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version 1.0.1 (57.2 KB) by John Williams
Optimal volume censoring methods for high-resolution (low TR) simultaneous multi-slice (multiband) RSFC fMRI.


Updated 14 Jan 2020

GitHub view license on GitHub

This software allows a user to get optimal LPF-FD and GEV-DV volume censoring parameters based on a given dataset size, and, using either these parameters or other, user-specified parameters, obtain a vector of volumes censored using LPF-FD and GEV-DV methods using a given matrix of motion parameters (MPs) and a resting-state fMRI time series (either a voxel or greyordinate time series) and brain mask, respectively. These methods may be used separately or in tandem, and using images that are either pre-loaded into MATLAB or loaded using user-specified file names.

1. [optimal_LPFFD_threshold,optimal_GEVDV_d] = …
This is a function that can be used to obtain the optimal LPF-FD and GEV-DV thresholds given a specified number of volumes per run, the number of runs per subject, and whether the data are additionally processed using global signal regression (GSR).

2. [censoredVolumes,lpffdCensoredVolumes,lpfdvCensoredVolumes] = …
This function returns a boolean (logical) vector of censored volumes using combined LPF-FD and GEV-DV based volume censoring, given a time series (either as a 4D matrix of voxel time series, or a 2D matrix of greyordinate time series), a gray matter mask (3D matrix, or 1D with columns corresponding to greyordinates), a matrix of motion parameters (MPs), the TR (required for low-pass filtering), and the desired LPF-FD threshold and DV-GEV d. It calculates the censored volumes using LPF-FD by calling lpfFDcensoredVolumes and the censored volumes using GEV-DV by calling lpfDVcensoredvolumes.

3. [censoredVolumes,lpffdCensoredVolumes,lpfdvCensoredVolumes] = …
This is a wrapper for getCensoredVolumes that opens a nifti or cifti time series using niftiread. It is identical, except that instead of taking in time series and a gray matter mask, it takes in the file name of the time series and the file name of the gray matter mask. The repetition time (TR) is obtained from the nifti header. It opens them using niftiread and calls getCensoredVolumes; the outputs are identical.

4. [lpffdCensoredVolumes] = lpfFDcensoredVolumes(MPs,TR,LPF_FD_Threshold)
This function returns a logical vector denoting which volumes are targeted for removal using LPF-FD based on input motion parameters, the TR (for low-pass filtering), and an LPF-FD threshold desired by the user.

5. [lpfdvCensoredVolumes] = lpfDVcensoredvolumes(timeSeries,brainMask,TR,DV_GEV_d)
This function returns a logical vector denoting which volumes are targeted for removal using GEV-DV based on input time series, a gray matter mask in the same space as the input time series, the TR (for low-pass filtering), and a GEV-DV d parameter desired by the user.

This is a MATLAB software package to accompany the article:
Motion denoising of multiband resting state functional connectivity MRI data: An improved volume censoring method.
John C. Williams, MS and Jared X. Van Snellenberg, PhD.
Multi-Modal Translational Imaging Laboratory
Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University
Stony Brook, NY, USA.

This software requires MATLAB R2017b or later running on any operating system. This was created and tested using MATLAB R2019b. This software does not require any non-standard software.

Installation of this software consists of downloading, copying, or moving all functions into a directory accessable to your MATLAB installation. If downloaded as a zip file, all functions should be unzipped to a single folder before use. Unzipping this software is expected to take less than one minute on a normal desktop computer.

This software is released under the GNU General Public License Version 3.

Cite As

John Williams (2020). Multiband_fMRI_Volume_Censoring (, GitHub. Retrieved .

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Minor update to Description.

MATLAB Release Compatibility
Created with R2019b
Compatible with R2017b to any release
Platform Compatibility
Windows macOS Linux