Basic echo-wise reports

Reports for: echo 1.

Standard deviation of signal through time

The voxel-wise standard deviation of the signal (variability along time).

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-1_desc-stdev_bold.svg

View of the background of the voxel-wise average of the BOLD timeseries

This panel shows a mosaic enhancing the background around the head. Artifacts usually unveil themselves in the air surrounding the head, where no signal sources are present.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-1_desc-background_bold.svg

Voxel-wise average of BOLD time-series, zoomed-in covering just the brain

This panel shows a mosaic of the brain. This mosaic is the most suitable to screen head-motion intensity inhomogeneities, global/local noise, signal leakage (for example, from the eyeballs and across the phase-encoding axis), etc.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-1_desc-zoomed_bold.svg

Carpetplot and nuisance signals

The so-called «carpetplot» may assist in assessing head-motion derived artifacts and respiation effects.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-1_desc-carpet_bold.svg

Reports for: echo 2.

Standard deviation of signal through time

The voxel-wise standard deviation of the signal (variability along time).

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-2_desc-stdev_bold.svg

View of the background of the voxel-wise average of the BOLD timeseries

This panel shows a mosaic enhancing the background around the head. Artifacts usually unveil themselves in the air surrounding the head, where no signal sources are present.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-2_desc-background_bold.svg

Voxel-wise average of BOLD time-series, zoomed-in covering just the brain

This panel shows a mosaic of the brain. This mosaic is the most suitable to screen head-motion intensity inhomogeneities, global/local noise, signal leakage (for example, from the eyeballs and across the phase-encoding axis), etc.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-2_desc-zoomed_bold.svg

Carpetplot and nuisance signals

The so-called «carpetplot» may assist in assessing head-motion derived artifacts and respiation effects.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-2_desc-carpet_bold.svg

Reports for: echo 3.

Standard deviation of signal through time

The voxel-wise standard deviation of the signal (variability along time).

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-3_desc-stdev_bold.svg

View of the background of the voxel-wise average of the BOLD timeseries

This panel shows a mosaic enhancing the background around the head. Artifacts usually unveil themselves in the air surrounding the head, where no signal sources are present.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-3_desc-background_bold.svg

Voxel-wise average of BOLD time-series, zoomed-in covering just the brain

This panel shows a mosaic of the brain. This mosaic is the most suitable to screen head-motion intensity inhomogeneities, global/local noise, signal leakage (for example, from the eyeballs and across the phase-encoding axis), etc.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-3_desc-zoomed_bold.svg

Carpetplot and nuisance signals

The so-called «carpetplot» may assist in assessing head-motion derived artifacts and respiation effects.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-3_desc-carpet_bold.svg

Reports for: echo 4.

Standard deviation of signal through time

The voxel-wise standard deviation of the signal (variability along time).

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-4_desc-stdev_bold.svg

View of the background of the voxel-wise average of the BOLD timeseries

This panel shows a mosaic enhancing the background around the head. Artifacts usually unveil themselves in the air surrounding the head, where no signal sources are present.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-4_desc-background_bold.svg

Voxel-wise average of BOLD time-series, zoomed-in covering just the brain

This panel shows a mosaic of the brain. This mosaic is the most suitable to screen head-motion intensity inhomogeneities, global/local noise, signal leakage (for example, from the eyeballs and across the phase-encoding axis), etc.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-4_desc-zoomed_bold.svg

Carpetplot and nuisance signals

The so-called «carpetplot» may assist in assessing head-motion derived artifacts and respiation effects.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-4_desc-carpet_bold.svg

Extended echo-wise reports

Reports for: echo 1.

Voxel-wise average of BOLD time-series

The average signal calculated across the last axis (time).

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-1_desc-mean_bold.svg

Reports for: echo 2.

Voxel-wise average of BOLD time-series

The average signal calculated across the last axis (time).

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-2_desc-mean_bold.svg

Reports for: echo 3.

Voxel-wise average of BOLD time-series

The average signal calculated across the last axis (time).

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-3_desc-mean_bold.svg

Reports for: echo 4.

Voxel-wise average of BOLD time-series

The average signal calculated across the last axis (time).

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_echo-4_desc-mean_bold.svg

Extended reports shared across echos

Brain extraction performance

Brain mask as internally extracted by MRIQC. Defects on the brainmask could indicate problematic aspects of the image quality-wise.

Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_desc-brainmask_bold.svg

Spatial normalization of the anatomical image

This panel shows a quick-and-dirty nonlinear registration into the MNI152NLin2009cAsym template accessed with TemplateFlow.

Problem loading figure sub-001/figures/sub-001_ses-022_task-rest_dir-AP_desc-norm_bold.svg. If the link below works, please try reloading the report in your browser.
Get figure file: sub-001/figures/sub-001_ses-022_task-rest_dir-AP_desc-norm_bold.svg

About

Errors

Reproducibility and provenance information

Thanks for using MRIQC. The following information may assist in reconstructing the provenance of the corresponding derivatives.

Execution environmentsingularity
Input filename/sub-001/ses-022/func/sub-001_ses-022_task-rest_dir-AP_echo-1_part-mag_bold.nii.gz
VersionsMRIQC24.1.0.dev0+g3fe90466.d20240417
VersionsNiPype1.8.6
VersionsTemplateFlow24.2.0
md5sume20ba7cdd0fc56c6664a5c76df45dae2

AcquisitionDuration1230.47
AcquisitionMatrixPE96
AcquisitionTime21:52:18.290000
B0FieldSource['022pepolarbold', '022phasediff']
BandwidthPerPixelPhaseEncode37.879
BaseResolution96
DerivedVendorReportedEchoSpacing0.000549997
DwellTime2.3e-06
EchoNumber1
EchoTime0.0126
EchoTrainLength35
EffectiveEchoSpacing0.000274998
FlipAngle64
ImageOrientationPatientDICOM[0.999002, 0.0331436, 0.0299534, -0.0216239, 0.945475, -0.324976]
ImageType['ORIGINAL', 'PRIMARY', 'FMRI', 'NONE']
ImageTypeText['ORIGINAL', 'PRIMARY', 'M', 'MB', 'TE1', 'DIS2D']
InPlanePhaseEncodingDirectionDICOMCOL
PercentPhaseFOV100
PhaseEchoTime10.0126
PhaseEchoTime20.03304
PhaseEncodingDirectionj-
PixelBandwidth2264
ProtocolNamefunc-bold_task-rest_dir-AP__cmrr_me4_sms4
PulseSequenceNameepfid2d4_96
ReceiveCoilActiveElementsHC1-7
ReconMatrixPE96
RefLinesPE22
RepetitionTime1.6
SequenceVariantSK\SS
SeriesDescriptionfunc-bold_task-rest_dir-AP__cmrr_me4_sms4
SeriesNumber23
ShimSetting[8241, -5625, -6004, 658, -40, -213, 43, 32]
SliceThickness2.2
SliceTiming[0, 0.8425, 0.105, 0.9475, 0.2125, 1.055, 0.3175, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.8425, 0.105, 0.9475, 0.2125, 1.055, 0.3175, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.8425, 0.105, 0.9475, 0.2125, 1.055, 0.3175, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.8425, 0.105, 0.9475, 0.2125, 1.055, 0.3175, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375]
SpacingBetweenSlices2.2
TotalReadoutTime0.0261249
TxRefAmp247.75
WipMemBlockd19c44ab-b621-459f-b8c9-f84f2af3006a||Sequence: R017 nxva30a/master r/d34922cc2+; Jun 27 2022 13:20:16 by eja

aor[0.0004184913217623498, 0.0004700400534045394, 0.0005095460614152203, 0.0005885714285714285]
aqi[0.004555969012016022, 0.0050541059279038715, 0.008750902363150868, 0.015602297289719623]
dummy_trs1
dvarsnstd25.118846146149657
dvarsstd1.0395690004278073
dvarsvstd0.9970642428074884
efc0.5793
fber644.2988
fdmean0.09982703550078784
fdnum10
fdperc1.335113484646195
fwhmavg3.7817635797333278
fwhmx3.464762006333834
fwhmy3.7614060948730006
fwhmz4.119122637993149
gcor[0.00980424, 0.0112715, 0.00995686, 0.00763368]
gsrx0.004764981102198362
gsry0.12016334384679794
sizet749
sizex96
sizey96
sizez60
snr2.4205110077598766
spacingtr1.600000023841858
spacingx2.197920083999634
spacingy2.197920083999634
spacingz2.200000047683716
summarybgk9.9279
summarybgmad22.2212
summarybgmean133.5503
summarybgmedian32.0
summarybgn418756.0
summarybgp050.0
summarybgp95667.0
summarybgstdv237.8416
summaryfgk1.7089
summaryfgmad300.0498
summaryfgmean852.3904
summaryfgmedian809.0
summaryfgn134204.0
summaryfgp05385.0
summaryfgp951436.0
summaryfgstdv334.2257
tsnr52.921937578357756
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