Download Links Description

  1. rsfmri-raw (Volume-NIFTI): the raw resting-state fMRI data in NIFTI format
  2. rsfMRI-noregr (Volume-NIFTI): the resting-state fMRI data after mininal preprocessed pipeline (no temporal denosing) in NIFTI format. Data was spatial transformed to the MBMv3 template space.
  3. rsfMRI-noregr (Surface-CIFTI): the resting-state fMRI after mininal preprocessed pipeline and mapped on to the MBMv3 surface in CIFTI format. Note the All runs in one session were concatenated into one file.
  4. rsfMRI-regrBMWC0 (Volume-NIFTI): the resting-state fMRI after full preprocessed pipeline, including mininal-preprocessing, and de-noising by regressing out linear and quadratic trends, band-pass regressors (0.01–0.1 Hz), demeaned and derivatives of motion parameters, white matter signals, csf signals, and motion-censor regressors (any TR and the previous TR were censored if the motion was >0.2 mm). Data was spatial transformed to the MBMv3 template space.
  5. rsfMRI-regrBMWC0 (Surface-CIFTI): The full processed data mapped to the MBMv3 surface in CIFTI format. Note the All runs in one session were concatenated into one file.
  6. rsfMRI-regrBMWCG0 (Volume-NIFTI) and rsfMRI-regrBMWCG0 (Surface-CIFTI) : As above but includes the global signal regression.
  7. rsfMRI: Regressors: Motion and noises regressor used in preprocessing
  8. dMRI-raw (Volume-NIFTI): the raw diffusion MRI data in NIFTI format
  9. Tracing data only: the neuronal tracing data mapped onto the MBMv3 template space
  10. Tracing data with v3 templates: as above but also include the MBMv3 templates
  11. Codes only: Codes only for building functional parcellation and the deep-learning parcellation generators.
  12. Codes with examples: As above but including the example data for testing the codes
  13. Atlas V4 only: the MBMv4 atlas includes the functional network parcellation (ICA results) and the functional cortical parcellations. All parcellations are in MBMv3 template space.
  14. Atlas V4 with V3 templates: As above but including the MBMv3 templates.

Resting-state fMRI Preprocessing Pipeline

  1. noregr represents minimal preprcessing pipeline without temporal denoising.
  2. regrBMWC0 and regrBMWCG0 represent the full preprocessing pipeline with temporal denoising: a) The B represents band-passing (0.01Hz-0.1Hz); b) The M represents demeaned motion parameters and their derivatives; c) The W represents the demeaned white matter signal; d) The C represents the demeaned csf signal; e) The G represents the global signal regression (demeaned whole brain signal); f) The 0 represents the motion-censor: any TR and the previous TR were censored (set to zeros) if the motion was >0.2 mm.

Minimal preprocessed (noregr)

Minimal preprcessing pipepline involves 1) despike (3dDespike), 2) slice-timing correction (3dTshift), 3) motion correction (3dvolreg), 4) EPI distortion correction (topup). No temporal filtering or regression was performed:

#!/bin/bash ## required AFNI and FSL to be installed, and the foreach function (included in the resource pipeline). sub=IONm01 # define the animal ses=ses-01 # define the sessions inputdir=${BIDS-sourcedata-fMRI}/${sub}/${ses} outputdir=${BIDS-preprocessed-fMRI}/noregr/${sub}/${ses}; mkdir -p ${outputdir} tmpdir=/tmp/${sub}/${ses}; mkdir -p ${tmpdir} topup-datain-RL=${pipeline-dir}/connectome-acq-param-blip-per2tp-RL.txt topup-datain-LR=${pipeline-dir}/connectome-acq-param-blip-per2tp-LR.txt topup-config=${pipeline-dir}/b02b0-marmoset.cnf tpattern=altplus TR=2 numCPU=8 # define the how many CPU for parallel computing ## ======== despike ======== foreach -${numCPU} ${inputdir}/func/*task-rest_run*.nii.gz : 3dDespike -NEW -nomask -prefix ${tmpdir}/PRE-despike.nii IN -overwrite ## ======== tshift ======== foreach -${numCPU} ${tmpdir}/tmp-*-despike.nii : 3dTshift -TR ${TR}s -Fourier -tpattern ${tpattern} -prefix ${tmpdir}/PRE-tshift.nii IN -overwrite ## ======== volreg (two round) ======== 3dbucket -prefix ${tmpdir}/tmp-RL-vr-base.nii ${tmpdir}/tmp-${sub}_${ses}_task-rest_run-RL-1-despike-tshift.nii"[0]" -overwrite 3dbucket -prefix ${tmpdir}/tmp-LR-vr-base.nii ${tmpdir}/tmp-${sub}_${ses}_task-rest_run-LR-1-despike-tshift.nii"[0]" -overwrite foreach -${numCPU} ${tmpdir}/tmp-*-RL-*-tshift.nii : 3dvolreg -zpad 5 -base ${tmpdir}/tmp-RL-vr-base.nii -prefix ${tmpdir}/PRE-volreg.nii -1Dfile ${tmpdir}/PRE-volreg.1D -Fourier -overwrite IN foreach -${numCPU} ${tmpdir}/tmp-*-LR-*-tshift.nii : 3dvolreg -zpad 5 -base ${tmpdir}/tmp-LR-vr-base.nii -prefix ${tmpdir}/PRE-volreg.nii -1Dfile ${tmpdir}/PRE-volreg.1D -Fourier -overwrite IN fslmerge -t ${tmpdir}/tmp-RL-vr-base.nii ${tmpdir}/tmp-*-RL-*-tshift-volreg.nii fslmerge -t ${tmpdir}/tmp-LR-vr-base.nii ${tmpdir}/tmp-*-LR-*-tshift-volreg.nii fslmaths ${tmpdir}/tmp-RL-vr-base.nii -Tmean ${tmpdir}/tmp-RL-vr-base.nii fslmaths ${tmpdir}/tmp-LR-vr-base.nii -Tmean ${tmpdir}/tmp-LR-vr-base.nii foreach -${numCPU} ${tmpdir}/tmp-*-RL-*-tshift.nii : 3dvolreg -zpad 5 -base ${tmpdir}/tmp-RL-vr-base.nii -prefix ${tmpdir}/PRE-volreg.nii -1Dfile ${tmpdir}/PRE-volreg.1D -Fourier -overwrite IN foreach -${numCPU} ${tmpdir}/tmp-*-LR-*-tshift.nii : 3dvolreg -zpad 5 -base ${tmpdir}/tmp-LR-vr-base.nii -prefix ${tmpdir}/PRE-volreg.nii -1Dfile ${tmpdir}/PRE-volreg.1D -Fourier -overwrite IN ## ======== topup ======== cp ${inputdir}/func/${sub}_${ses}_task-rest_SEEPI-RL.nii.gz ${tmpdir}/tmp-SEEPI-RL.nii.gz cp ${inputdir}/func/${sub}_${ses}_task-rest_SEEPI-LR.nii.gz ${tmpdir}/tmp-SEEPI-LR.nii.gz # preprocess SEEPI for RL 3dvolreg -zpad 5 -base ${tmpdir}/tmp-RL-vr-base.nii -prefix ${tmpdir}/tmp-SEEPI-RLLR-RL.nii -Fourier -overwrite ${tmpdir}/tmp-SEEPI-RL.nii.gz fslmaths ${tmpdir}/tmp-SEEPI-RLLR-RL.nii -Tmean ${tmpdir}/tmp-SEEPI-RLLR-RL.nii 3dvolreg -zpad 5 -base ${tmpdir}/tmp-SEEPI-RLLR-RL.nii -prefix ${tmpdir}/tmp-SEEPI-RLLR-LR.nii -Fourier -overwrite ${tmpdir}/tmp-SEEPI-LR.nii.gz fslmaths ${tmpdir}/tmp-SEEPI-RLLR-LR.nii -Tmean ${tmpdir}/tmp-SEEPI-RLLR-LR.nii fslmerge -t ${tmpdir}/tmp-SEEPI-RLLR.nii ${tmpdir}/tmp-SEEPI-RLLR-RL.nii ${tmpdir}/tmp-SEEPI-RLLR-LR.nii # preprocess SEEPI for LR 3dvolreg -zpad 5 -base ${tmpdir}/tmp-LR-vr-base.nii -prefix ${tmpdir}/tmp-SEEPI-LRRL-LR.nii -Fourier -overwrite ${tmpdir}/tmp-SEEPI-LR.nii.gz fslmaths ${tmpdir}/tmp-SEEPI-LRRL-LR.nii -Tmean ${tmpdir}/tmp-SEEPI-LRRL-LR.nii 3dvolreg -zpad 5 -base ${tmpdir}/tmp-SEEPI-LRRL-LR.nii -prefix ${tmpdir}/tmp-SEEPI-LRRL-RL.nii -Fourier -overwrite ${tmpdir}/tmp-SEEPI-RL.nii.gz fslmaths ${tmpdir}/tmp-SEEPI-LRRL-RL.nii -Tmean ${tmpdir}/tmp-SEEPI-LRRL-RL.nii fslmerge -t ${tmpdir}/tmp-SEEPI-LRRL.nii ${tmpdir}/tmp-SEEPI-LRRL-LR.nii ${tmpdir}/tmp-SEEPI-LRRL-RL.nii # topup for LR topup --imain=${tmpdir}/tmp-SEEPI-LRRL.nii --datain=${topup-datain-LR} --config=${topup-config} --out=${tmpdir}/tmp-SEEPI-LRRL-topup-results --fout=${tmpdir}/tmp-SEEPI-LRRL-fout --iout=${tmpdir}/tmp-SEEPI-LRRL-iout # topup for RL topup --imain=${tmpdir}/tmp-SEEPI-RLLR.nii --datain=${topup-datain-RL} --config=${topup-config} --out=${tmpdir}/tmp-SEEPI-RLLR-topup-results --fout=${tmpdir}/tmp-SEEPI-RLLR-fout --iout=${tmpdir}/tmp-SEEPI-RLLR-iout # applytopup for LR foreach -${numCPU} ${tmpdir}/tmp-*-RL-*-volreg.nii : applytopup --imain=IN --datain=${topup-datain-RL} --method=jac --inindex=1 --topup=${tmpdir}/tmp-SEEPI-RLLR-topup-results --out=${outputdir}/PRE-topup.nii.gz # applytopup for RL foreach -${numCPU} ${tmpdir}/tmp-*-LR-*-volreg.nii : applytopup --imain=IN --datain=${topup-datain-LR} --method=jac --inindex=1 --topup=${tmpdir}/tmp-SEEPI-LRRL-topup-results --out=${outputdir}/PRE-topup.nii.gz rename -f 's/tmp-//' ${outputdir}/* rename -f 's/-despike-tshift-volreg-topup//' ${outputdir}/*

Fully preprocessed (regrBMWC0; regrBMWCG0)

Fllowing the Minimal preprcessing pipepline, the data were de-noised by regressing out different noise components by 3dDeconvolve and 3dTproject. The B represents band-passing (0.01Hz-0.1Hz);

The M represents demeaned motion parameters and their derivatives;

The W represents the demeaned white matter signal;

The C represents the demeaned csf signal;

The G represents the global signal regression (demeaned whole brain signal);

The 0 represents the motion-censor: any TR and the previous TR were censored (set to zeros) if the motion was >0.2 mm.

sub=IONm01 # define the animal ses=ses-01 # define the sessions run=run-RL-1 #define the runs inputdir=${BIDS-preprocessed-fMRI}/noregr/${sub}/${ses} numCPU=8 # take regrBMWCG0 as the example; regrBMWC0 should be similar (removing the *_brain_demean.1D regressor) regr_type=regrBMWCG0 outputdir=${BIDS-preprocessed-fMRI}/regrBMWCG0/${sub}/${ses}; mkdir -p ${outputdir} 3dDeconvolve -input ${inputdir}/${sub}_${ses}_task-rest_${run}.nii.gz \ -censor ${regressordir}/${sub}_${ses}_task-rest_${run}_combined_censor.1D \ -ortvec ${regressordir}/${sub}_${ses}_task-rest_${run}_bpass.1D bandpass \ -ortvec ${regressordir}/${sub}_${ses}_task-rest_${run}_wm_demean.1D wm \ -ortvec ${regressordir}/${sub}_${ses}_task-rest_${run}_csf_demean.1D csf \ -ortvec ${regressordir}/${sub}_${ses}_task-rest_${run}_brain_demean.1D brain \ -polort $polort -float \ -num_stimts 12 \ -stim_file 1 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_demean.1D'[0]' -stim_base 1 -stim_label 1 roll_01 \ -stim_file 2 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_demean.1D'[1]' -stim_base 2 -stim_label 2 pitch_01 \ -stim_file 3 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_demean.1D'[2]' -stim_base 3 -stim_label 3 yaw_01 \ -stim_file 4 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_demean.1D'[3]' -stim_base 4 -stim_label 4 dS_01 \ -stim_file 5 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_demean.1D'[4]' -stim_base 5 -stim_label 5 dL_01 \ -stim_file 6 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_demean.1D'[5]' -stim_base 6 -stim_label 6 dP_01 \ -stim_file 7 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_deriv.1D'[0]' -stim_base 7 -stim_label 7 roll_02 \ -stim_file 8 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_deriv.1D'[1]' -stim_base 8 -stim_label 8 pitch_02 \ -stim_file 9 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_deriv.1D'[2]' -stim_base 9 -stim_label 9 yaw_02 \ -stim_file 10 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_deriv.1D'[3]' -stim_base 10 -stim_label 10 dS_02 \ -stim_file 11 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_deriv.1D'[4]' -stim_base 11 -stim_label 11 dL_02 \ -stim_file 12 ${regressordir}/${sub}_${ses}_task-rest_${run}_motion_deriv.1D'[5]' -stim_base 12 -stim_label 12 dP_02 \ -fout -tout -x1D ${outputdir}/${sub}_${ses}_task-rest_${run}_${regr_type}.xmat.1D \ -xjpeg ${outputdir}/tmp_${sub}_${ses}_task-rest_${run}_${regr_type}.jpg \ -x1D_uncensored ${outputdir}/${sub}_${ses}_task-rest_${run}_${regr_type}.nocensor.xmat.1D \ -fitts ${outputdir}/tmp_${sub}_${ses}_task-rest_${run}_${regr_type}_fitts \ -errts ${outputdir}/tmp_${sub}_${ses}_task-rest_${run}_${regr_type}_errts \ -bucket ${outputdir}/tmp_${sub}_${ses}_task-rest_${run}_${regr_type}_stats \ -jobs ${numCPU} -overwrite -x1D_stop cenmode=ZERO ## Kill for ICA; Zero for correlation 3dTproject -polort 0 -input ${inputdir}/${sub}_${ses}_task-rest_${run}.nii.gz -censor ${regressordir}/${sub}_${ses}_task-rest_${run}_combined_censor.1D -cenmode ${cenmode} -ort ${outputdir}/${sub}_${ses}_task-rest_${run}_${regr_type}.nocensor.xmat.1D -prefix ${outputdir}/${sub}_${ses}_task-rest_${run}.nii.gz -overwrite

rsfMRI-regr (BIDS-fMRI-regressors): regression parameters explaination

  1. bpass.1D: band-pass filtering regressors (0.01Hz - 0.1 Hz)
  2. brain-demean.1D: the demeaned whole brain signals
  3. combined-censor.1D: the timepoint censors of the outlier time points and the motion censors
  4. csf-demean.1D: the demeaned csf signals
  5. despike-tshift_volreg.1D: the raw motion parameters
  6. hpass.1D: the high-pass filtering regressors (0.1 Hz -)
  7. motion-censor.1D: the motion-censor: any TR and the previous TR were censored (set to zeros) if the motion was >0.2 mm.
  8. motion-CENSORTR.txt: the censored time points
  9. motion-demean.1D: the demeaned motion parameters
  10. motion-deriv.1D: the derivatives of motion parameters
  11. outcount.1D: the outlier time points (estimated by afni)
  12. outcount-censor.1D: the censor regressor of the outlier time points
  13. wm-demean.1D: the demeaned white matter signals