** This is a repository for the dcan labs bold signal processing. It is forked from FNL_preproc and is meant to take its place. **
This code repository consists of python wrappers and matlab scripts for signal processing of the bold signal extracted from fMRI data. This program is designed for the explicit output data of the HCP fMRI pipeline or its DCAN derivatives. It is not designed with other preprocessed data in mind, so use at your own peril.
Installation requires use of the matlab compiler tool distributed with matlab, or acquiring an up-to-date version of the binaries available upon request.
git clone git@gitlab.com:Fair_lab/dcan_signal_processing.git
cd dcan_signal_processing
./compile.sh <matlab compiler path>
main wrapper for signal processing scripts.
usage: dcan_bold_proc.py [-h] [-v] [--setup] --subject SUBJECT --task TASK
[--output-folder OUTPUT_FOLDER]
[--legacy-tasknames]
[--filter-order FILTER_ORDER] [--lower-bpf LOWER_BPF]
[--upper-bpf UPPER_BPF] [--fd-threshold FD_THRESHOLD]
[--skip-seconds SKIP_SECONDS]
[--contiguous-frames CONTIGUOUS_FRAMES]
[--brain-radius BRAIN_RADIUS]
[--motion-filter-type {notch,lp}]
[--motion-filter-order MOTION_FILTER_ORDER]
[--band-stop-min BAND_STOP_MIN]
[--band-stop-max BAND_STOP_MAX]
[--motion-filter-option MOTION_FILTER_OPTION]
[--teardown] [--tasklist TASKLIST] [--physio PHYSIO]
Wraps the compiled DCAN Signal Processing Matlab script, version: 4.0.0.
Runs in 3 main modes: [setup], [task], and [teardown].
[setup]: creates white matter and ventricular masks for regression, must be
run prior to task.
[task]: computes fd numbers [1][2], runs regressions on a given task/fmri [3]
and outputs a corrected dtseries, along with motion numbers in an
hdf5 (.mat) formatted file.
[teardown]: concatenates any resting state runs into a single dtseries, and
parcellates all final tasks.
optional arguments:
-h, --help show this help message and exit
-v, --version print the software name and version
--setup prepare white matter and ventricle masks, must be run
prior to individual task runs.
--subject SUBJECT subject/participant id
--task TASK name of fmri data as used in the dcan fmri pipeline.
For bids data it is set to "task-NAME"
--output-folder OUTPUT_FOLDER
output folder which contains all files produced by the
dcan fmri-pipeline. Used for setting up standard
inputs and outputs
--legacy-tasknames
parse input task names as done in dcan_bold_processing <= 4.0.4.
use this flag if the input task filenames use the older DCAN HCP
pipeline filename convention in which run index is appended to
task name, e.g. task-myTask01 instead of task-myTask_run-01.
bold signal filtering:
bold signal filtering parameters.
--filter-order FILTER_ORDER
number of filter coefficients for butterworth bandpass
filter.
--lower-bpf LOWER_BPF
lower cut-off frequency (Hz) for the butterworth
bandpass filter.
--upper-bpf UPPER_BPF
upper cut-off frequency (Hz) for the butterworth
bandpass filter.
framewise displacement:
parameters related to computation of framewise displacment (FD)
--fd-threshold FD_THRESHOLD
upper framewise displacement threshold for use in
signal regression.
--skip-seconds SKIP_SECONDS
number of seconds to cut off the beginning of fmri
time series.
--contiguous-frames CONTIGUOUS_FRAMES
number of contigious frames for power 2014 fd
thresholding.
--brain-radius BRAIN_RADIUS
radius of brain for computation of rotational
displacements
--motion-filter-type {notch,lp}
type of band-stop filter to use for removing
respiratory artifact from motion regressors. Current
options are 'notch' for a notch filter or 'lp' for a
lowpass filter.
--motion-filter-order MOTION_FILTER_ORDER
number of filter coeffecients for the band-stop
filter.
--band-stop-min BAND_STOP_MIN
lower frequency (bpm) for the band-stop motion filter.
--band-stop-max BAND_STOP_MAX
upper frequency (bpm) for the band-stop motion filter.
--motion-filter-option MOTION_FILTER_OPTION
determines direction(s) in which to filter respiratory
artifact. Default is all directions.
--physio PHYSIO input .tsv file containing physio data to
automatically determine motion filter parameters.
Columns, start time, and frequency will also need to
be specified. NOT IMPLEMENTED.
final concatenation:
final stage parameters for after setup and tasks. Concatenates, parcellates,
and saves combined FD numbers.
--teardown flag to run final concatenation steps. After tasks
have completed, concatenate resting state data and
parcellate.
--tasklist TASKLIST comma delimited tasks to be concatenated, pass in
argument multiple times to add more task lists. Also
determines which tasks will be parcellated, so a
single task may be input to parcellate it. Required
for this stage. May be a list of one.
The script is run with calls to three "modes":
creates ventricular and wm masks from the anatomical segmentations, and computes mean time courses in these classes for use in bold regression later on.
for each fmri run, this script is called to perform regressions on motion, ventricular and white matter signals, as well as mean signal regression. Framewise displacement (FD) is calculated on the motion numbers and used for regression, but also saved for available use in FD thresholding. If motion band-stop parameters are specified, the motion numbers are first filtered in each spatial dimension then FD is computed. The resulting time series is saved along with a 'grayplot' displaying relevant time series data.
concatenates any resting state data which shares the same bids task name, also concatenates any FD numbers and saves out a matlab (hdf5) file with various FD threshold masks computed.
[1] Damien A. Fair, Oscar Miranda-Dominguez, et al. Correction of respiratory artifacts in MRI head motion estimates. NeuroImage, Volume 208, 2020, doi:10.1016/j.neuroimage.2013.08.048.
[2] Power J, et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage [Internet]. Elsevier Inc.; 2014 Jan 1 [cited 2014 Jul 9];84:32041. doi:10.1016/j.neuroimage.2013.08.048
[3] Friston KJ, et al. Movement-related effects in fMRI time-series. Magn Reson Med [Internet]. 1996;35(3):34655. doi:10.1002/mrm.1910350312