IQMs for functional images

Measures for the spatial information

Definitions are given in the summary of structural IQMs.

  • Entropy-focus criterion (efc()).

  • Signal-to-noise ratio (snr()).

Measures for the temporal information


D referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels ([Power2012] dvars_nstd) indexes the rate of change of BOLD signal across the entire brain at each frame of data. DVARS is calculated with nipype after motion correction:

\[\text{DVARS}_t = \sqrt{\frac{1}{N}\sum_i \left[x_{i,t} - x_{i,t-1}\right]^2}\]


Intensities are scaled to 1000 leading to the units being expressed in x10 \(\%\Delta\text{BOLD}\) change.


MRIQC calculates two additional standardized values of the DVARS. The dvars_std metric is normalized with the standard deviation of the temporal difference time series. The dvars_vstd is a voxel-wise standardization of DVARS, where the temporal difference time series is normalized across time by that voxel standard deviation across time, before computing the RMS of the temporal difference [Nichols2013].

Global Correlation (gcor)

calculates an optimized summary of time-series correlation as in [Saad2013] using AFNI’s @compute_gcor:

\[\text{GCOR} = \frac{1}{N}\mathbf{g}_u^T\mathbf{g}_u\]

where \(\mathbf{g}_u\) is the average of all unit-variance time series in a \(T\) (# timepoints) \(\times\) \(N\) (# voxels) matrix.

Temporal SNR (tSNR, tsnr)

is a simplified interpretation of the tSNR definition [Kruger2001]. We report the median value of the tSNR map calculated like:

\[\text{tSNR} = \frac{\langle S \rangle_t}{\sigma_t},\]

where \(\langle S \rangle_t\) is the average BOLD signal (across time), and \(\sigma_t\) is the corresponding temporal standard-deviation map. Higher values are better.

Measures for artifacts and other

Framewise Displacement

expresses instantaneous head-motion [Jenkinson2002]. MRIQC reports the average FD, labeled as fd_mean. Rotational displacements are calculated as the displacement on the surface of a sphere of radius 50 mm [Power2012]:

\[\text{FD}_t = |\Delta d_{x,t}| + |\Delta d_{y,t}| + |\Delta d_{z,t}| + |\Delta \alpha_t| + |\Delta \beta_t| + |\Delta \gamma_t|\]

Along with the base framewise displacement, MRIQC reports the number of timepoints above FD threshold (fd_num), and the percent of FDs above the FD threshold w.r.t. the full timeseries (fd_perc). In both cases, the threshold is set at 0.20mm.

Ghost to Signal Ratio (gsr())

labeled in the reports as gsr_x and gsr_y (calculated along the two possible phase-encoding axes x, y):

\[\text{GSR} = \frac{\mu_G - \mu_{NG}}{\mu_S}\]
AFNI’s outlier ratio (aor)

Mean fraction of outliers per fMRI volume as given by AFNI’s 3dToutcount.

AFNI’s quality index (aqi)

Mean quality index as computed by AFNI’s 3dTqual; for each volume, it is one minus the Spearman’s (rank) correlation of that volume with the median volume. Lower values are better.

Number of dummy scans** (dummy)

A number of volumes in the beginning of the fMRI timeseries identified as non-steady state.

mriqc.qc.functional.gsr(epi_data, mask, direction='y', ref_file=None, out_file=None)[source]

Compute the GSR [Giannelli2010].

The procedure is as follows:

  1. Create a Nyquist ghost mask by circle-shifting the original mask by \(N/2\).

  2. Rotate by \(N/2\)

  3. Remove the intersection with the original mask

  4. Generate a non-ghost background

  5. Calculate the GSR


This should be used with EPI images for which the phase encoding direction is known.

  • epi_file (str) – path to epi file

  • mask_file (str) – path to brain mask

  • direction (str) – the direction of phase encoding (x, y, all)


the computed gsr