Changes.log
22/Dec/2015
Files are pointing to our lab's git repository https://git.becs.aalto.fi/bml/bramila |
bramila_framewiseDisplacement.m (02.10.2014) bramila_detrend.m (28.07.2014, used by bramila_framewiseDisplacement) bramila_dvars.m (09.03.2014) bramila_bold2perc.m (10.01.2014)
% BRAMILA_FRAMEWISEDISPLACEMENT - Computes the framewise displacement % metric as described in % Power et al. (2012) doi:10.1016/j.neuroimage.2011.10.018 and also % Power et al. (2014) doi:10.1016/j.neuroimage.2013.08.048 % - Usage: % fwd = bramila_framewiseDisplacement(cfg) % - Input: % cfg is a struct with following parameters % cfg.motionparam = the 6 time series of motion parameters (time in 1st dimension) % cfg.prepro_suite = 'fsl-fs', 'spm' (default fsl-fs, fs = freesurfer) % cfg.radius = radius of sphere in mm to convert degrees to motion, % default = 50 as in Power et al 2014 % - Output: % fwd = framewise displacement timeseries % - Notes: % Need to check that spm is indeed different, see end of Yan 2013 10.1016/j.neuroimage.2013.03.004 % BRAMILA_DVARS - Computes Derivative VARiance across voxels as defined in % Power et al. (2012) doi:10.1016/j.neuroimage.2011.10.018 % - Usage: % dvars=bramila_dvars(cfg) Returns a time series 'dvars' with a value of % RMS for each time point. First time point is set to 0. % - Input: % cfg is a struct with following parameters % Possible input formats % cfg.nii = 'path/to/a/nifti/file' - insert the full path to a nifti % file with 4D fMRI data % cfg.vol = vol - a matlab 4D volume with fMRI data, time on the % 4th dimension % cfg.ts = ts - a two dimensional vector of time series, time on the % 1st dimension % cfg.plot = 0 or 1 - set to 1 if you want to output a plot like in % Power et al. (2014) doi:10.1016/j.neuroimage.2013.08.048 (defult 0) % cfg.mask = a 3D matlab volume mask of voxels to consider for RMS computation % (the parameter is ignored if cfg.ts is specified) % - Note: % if more than one input format is specified matlab will give % priority to cfg.ts > cfg.vol > cfg.nii % BRAMILA_BOLD2PERC - Converts a time series with mean into a time series of percentage changes. % - Usage: % ts_perc = bramila_bold2perc(ts) ts is a matrix NxM where N is the % number of time points. Values returned are in percentages % - Notes: % If the mean is zero, then the absolute maximum is used. % % The formula used follows the SPM convention, i.e. we first normalize the % time series so that they have 100 as mean value.