IQMs for functional images¶
MRIQC: image quality metrics for functional MRI.
Measures for the spatial 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 <http://nipype.readthedocs.io/en/latest/interfaces/generated/
nipype.algorithms.confounds.html#computedvars>`_ 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
) calculates an optimized summary of timeseries 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 unitvariance time series in a \(T\) (# timepoints) \(\times\) \(N\) (# voxels) matrix.
 Global Correlation (
Temporal SNR (tSNR,
tsnr
) is a simplified interpretation of the tSNR definition [Kruger2001]. We report the median value of the `tSNR map <http://nipype.readthedocs.io/en/latest/interfaces/generated/
nipype.algorithms.confounds.html#tsnr>`_ 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
.
Number of *dummy* scans (
dummy
)  A number of volumes in the begining of the fMRI timeseries identified as nonsteady state.
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 <http://www2.warwick.ac.uk/fac/sci/statistics/staff/academicresearch
/nichols/scripts/fsl/standardizeddvars.pdf>`_, 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
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.
gsr
(epi_data, mask, direction='y', ref_file=None, out_file=None)[source]¶ Compute 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.