IQMs for functional images¶
Measures for the structural information¶
Definitions are given in the summary of structural IQMs.
 Entropyfocus criterion (
efc()
).  ForegroundBackground energy ratio (
fber()
, [Shehzad2015]).  Fullwidth half maximum smoothness (
fwhm_*
).  Signaltonoise ratio (
snr()
).  Summary statistics (
summary_stats()
).
Measures for the temporal information¶
DVARS  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:\[\text{DVARS}_t = \sqrt{\frac{1}{N}\sum_i \left[x_{i,t}  x_{i,t1}\right]^2}\]Note
Intensities are scaled to 1000 leading to the units being expressed in x10 \(\%\Delta\text{BOLD}\) change.
Note
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. Thedvars_vstd
is a voxelwise 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()
,gcor
) calculates an optimized summary of timeseries correlation as in [Saad2013]:\[\text{GCOR} = \frac{1}{N}\mathbf{g}_u^T\mathbf{g}_u\]where \(\mathbf{g}_u\) is the average of all unitvariance 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 standarddeviation map.
Measures for artifacts and other¶
Framewise Displacement: expresses instantaneous headmotion. 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 asgsr_x
andgsr_y
): along the two possible phaseencoding 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’s3dToutcount
.AFNI’s quality index (
aqi
)  Mean quality index as computed by AFNI’s3dTqual
.
References
[Atkinson1997]  Atkinson et al., Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion, IEEE Trans Med Imag 16(6):903910, 1997. doi:10.1109/42.650886. 
[Friedman2008]  Friedman, L et al., Test–retest and between‐site reliability in a multicenter fMRI study. Hum Brain Mapp, 29(8):958–972, 2008. doi:10.1002/hbm.20440. 
[Giannelli2010]  Giannelli et al., Characterization of Nyquist ghost in EPIfMRI acquisition sequences implemented on two clinical 1.5 T MR scanner systems: effect of readout bandwidth and echo spacing. J App Clin Med Phy, 11(4). 2010. doi:10.1120/jacmp.v11i4.3237. 
[Jenkinson2002]  Jenkinson et al., Improved Optimisation for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825841, 2002. doi:10.1006/nimg.2002.1132. 
[Kruger2001]  Krüger et al., Physiological noise in oxygenationsensitive magnetic resonance imaging, Magn. Reson. Med. 46(4):631637, 2001. doi:10.1002/mrm.1240. 
[Nichols2013]  Nichols, Notes on Creating a Standardized Version of DVARS, 2013. 
[Power2012]  (1, 2) Power et al., Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, NeuroImage 59(3):21422154, 2012, doi:10.1016/j.neuroimage.2011.10.018. 
[Saad2013]  (1, 2) Saad et al. Correcting BrainWide Correlation Differences in RestingState FMRI, Brain Conn 3(4):339352, 2013, doi:10.1089/brain.2013.0156. 
mriqc.qc.functional module¶

mriqc.qc.functional.
gcor
(func, mask=None)[source]¶ Compute the GCOR [Saad2013].
Parameters:  func (numpy.ndarray) – input fMRI dataset, after motion correction
 mask (numpy.ndarray) – 3D brain mask
Returns: the computed GCOR value

mriqc.qc.functional.
gsr
(epi_data, mask, direction='y', ref_file=None, out_file=None)[source]¶ Computes the GSR [Giannelli2010]. The procedure is as follows:
 Create a Nyquist ghost mask by circleshifting the original mask by \(N/2\).
 Rotate by \(N/2\)
 Remove the intersection with the original mask
 Generate a nonghost background
 Calculate the GSR
Warning
This should be used with EPI images for which the phase encoding direction is known.
Parameters: Returns: the computed gsr