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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008_task-bht_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-pilot008/func/sub-001_ses-pilot008_task-bht_dir-AP_echo-1_bold.nii.gz
VersionsMRIQC24.1.0.dev0+g3fe90466.d20240417
VersionsNiPype1.8.6
VersionsTemplateFlow24.2.0
md5sum51ca07792ace90cc88d4f40640422a0b

AcquisitionDuration275.442
AcquisitionMatrixPE108
AcquisitionTime18:17:38.827500
B0FieldSource['pilot008pepolarbold', 'pilot008phasediff']
BandwidthPerPixelPhaseEncode34.294
BaseResolution108
DerivedVendorReportedEchoSpacing0.000539993
DwellTime2e-06
EchoNumber1
EchoTime0.0126
EchoTrainLength40
EffectiveEchoSpacing0.000269996
ImageOrientationPatientDICOM[0.997239, 0.0607052, 0.0427635, -0.0597709, 0.997952, -0.0228]
ImageType['ORIGINAL', 'PRIMARY', 'FMRI', 'NONE']
ImageTypeText['ORIGINAL', 'PRIMARY', 'M', 'MB', 'TE1', 'DIS2D']
InPlanePhaseEncodingDirectionDICOMCOL
PercentPhaseFOV100
PercentSampling100
PhaseEncodingDirectionj-
PhaseResolution1
PixelBandwidth2315
ProtocolNamefunc-bold_task-bht_dir-AP__cmrr_me4_sms4
PulseSequenceNameepfid2d4_108
ReceiveCoilActiveElementsHC1-6
ReconMatrixPE108
RefLinesPE26
RepetitionTime1.573
SequenceVariantSK\SS
SeriesDescriptionfunc-bold_task-bht_dir-AP__cmrr_me4_sms4
SeriesNumber24
ShimSetting[8231, -5635, -6006, 695, -36, -128, -25, 63]
SliceThickness2
SliceTiming[0, 0.3325, 0.6675, 1, 1.3325, 0.1125, 0.445, 0.7775, 1.11, 1.4425, 0.2225, 0.555, 0.8875, 1.22, 0, 0.3325, 0.6675, 1, 1.3325, 0.1125, 0.445, 0.7775, 1.11, 1.4425, 0.2225, 0.555, 0.8875, 1.22, 0, 0.3325, 0.6675, 1, 1.3325, 0.1125, 0.445, 0.7775, 1.11, 1.4425, 0.2225, 0.555, 0.8875, 1.22, 0, 0.3325, 0.6675, 1, 1.3325, 0.1125, 0.445, 0.7775, 1.11, 1.4425, 0.2225, 0.555, 0.8875, 1.22]
SpacingBetweenSlices2
TotalReadoutTime0.0288896
TxRefAmp244.677
WipMemBlock3c4cbc74-f09c-4033-9d8f-5cd41e5708f1||Sequence: R017 nxva30a/master r/d34922cc2+; Jun 27 2022 13:20:16 by eja

aor[0.0009280769230769232, 0.0007591025641025641, 0.0008130769230769231, 0.0010646153846153848]
aqi[0.004404740128205129, 0.006061344679487179, 0.010236250512820514, 0.019732457051282053]
dummy_trs0
dvarsnstd35.75745518612903
dvarsstd1.0694602899354835
dvarsvstd1.0308328925161292
efc0.5779
fber655.6066
fdmean0.1046306174849197
fdnum9
fdperc5.769230769230769
fwhmavg3.7451333333333334
fwhmx3.375315
fwhmy3.814555
fwhmz4.04553
gcor[0.0107588, 0.0188091, 0.0168796, 0.0132398]
gsrx0.004853485152125359
gsry0.11462006717920303
sizet156
sizex108
sizey108
sizez56
snr2.517787827334501
spacingtr1.5729999542236328
spacingx2.0
spacingy2.0
spacingz2.0
summarybgk10.5212
summarybgmad20.7279
summarybgmean136.2865
summarybgmedian32.0
summarybgn489952.0
summarybgp050.0
summarybgp95748.0
summarybgstdv260.8682
summaryfgk1.0221
summaryfgmad296.4159
summaryfgmean867.2229
summaryfgmedian821.0
summaryfgn163232.0
summaryfgp05428.0
summaryfgp951468.0
summaryfgstdv326.0789
tsnr42.382630337029696
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