Anatomical image input to CONN toolbox

Summary of what happened:

I am using CONN toolbox and its default mni preprocessing pipeline.
Do I need to feed the skull stripped anatomical data or just the original anatomical image is fine?

I have to say that I ran the pipeline when the original structural (without skull stripping) data was but there was some mis match in the registration of functional image to the structural images. Could it be because the structural images were not skull stripped?

Hi @maryam.riazi,

It depends on what you do for preprocessing. Skull stripping is part of the default preprocessing pipeline, so it expects a raw t1 with no processing, but you can input a skull stripped image too and skip that step.

If CONN registration is going poorly, it could be due to some issue with the image quality, but you can also run preprocessing via fMRIPrep and import it into CONN. CONN has a dedicated fmriprep import module.


Thanks Steven.

Due to the link below:

Steps of default preprocessing pipeline are: Functional realignment and unwarp, Slice-Timing Correction, Outlier identification, Direct segmentation and normalization, and Functional smoothing.

Due to what was described in the link, non of these steps are doing skull stripping. Can you tell me which step to remove in case of inputing a skull stripped image?

Direct segmentation and normalization

Functional and anatomical data are normalized into standard MNI space and segmented into grey matter, white matter, and CSF tissue classes using SPM12 unified segmentation and normalization procedure (Ashburner and Friston, 2005). This procedure iteratively performs tissue classification, estimating the posterior tissue probability maps (TPMs) from the intensity values of the reference functional/anatomical image, and registration, estimating the non-linear spatial transformation best approximating the posterior and prior TPMs, until convergence. Direct normalization applies this unified segmentation and normalization procedure separately to the functional data, using the mean BOLD signal as reference image, and to the structural data, using the raw T1-weighted volume as reference image. Both functional and anatomical data are resampled to a default 180x216x180mm bounding box, with 2mm isotropic voxels for functional data and 1mm for anatomical data, using 4th order spline interpolation

  • Preprocessing step name: functional_segment&normalize_direct, structural_segment&normalize
  • Inputs: functional data (in primary dataset), structural data
  • Outputs: MNI-space functional and structural data. grey matter, white matter, and CSF masks. Skull-stripped structural volume
  • See also: #affreg, #boundingbox, #coregtomean, #interp, #tpm_template, #tpm_ngaus, #voxelsize_anat, #voxelsize_func fields for advanced options

I guess an issue is I do not know if it is possible to disable just the skull stripping. But you can see that it is performed here, and the skull-stripped volume is used to register the functional and anatomical data in later steps.