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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045_task-bht_dir-LR_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-045/func/sub-001_ses-045_task-bht_dir-LR_echo-1_part-mag_bold.nii.gz
VersionsMRIQC24.1.0.dev0+g3fe90466.d20240417
VersionsNiPype1.8.6
VersionsTemplateFlow24.2.0
md5sume10a0bb1102c8ef45d9be37f1d438de2

AcquisitionDuration361.475
AcquisitionMatrixPE76
AcquisitionTime19:49:39.445000
B0FieldSource['045pepolarbold', '045phasediff']
BandwidthPerPixelPhaseEncode40.486
BaseResolution102
DerivedVendorReportedEchoSpacing0.000649997
DwellTime2.5e-06
EchoNumber1
EchoTime0.0126
EchoTrainLength28
EffectiveEchoSpacing0.000324999
ImageOrientationPatientDICOM[0.999432, -0.00158001, 0.0336498, 0.00994306, 0.968232, -0.249856]
ImageType['ORIGINAL', 'PRIMARY', 'FMRI', 'NONE']
ImageTypeText['ORIGINAL', 'PRIMARY', 'M', 'MB', 'TE1', 'DIS2D']
InPlanePhaseEncodingDirectionDICOMROW
PercentPhaseFOV74.5098
PercentSampling100
PhaseEchoTime10.0126
PhaseEchoTime20.0322
PhaseEncodingDirectioni-
PhaseResolution1
PixelBandwidth1961
ProtocolNamefunc-bold_task-bht_dir-LR__cmrr_me4_sms4
PulseSequenceNameepfid2d4_76
ReceiveCoilActiveElementsHC1-7
ReconMatrixPE76
RefLinesPE18
RepetitionTime1.6
SequenceVariantSK\SS\OSP
SeriesDescriptionfunc-bold_task-bht_dir-LR__cmrr_me4_sms4
SeriesNumber25
ShimSetting[8235, -5631, -6032, 694, 16, -188, 74, 42]
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
TotalReadoutTime0.0243749
TxRefAmp243.334
WipMemBlock206ecd97-aae1-4f74-b202-252923e8abe7||Sequence: R017 nxva30a/master r/d34922cc2+; Jun 27 2022 13:20:16 by eja

aor[0.0004006372549019608, 0.0004188235294117648, 0.0004910294117647058, 0.0006420588235294118]
aqi[0.006425129068627451, 0.008602184607843138, 0.014655826127450982, 0.02472158676470588]
dummy_trs3
dvarsnstd38.23119621108377
dvarsstd1.0206375207881773
dvarsvstd0.9928703879310345
efc0.65
fber159.4285
fdmean0.15314969655878222
fdnum61
fdperc29.901960784313726
fwhmavg3.6549377954950675
fwhmx3.433836702239609
fwhmy3.7241767667557593
fwhmz3.8067999174898337
gcor[0.00834363, 0.0124718, 0.0132432, 0.0112756]
gsrx0.13869401812553406
gsry0.02634432725608349
sizet204
sizex76
sizey102
sizez60
snr2.645023124195984
spacingtr1.600000023841858
spacingx2.196079969406128
spacingy2.196079969406128
spacingz2.200000047683716
summarybgk5.742
summarybgmad58.2575
summarybgmean167.6157
summarybgmedian63.0
summarybgn330417.0
summarybgp050.0
summarybgp95729.0
summarybgstdv242.5723
summaryfgk0.7721
summaryfgmad293.5989
summaryfgmean825.1023
summaryfgmedian800.0
summaryfgn134703.0
summaryfgp05387.0
summaryfgp951344.0
summaryfgstdv302.4537
tsnr36.0326749344822
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