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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021_task-rest_dir-RL_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-021/func/sub-001_ses-021_task-rest_dir-RL_echo-1_part-mag_bold.nii.gz
VersionsMRIQC24.1.0.dev0+g3fe90466.d20240417
VersionsNiPype1.8.6
VersionsTemplateFlow24.2.0
md5sumbc6c4f6b43fc32624a3b655c394d8118

AcquisitionDuration1230.47
AcquisitionMatrixPE76
AcquisitionTime20:04:16.197500
B0FieldSource['021pepolarbold', '021phasediff']
BandwidthPerPixelPhaseEncode40.486
BaseResolution102
DerivedVendorReportedEchoSpacing0.000649997
DwellTime2.5e-06
EchoNumber1
EchoTime0.0126
EchoTrainLength28
EffectiveEchoSpacing0.000324999
FlipAngle64
ImageOrientationPatientDICOM[0.999833, 0.00493759, 0.017604, 0.000698016, 0.951833, -0.306616]
ImageType['ORIGINAL', 'PRIMARY', 'FMRI', 'NONE']
ImageTypeText['ORIGINAL', 'PRIMARY', 'M', 'MB', 'TE1', 'DIS2D']
InPlanePhaseEncodingDirectionDICOMROW
PercentPhaseFOV74.5098
PhaseEchoTime10.0126
PhaseEchoTime20.0322
PhaseEncodingDirectioni
PixelBandwidth1961
ProtocolNamefunc-bold_task-rest_dir-RL__cmrr_me4_sms4
PulseSequenceNameepfid2d4_76
ReceiveCoilActiveElementsHC1-7
ReconMatrixPE76
RefLinesPE18
RepetitionTime1.6
SequenceVariantSK\SS\OSP
SeriesDescriptionfunc-bold_task-rest_dir-RL__cmrr_me4_sms4
SeriesNumber23
ShimSetting[8214, -5616, -6005, 711, -78, -163, 46, 77]
SliceThickness2.2
SliceTiming[0, 0.8425, 0.105, 0.9475, 0.21, 1.0525, 0.315, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.8425, 0.105, 0.9475, 0.21, 1.0525, 0.315, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.8425, 0.105, 0.9475, 0.21, 1.0525, 0.315, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.8425, 0.105, 0.9475, 0.21, 1.0525, 0.315, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375]
SpacingBetweenSlices2.2
TotalReadoutTime0.0243749
TxRefAmp248.137
WipMemBlock4ae80283-be0a-476c-9a13-f55b8488ce44||Sequence: R017 nxva30a/master r/d34922cc2+; Jun 27 2022 13:20:16 by eja

aor[0.00028818424566088115, 0.0004556208277703605, 0.0005424966622162884, 0.0006059546061415221]
aqi[0.005473063698264352, 0.0073655549399198935, 0.012471447316421894, 0.02096430173564753]
dummy_trs1
dvarsnstd35.981454089478646
dvarsstd1.0293572092513388
dvarsvstd1.0017140326737972
efc0.655
fber133.6233
fdmean0.12018593205202292
fdnum41
fdperc5.473965287049399
fwhmavg3.705196940275954
fwhmx3.515896555482901
fwhmy3.734044349130667
fwhmz3.865649916214294
gcor[0.0213597, 0.0174096, 0.0140606, 0.0109979]
gsrx0.14163799583911896
gsry0.019400374963879585
sizet749
sizex76
sizey102
sizez60
snr2.626835106063181
spacingtr1.600000023841858
spacingx2.196079969406128
spacingy2.196079969406128
spacingz2.200000047683716
summarybgk5.31
summarybgmad65.7373
summarybgmean174.337
summarybgmedian70.0
summarybgn331253.0
summarybgp050.0
summarybgp95740.0
summarybgstdv245.3072
summaryfgk0.8371
summaryfgmad296.6531
summaryfgmean831.0734
summaryfgmedian804.0
summaryfgn133867.0
summaryfgp05396.0
summaryfgp951358.0
summaryfgstdv306.0706
tsnr37.436864741146564
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