamplet.m]]''** and **''[[robustnsamplet_img|robustnsamplet_img.m]]''** are available as well