Hello Neurostars and experts,
I am new to fMRI and fMRIprep and I wanted to get some opinions regarding the fMRIprep set-up I am using, as well as the selections in CONN toolbox to make sure I am doing it correctly.
The data – resting-state scans are from different sites with different voxel sizes and TR. In some cases, the scans come fro a single session, in others, resting scans were collected in 2 separate sessions but regardless of the number of sessions, the total duration of the resting scans is similar.
Because of the different resolution of the native functional spaces, and the character of the task (non, aka resting state), I used the following options (not all subjects have necessary data to use fieldmaps option):
–output-spaces MNI152NLin6Asym:res-2 MNI1522NLin2009cAsym --use-syn-sdc --fs-no-reconall --ignore fieldmaps slicetiming
I was told that slice timing correction is not needed with resting-state scans and because I want to run ROI-to-ROI comparisons defined on a 2mm template consistent with MNI152NLin6Asym, I should resample the functional scans to the same space rather than use resampled to native space that varies in resolution across subjects.
Once preprocessed in fMRIprep, the following steps and confounds from fMRIprep for denoising are to follow, before ROI-to-ROI analysis and between-groups comparisons:
* Smoothing: with a 5mm Gaussian kernel * Realignment * Scrubbing
fMRIprep Confounds in denoising:
* aCompCor, tCompCor & their corresponding cosine_XX regressors
* Motion correction: DVARS, framewise displacement, motion outlier
* Rotational parameters: rot, rot_x_derivative, rot_y_derivative, rot_z_derivative
* Translational parameters: trans, trans_x_derivative, trans_y_derivative, trans_z_derivative
* Default band-pass filter: 0.008-0.09 Hz
* Detrending: linear
So far I was able to troubleshoot any problems related to preprocessing data, but it would be great if I could get some input an opinions:
Are any of the steps redundant?
Is resampling the functional data to a standard space helpful?
Is the band-pass filter of an appropriate range given the type of the data?
Can you see any red flags that could potentially distort the results?