This is a toolbox for spm8, allowing you to assess the lateralization of activation in functional MRI (although any image volume can be evaluated), as described by Wilke & Lidzba (2007) and Wilke & Schmithorst (2006). Several different algorithms have been implemented. It can be called from the spm8 toolbox menu as well as from the command line and now also allows generating jobs from within the spm8 batch editor. It requires Matlab and spm8.
This is a toolbox for spm8, allowing you to semi-automatically assess a structural MR-image with regard to the presence of brain lesions, using a "clusterize" algorithm as described by Clas et al. (2011). Several different options allow assessing T1, T2, or FLAIR images. It can be called from the spm8 toolbox menu. It requires Matlab and spm8 or 12. In its newest (beta) Implementation, contained in the download, it now also allows assessing CT images as described in de Haan et al. (2015).
This is a function for spm8 or spm12, allowing you to automatically assess the effects of motion in an fMRI timeseries, as described by Wilke (Neuroimage 2012) and Wilke (PLoS 2014). Several different options allow generating a more comprehensive indicator of motion (combining translation and rotation) as well as generating a motion mask and a "motion fingerprint". This fingerprint characterizes the effects of motion in an individual timeseries and can then be used in ensuing statistical analyses. It can be used interactively, can easily be scripted for command line usage, and now finally also features full spm12 batch integration. It requires Matlab and spm8 or spm12.
This is a toolbox for spm12 that allows you to generate matched tissue probability maps (and potentially T1's) for tissue segmentation and spatial normalization. They are based on statistically-generated regression parameters from a large sample of healthy infants, children, and adults. Extending the approach of the Template-O-Matic toolbox, the CerebroMatic now uses a more flexible statistical approach, namely multivariate adaptive regression splines. More details can be found in Wilke et al., 2017. It requires Matlab and spm12, as well as the AresLab toolbox.
Optimized Censoring Toolbox
This is a toolbox for spm12 that allows you to identify outlying datapoints in single subject fMRI sessions, based on three outlier criteria which are balanced using the corrected Akaike outlier criterion. The toolbox will censor and/or interpolate outliers in the final statistical analyses with the aim to salvage "bad" sessions especially in the context of clinically-indicated fMRI sessions. More details can be found in Wilke & Baldeweg, 2019. It requires Matlab and spm12.