IQMs for functional images
Measures for the spatial information
Definitions are given in the summary of structural IQMs.
Entropy-focus criterion (
efc()
).
Foreground-Background energy ratio (
fber()
, [Shehzad2015]).
Full-width half maximum smoothness (
fwhm_*
, see [Friedman2008]).
Signal-to-noise 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:\[\text{DVARS}_t = \sqrt{\frac{1}{N}\sum_i \left[x_{i,t} - x_{i,t-1}\right]^2}\]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 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
andgsr_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 begining of the fMRI timeseries identified as non-steady 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):903-910, 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 EPI-fMRI 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), 825-841, 2002. doi:10.1006/nimg.2002.1132.
- Kruger2001
Krüger et al., Physiological noise in oxygenation-sensitive magnetic resonance imaging, Magn. Reson. Med. 46(4):631-637, 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):2142-2154, 2012, doi:10.1016/j.neuroimage.2011.10.018.
- Saad2013
Saad et al. Correcting Brain-Wide Correlation Differences in Resting-State FMRI, Brain Conn 3(4):339-352, 2013, doi:10.1089/brain.2013.0156.
- 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 circle-shifting the original mask by \(N/2\).
Rotate by \(N/2\)
Remove the intersection with the original mask
Generate a non-ghost background
Calculate the GSR
Warning
This should be used with EPI images for which the phase encoding direction is known.