# Summary

- Subject ID: 01
- Structural images: 1 T1-weighted
- PET series: 1

- Task: (1 run)

- Standard output spaces: MNI152NLin2009cAsym
- Non-standard output spaces: anat
- FreeSurfer reconstruction: Pre-existing directory

# Anatomical

#### A previously computed T1w template was provided.

### Brain mask and brain tissue segmentation of the T1w

This panel shows the final, preprocessed T1-weighted image, with contours delineating the detected brain mask and brain tissue segmentations.

Get figure file: figures/sub-01_desc-preproc_dseg.svg

This panel shows the final, preprocessed T1-weighted image, with contours delineating the detected brain mask and brain tissue segmentations.

Get figure file: figures/sub-01_dseg.svg

### Spatial normalization of the anatomical T1w reference

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.

Spatial normalization of the T1w image to the `MNI152NLin2009cAsym` template.

Get figure file: figures/sub-01_space-MNI152NLin2009cAsym_desc-preproc_T1w.svg

### Surface reconstruction

Surfaces (white and pial) reconstructed with FreeSurfer (`recon-all`) overlaid on the participant's T1w template.

Get figure file: figures/sub-01_desc-reconall_T1w.svg

# PET

## Reports for: session baseline.

### PET Acquisition and Workflow Summary

Summary of PET data acquisition parameters and processing workflow overview, including details such as injected dose, radiotracer used, and scan duration.

Summary

- Original orientation: LAS
- Registration: Precomputed affine transformation
- Time zero: 12:00:48
- Radiotracer: [11C]PS_13
- Injected dose: 719650 kBq
- Scan duration: 90.0 minutes
- Number of frames: 27
- Frame start times (seconds): [0, 30, 60, 90, 120, 150, 180, 240, 300, 360, 480, 600, 900, 1200, 1500, 1800, 2100, 2400, 2700, 3000, 3300, 3600, 3900, 4200, 4500, 4800, 5100]
- Frame durations (seconds): [30, 30, 30, 30, 30, 30, 60, 60, 60, 120, 120, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300, 300]

### PET Summary and Carpet Plot

Summary statistics and global PET signal measures are presented. A carpet plot displays voxel-level PET tracer uptake over time within the brain mask. Global signals calculated across the whole-brain (GS), white matter (WM), and cerebrospinal fluid (CSF) regions are plotted, along with DVARS and framewise displacement (FD) to visualize potential motion or acquisition artifacts. "Ctx" = cortex, "Cb" = cerebellum, "WM" = white matter, "CSF" = cerebrospinal fluid.

Get figure file: figures/sub-01_ses-baseline_desc-carpetplot_pet.svg

### PET Confound Correlation

Left: Correlation heatmap illustrating relationships among PET-derived confound variables (e.g., motion parameters, global signal). Right: Magnitude of correlation between each PET confound time series and the global PET signal. High correlations suggest potential partial volume effects or motion-induced artifacts, informing subsequent confound regression strategies.

Get figure file: figures/sub-01_ses-baseline_desc-confoundcorr_pet.svg

### Additional PET Visualizations

PET to anatomical alignment check

Get figure file: figures/sub-01_ses-baseline_desc-coreg_pet.svg

### Reference mask check

Reference region mask overlaid on the PET reference and anatomical.

Get figure file: figures/sub-01_ses-baseline_ref-cerebellum_desc-refmask_pet.svg

Reference region mask overlaid on the PET reference and anatomical.

Get figure file: figures/sub-01_ses-baseline_ref-neocortex_desc-refmask_pet.svg

# About

- PETPrep version: 0.0.5
- PETPrep command: `/Users/martinnorgaard/anaconda3/envs/petprep/bin/petprep /Users/martinnorgaard/Desktop/ses-baseline/test_data/ /Users/martinnorgaard/Desktop/ses-baseline/test_data/derivatives/petprep participant --fs-subjects-dir /Users/martinnorgaard/Desktop/ses-baseline/test_data/derivatives/freesurfer/ --no-msm --seg gtm --derivatives /Users/martinnorgaard/Desktop/ses-baseline/test_data/derivatives/petprep --output-spaces anat --ref-mask-name neocortex`
- Date preprocessed: 2025-07-25 12:40:09 +0200

## Methods

We kindly ask to report results preprocessed with this tool using the following boilerplate.

Copyright Waiver. The boilerplate text was automatically generated by NiReports with the express intention that users should copy and paste this text into their manuscripts unchanged. It is released under the CC0 license.

    Results included in this manuscript come from preprocessing
    performed using *PETPrep* 25.0.0.dev571+g8a71cbe
    (@fmriprep1; @fmriprep2; RRID:SCR_016216),
    which is based on *Nipype* 1.10.0
    (@nipype1; @nipype2; RRID:SCR_002502).

    Anatomical data preprocessing

    : A total of 1 T1-weighted (T1w) images were found within the input
    BIDS dataset. A preprocessed T1w image was provided as a precomputed input
    and used as T1w-reference throughout the workflow.
    A pre-computed brain mask was provided as input and used throughout the workflow.
    Precomputed discrete tissue segmentations were provided as inputs.
    Precomputed tissue probabiilty maps were provided as inputs.
    Brain surfaces were reconstructed using `recon-all` [FreeSurfer 7.4.1,
    RRID:SCR_001847, @fs_reconall], and the brain mask estimated
    previously was refined with a custom variation of the method to reconcile
    ANTs-derived and FreeSurfer-derived segmentations of the cortical
    gray-matter of Mindboggle [RRID:SCR_002438, @mindboggle].

    PET data preprocessing

    : For each of the 1 PET runs found per subject (across all
    tasks and sessions), the following preprocessing was performed.
    Several confounding time-series were calculated based on the
    *preprocessed PET*: framewise displacement (FD), DVARS and
    three region-wise global signals.
    FD was computed using two formulations following Power (absolute sum of
    relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
    displacement between affines, @mcflirt).
    FD and DVARS are calculated for each PET run, both using their
    implementations in *Nipype* [following the definitions by @power_fd_dvars].
    The three global signals are extracted within the CSF, the WM, and
    the whole-brain masks.
    Additionally, a set of physiological regressors were extracted to
    allow for component-based noise correction [*CompCor*, @compcor].
    Principal components are estimated after high-pass filtering the
    *preprocessed PET* time-series (using a discrete cosine filter with
    128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
    and anatomical (aCompCor).
    tCompCor components are then calculated from the top 2% variable
    voxels within the brain mask.
    For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
    are generated in anatomical space.
    The implementation differs from that of Behzadi et al. in that instead
    of eroding the masks by 2 pixels on PET space, a mask of pixels that
    likely contain a volume fraction of GM is subtracted from the aCompCor masks.
    This mask is obtained by dilating a GM mask extracted from the FreeSurfer's *aseg* segmentation, and it ensures components are not extracted
    from voxels containing a minimal fraction of GM.
    Finally, these masks are resampled into PET space and binarized by
    thresholding at 0.99 (as in the original implementation).
    Components are also calculated separately within the WM and CSF masks.
    For each CompCor decomposition, the *k* components with the largest singular
    values are retained, such that the retained components' time series are
    sufficient to explain 50 percent of variance across the nuisance mask (CSF,
    WM, combined, or temporal). The remaining components are dropped from
    consideration.
    The head-motion estimates calculated in the correction step were also
    placed within the corresponding confounds file.
    The confound time series derived from head motion estimates and global
    signals were expanded with the inclusion of temporal derivatives and
    quadratic terms for each [@confounds_satterthwaite_2013].
    Frames that exceeded a threshold of 0.5 mm FD or
    1.5 standardized DVARS were annotated as motion outliers.
    Additional nuisance timeseries are calculated by means of principal components
    analysis of the signal found within a thin band (*crown*) of voxels around
    the edge of the brain, as proposed by [@patriat_improved_2017].
    All resamplings can be performed with *a single interpolation
    step* by composing all the pertinent transformations (i.e. head-motion
    transform matrices, susceptibility distortion correction when available,
    and co-registrations to anatomical and output spaces).
    Gridded (volumetric) resamplings were performed using `nitransforms`,
    configured with cubic B-spline interpolation.

    Many internal operations of *PETPrep* use
    *Nilearn* 0.11.1 [@nilearn, RRID:SCR_001362],
    mostly within the PET processing workflow.
    For more details of the pipeline, see [the section corresponding
    to workflows in *PETPrep*'s documentation](https://petprep.readthedocs.io/en/latest/workflows.html "PETPrep's documentation").

    ### Copyright Waiver

    The above boilerplate text was automatically generated by PETPrep
    with the express intention that users should copy and paste this
    text into their manuscripts *unchanged*.
    It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.

    ### References

## Errors

No errors to report!