BOLD images

One individual report per input functional timeseries will be generated in the path <output_dir>/reports/sub-IDxxx_task-name_bold.html`. An example report is given here.

The individual report for the functional images is structured as follows:

Summary

The first section summarizes some important information:

• subject identifier, date and time of execution of mriqc, software version;

• workflow details and flags raised during execution; and

• the extracted IQMs.

Visual reports

The section with visual reports contains:

1. Mosaic view of the average BOLD signal.

   

2. Mosaic view of the temporal standard deviation.

   

3. Summary plot, showing the slice-wise signal intensity at the extremes for the identification of spikes, the outliers metric, the DVARS and the FD. Finally the so-called carpetplot [Power2016]. The carpet plot rows correspond to voxelwise time series, and are separated into regions: cortical gray matter, deep gray matter, white matter and cerebrospinal fluid, cerebellum and the brain-edge or “crown” [Provins2022]. The crown corresponds to the voxels located on a closed band around the brain [Patriat2015].

   

Verbose reports

If mriqc was run with the --verbose-reports flag, the following plots will be appended:

1. Mosaic view of the average BOLD signal, zoomed-in to the bounding box of brain activation.

   

2. Mosaic view of the average BOLD signal, with background enhancement.

   

3. One rows of axial views at different Z-axis points showing the calculated brain mask.

   

4. Mosaic view with animation for assessment of the co-registration to MNI space (roll over the image to activate the animation).

   

Metadata

If some metadata was found in the BIDS structure, it is reported here.

References

Patriat2015

Patriat, R., EK Molloy, RM Birn, T. Guitchev, and A. Popov. “Using Edge Voxel Information to Improve Motion Regression for Rs-FMRI Connectivity Studies.” Brain Connectivity 5, no. 9 (28 2015): 582–95. doi: 10.1089/brain.2014.0321.

Power2016

Power JD, A simple but useful way to assess fMRI scan qualities. NeuroImage. 2016. doi: 10.1016/j.neuroimage.2016.08.009.

Provins2022

Provins, Céline, Christopher J. Markiewicz, Rastko Ciric, Mathias Goncalves, César Caballero-Gaudes, Russell Poldrack, Patric Hagmann, and Oscar Esteban. “Quality Control and Nuisance Regression of FMRI, Looking out Where Signal Should Not Be Found.” OSF Preprints, January 21, 2022. doi: 10.31219/osf.io/hz52v.