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package: niworkflows
sections:
- name: Summary
reportlets:
- bids: {datatype: anat, desc: summary, suffix: T1w}
- name: Anatomical
reportlets:
- bids:
datatype: anat
desc: conform
extension: [.html]
suffix: T1w
- bids: {datatype: anat, suffix: dseg}
caption: This panel shows the template T1-weighted image (if several T1w images
were found), with contours delineating the detected brain mask and brain tissue
segmentations.
subtitle: Brain mask and brain tissue segmentation of the T1w
- bids: {datatype: anat, space: .*, suffix: T1w, regex_search: True}
caption: Spatial normalization of the T1w image to the <code>{space}</code> template.
description: Results of nonlinear alignment of the T1w reference one or more template
space(s). Hover on the panels with the mouse pointer to transition between both
spaces.
static: false
subtitle: Spatial normalization of the anatomical T1w reference
- bids: {datatype: anat, desc: reconall, suffix: T1w}
caption: Surfaces (white and pial) reconstructed with FreeSurfer (<code>recon-all</code>)
overlaid on the participant's T1w template.
subtitle: Surface reconstruction
- name: Fieldmaps
ordering: session
reportlets:
- bids: {datatype: fmap, desc: brain, suffix: mask}
caption: Brain extraction of the magnitude image from the fieldmap.
subtitle: Skull stripped magnitude image
- name: Functional
ordering: session,task,run
reportlets:
- bids: {datatype: func, desc: summary, suffix: bold}
- bids: {datatype: func, desc: validation, suffix: bold}
- bids: {datatype: func, desc: magnitude, suffix: bold}
caption: Results of affine coregistration between the magnitude image of the fieldmap
and the reference EPI image
static: false
subtitle: Fieldmap to EPI registration
- bids: {datatype: func, desc: fieldmap, suffix: bold}
caption: Overlaid on the reference EPI image.
static: false
subtitle: Fieldmap
- bids: {datatype: func, desc: sdc, suffix: bold}
caption: Results of performing susceptibility distortion correction (SDC) on the
EPI
static: false
subtitle: Susceptibility distortion correction
- bids: {datatype: func, desc: forcedsyn, suffix: bold}
caption: The dataset contained some fieldmap information, but the argument <code>--force-syn</code>
was used. The higher-priority SDC method was used. Here, we show the results
of performing SyN-based SDC on the EPI for comparison.
static: false
subtitle: Experimental fieldmap-less susceptibility distortion correction
- bids: {datatype: func, desc: flirtnobbr, suffix: bold}
caption: FSL <code>flirt</code> was used to generate transformations from EPI
space to T1 Space - BBR refinement rejected. Note that Nearest Neighbor interpolation
is used in the reportlets in order to highlight potential spin-history and other
artifacts, whereas final images are resampled using Lanczos interpolation.
static: false
subtitle: Alignment of functional and anatomical MRI data (volume based)
- bids: {datatype: func, desc: coreg, suffix: bold}
caption: <code>mri_coreg</code> (FreeSurfer) was used to generate transformations
from EPI space to T1 Space - <code>bbregister</code> refinement rejected. Note
that Nearest Neighbor interpolation is used in the reportlets in order to highlight
potential spin-history and other artifacts, whereas final images are resampled
using Lanczos interpolation.
static: false
subtitle: Alignment of functional and anatomical MRI data (volume based)
- bids: {datatype: func, desc: flirtbbr, suffix: bold}
caption: FSL <code>flirt</code> was used to generate transformations from EPI-space
to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain
tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation
is used in the reportlets in order to highlight potential spin-history and other
artifacts, whereas final images are resampled using Lanczos interpolation.
static: false
subtitle: Alignment of functional and anatomical MRI data (surface driven)
- bids: {datatype: func, desc: bbregister, suffix: bold}
caption: <code>bbregister</code> was used to generate transformations from EPI-space
to T1w-space. Note that Nearest Neighbor interpolation is used in the reportlets
in order to highlight potential spin-history and other artifacts, whereas final
images are resampled using Lanczos interpolation.
static: false
subtitle: Alignment of functional and anatomical MRI data (surface driven)
- bids: {datatype: func, desc: rois, suffix: bold}
caption: Brain mask calculated on the BOLD signal (red contour), along with the
masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative
CSF and white-matter mask for extracting physiological and movement confounds.
<br />The fCompCor mask (blue contour) contains the top 5% most variable voxels
within a heavily-eroded brain-mask.
subtitle: Brain mask and (temporal/anatomical) CompCor ROIs
- bids:
datatype: func
desc: '[at]compcor'
extension: [.html]
suffix: bold
- bids: {datatype: func, desc: 'compcorvar', suffix: bold}
caption: The cumulative variance explained by the first k components of the
<em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>.
The number of components that must be included in the model in order to
explain some fraction of variance in the decomposition mask can be used
as a feature selection criterion for confound regression.
subtitle: Variance explained by t/aCompCor components
- bids: {datatype: func, desc: carpetplot, suffix: bold}
caption: Summary statistics are plotted, which may reveal trends or artifacts
in the BOLD data. Global signals calculated within the whole-brain (GS), within
the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD
signal in their corresponding masks. DVARS and FD show the standardized DVARS
and framewise-displacement measures for each time point.<br />A carpet plot
shows the time series for all voxels within the brain mask. Voxels are grouped
into cortical (blue), and subcortical (orange) gray matter, cerebellum (green)
and white matter and CSF (red), indicated by the color map on the left-hand
side.
subtitle: BOLD Summary
- bids: {datatype: func, desc: 'confoundcorr', suffix: bold}
caption: |
Left: Heatmap summarizing the correlation structure among confound variables.
(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
Right: magnitude of the correlation between each confound time series and the
mean global signal. Strong correlations might be indicative of partial volume
effects and can inform decisions about feature orthogonalization prior to
confound regression.
subtitle: Correlations among nuisance regressors
- bids: {datatype: func, desc: aroma, suffix: bold}
caption: |
Maps created with maximum intensity projection (glass brain) with a
black brain outline. Right hand side of each map: time series (top in seconds),
frequency spectrum (bottom in Hertz). Components classified as signal are plotted
in green; noise components in red.
subtitle: ICA Components classified by AROMA
- name: About
reportlets:
- bids: {datatype: anat, desc: about, suffix: T1w}
template_path: report.tpl
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