Specifications

Detailed file descriptions

In the following, brackets [/] will denote optional key/value pairs in the filename while accolades {/} will indicate a list of compulsory values (e.g. hemi-{left|right} means that the key hemi only accepts left or right as values).

T1 MRI data

t1-linear - Affine registration of T1w images to the MNI standard space

subjects/
└─ sub-<participant_label>/
    └─ ses-<session_label>/
      └─ t1_linear/
         ├─ <source_file>_space-MNI152NLin2009cSym_res-1x1x1_affine.mat
         ├─ <source_file>_space-MNI152NLin2009cSym_res-1x1x1_T1w.nii.gz
         └─ <source_file>_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz

The desc-Crop indicates images of size 169×208×179 after cropping to remove the background.

t1-volume pipeline - Volume-based processing of T1-weighted MR images

Segmentation

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ t1/
         └─ spm/
            └─ segmentation/
               ├─ normalized_space/
               │  ├─ <source_file>_target-Ixi549Space_transformation-{inverse|forward}_deformation.nii.gz
               │  ├─ <source_file>_segm-<segm>_space-Ixi549Space_modulated-{on|off}_probability.nii.gz
               │  └─ <source_file>_space-Ixi549Space_T1w.nii.gz
               ├─ native_space/
               │  └─ <source_file>_segm-<segm>_probability.nii.gz
               └─ dartel_input/
                  └─ <source_file>_segm-<segm>_dartelinput.nii.gz

The modulated-{on|off} key indicates if modulation has been used in SPM to compensate for the effect of spatial normalization.

The possible values for the segm-<segm> key/value are: graymatter, whitematter, csf, bone, softtissue, and background.

The T1 image in Ixi549Space (reference space of the TPM) is obtained by applying the transformation obtained from the SPM Segmentation routine to the T1 image in native space.

DARTEL

groups/
└─ group-<group_label>/
   ├─ group-<group_label>_subjects_visits_list.tsv
   └─ t1/
      ├─ group-<group_label>_iteration-<index>_template.nii.gz
      └─ group-<group_label>_template.nii.gz

The final group template is group-<group_label>_template.nii.gz.

The group-<group_label>_iteration-<index>_template.nii.gz obtained at each iteration will only be used when obtaining flow fields for registering a new image into an existing template (SPM DARTEL Existing templates procedure).

!!! Note "Note for SPM experts" The original name of group-<group_label>_iteration-<index>_template.nii.gz is Template<index>.nii.

DARTEL to MNI

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ t1/
         └─ spm/
            └─ dartel/
               └─ group-<group_label>/
                  ├─ <source_file>_target-<group_label>_transformation-forward_deformation.nii.gz
                  └─ <source_file>_segm-<segm>_space-Ixi549Space_modulated-{on|off}[_fwhm-<X>mm]_probability.nii.gz

Atlas statistics

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ t1/
         └─ spm/
            └─ dartel/
               └─ group-<group_label>/
                  └─ atlas_statistics/
                     └─ <source_file>_space-<space>_map-graymatter_statistics.tsv

Statistics files (with _statistics.tsv suffix) are detailed in appendix.

t1-freesurfer - FreeSurfer-based processing of T1-weighted MR images

The outputs of the t1-freesurfer pipeline are split into two subfolders, the first one containing the FreeSurfer outputs and a second with additional outputs specific to Clinica.

FreeSurfer outputs:

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ t1/
         └─ freesurfer_cross_sectional/
            └─ sub-<participant_label>_ses-<session_label>/
               ├─ label/
               ├─ mri/
               ├─ scripts/
               ├─ stats/
               └─ surf/

Clinica additional outputs:

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ t1/
         └─ freesurfer_cross_sectional/
            └─ regional_measures/
               ├─ <source_file>_parcellation-wm_volume.tsv
               ├─ <source_file>_segmentationVolumes.tsv
               └─ <source_file>_hemi-{left|right}_parcellation-<parcellation>_thickness.tsv

For the file: *_hemi-{left|right}_parcellation-<parcellation>_thickness.tsv, _thickness is just an example for the cortical thickness, we also have other measurements defined in the table below:

Name	Suffix	Description
Cortical thickness	_thickness	Cortical thickness between pial surface and white surface
Cortical volume	_volume	Volume of gray matter
Cortical surface area	_area	Cortical surface area
Cortical mean curvature	_meancurv	Mean curvature of cortical surface

The hemi-{left|right} key/value stands for left or right hemisphere.

The possible values for the parcellation-<parcellation> key/value are: desikan (Desikan-Killiany Atlas), destrieux (Destrieux Atlas) and ba (Brodmann Area Maps). The TSV files for Brodmann areas contain a selection of regions, see this link for the content of this selection: http://ftp.nmr.mgh.harvard.edu/fswiki/BrodmannAreaMaps).

The details of the white matter parcellation of FreeSurfer can be found here: https://surfer.nmr.mgh.harvard.edu/pub/articles/salat_2008.pdf.

!!! Example "Example - Content of the TSV files" Content of sub-CLNC01_ses­-M00_T1w_segmentationVolumes.tsv: Text Measure:volume Left-Lateral-Ventricle Left-Inf-Lat-Vent ... /path/to/freesurfer/segmentation/ 12345.6 12.334 ...

This file contains the volume of the different subcortical structures after segmentation.

Content of `sub-CLNC01_ses­-M00_T1w_parcellation-wm_volume.tsv`:
```Text
Measure:volume	wm-lh-bankssts	wm-lh-caudalanteriorcingulate ...
/path/to/freesurfer/wm parcellation/ 2474.6	1863.7 ...
```
This file contains the volume of the different white matter regions after parcellation.


Content of `sub-CLNC01_ses­-M00_hemi-left_parcellation-desikan_thickness.tsv`:
```Text
lh.aparc.thickness	lh_bankssts_thickness	lh_caudalanteriorcingulate_thickness …
/path/to/freesurfer/cortical thickness/parcellation 2.048 2.892 …
```
This file contains the cortical thickness in different regions of the Desikan atlas.

Diffusion imaging data

dwi-preprocessing-* - Preprocessing of raw diffusion weighted imaging (DWI) datasets

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ dwi/
         └─ preprocessing/
            ├─ <source_file>_space-<space>_preproc.bval
            ├─ <source_file>_space-<space>_preproc.bvec
            ├─ <source_file>_space-<space>_preproc.nii.gz
            └─ <source_file>_space-<space>_brainmask.nii.gz

The resulting DWI file after preprocessing. According to the subtype of pipeline run, <space> can be T1w (dwi-preprocessing-using-t1 pipeline) or b0 (dwi-preprocessing-using-fieldmap pipeline). A brain mask of the preprocessed file is provided.

dwi-dti - DTI-based processing of corrected DWI datasets

subjects/
└─ sub-<participant_label>/
   └─ ses­-<session_label>/
      └─ dwi/
         └─ dti_based_processing/
            ├─ native_space/
            │  ├─ <source_file>_space-<space>_model-DTI_diffmodel.nii.gz
            │  ├─ <source_file>_space-<space>_{FA|MD|AD|RD}.nii.gz
            │  └─ <source_file>_space-<space>_DECFA.nii.gz
            │─ normalized_space/
            │  ├─ <source_file>_space-MNI152Lin_res-1x1x1_affine.mat
            │  ├─ <source_file>_space-MNI152Lin_res-1x1x1_deformation.nii.gz
            │  └─ <source_file>_space-MNI152Lin_res-1x1x1_{FA|MD|AD|RD}.nii.gz
            └─ atlas_statistics/
               └─ <source_file>_space-<space>_res-1x1x1_map-{FA|MD|AD|RD}_statistics.tsv

The DTI is saved under the *_model-DTI_diffmodel.nii.gz filename. The different maps based on the DTI are the fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) parametric maps, as well as the directionally-encoded colour (DEC) FA (DECFA) map.

Current atlases used for statistics are the 1mm version of JHUDTI81, JHUTract0 and JHUTract25 (see Atlases page for further details).

Statistics files (with _statistics.tsv suffix) are detailed in appendix.

!!! Note The naming convention for suffixes follows the BIDS derivative specifications except for the statistics files (specific files for our needs) and the _deformation.nii.gz file (it is equivalent to the _warp.nii.gz file in the BEP014 specifications).

dwi-connectome - Computation of structural connectome from corrected DWI datasets

subjects/
└─ sub-<participant_label>/
    └─ ses­-<session_label>/
        └─ dwi/
            └─ connectome_based_processing/
                ├─ <source_file>_space-{b0|T1w}_model-CSD_diffmodel.nii.gz
                │─ <source_file>_space-{b0|T1w}_model-CSD_tractography.tck
                └─ <source_file>_space-{b0|T1w}_model-CSD_parcellation-{desikan|destrieux}_connectivity.tsv

The constrained spherical deconvolution (CSD) diffusion model is saved under the *_model-CSD_diffmodel.nii.gz filename. The whole-brain tractography is saved under the *_tractography.tck filename. The connectivity matrices are saved under *_connectivity.tsv filenames.

Current parcellations used for the computation of connectivity matrices are desikan and desikan (see Atlases page for further details).

PET imaging data

pet-volume - Volume-based processing of PET images

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ pet/
         └─ preprocessing/
            └─ group-<group_label>/
               ├─ <source_file>_space-T1w_pet.nii.gz
               ├─ <source_file>_space-T1w[_pvc-rbv]_pet.nii.gz
               ├─ <source_file>_space-Ixi549Space[_pvc-rbv]_pet.nii.gz
               ├─ <source_file>_space-Ixi549Space[_pvc-rbv]_suvr-<suvr>_pet.nii.gz
               ├─ <source_file>_space-Ixi549Space_brainmask.nii.gz
               └─ <source_file>_space-Ixi549Space[_pvc-rbv]_suvr-<suvr>_mask-brain_pet.nii.gz

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ pet/
         └─ preprocessing/
            └─ atlas_statistics/
               └─ <source_file>_space-<space>[_pvc-rbv]_suvr-<suvr>_statistics.tsv

The _acq-<label> key/value describes the radiotracer used for the PET acquisition (currently supported: fdg and av45).

The [_pvc-rbv] label is optional, depending on whether your image has undergone partial volume correction (region-based voxel-wise (RBV) method) or not.

The possible values for the suvr-<suvr> key/value are: pons for FDG-PET and cerebellumPons for different types of amyloid PET.

Statistics files (with _statistics.tsv suffix) are detailed in appendix.

pet-surface - Surface-based processing of PET images

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ pet/
         └─ surface/
            ├─ atlas_statistics/
            │  └─ <source_file>_task-<label>_acq-<label>_pet_space-<space>_pvc-iy_suvr-<suvr>_statistics.tsv
            ├─ <source_file>_hemi-{left|right}_midcorticalsurface
            └─ <source_file>_hemi-{left|right}_task-rest_acq-<label>_pet_space-<space>_suvr-<suvr>_pvc-iy_hemi-{left|right}_fwhm-<label>_projection.mgh

The _acq-<label> key/value describes the radiotracer used for the PET acquisition (currently supported: fdg and av45).

The [_pvc-iy] label describes the partial volume correction used in the algorithm for the projection (Iterative Yang).

The possible values for the suvr-<suvr> key/value are: pons for FDG-PET and cerebellumPons for different types of amyloid PET

The fwhm represents the FWHM (in mm) of the Gaussian filter applied to the data mapped onto the FsAverage surface. The different values are 0 (no smoothing), 5, 10, 15, 20, and 25.

Files with the _midcorticalsurface suffix represent the surface at equal distance between the white matter/gray matter interface and the pial surface (one per hemisphere).

Files with the projection suffix are PET data that can be mapped onto meshes. If _space_fsaverage_ is in the name, it can be mapped either onto the white or pial surface of FsAverage. If _space_native_ is in the name, it can be mapped onto the white or pial surface of the subject’s surface ({l|r}h.white, {l|r}h.pial files from t1-freesurfer pipeline).

Files with the statistics suffix are text files that display average PET values on either _space-desikan or _space-destrieux atlases. Example of this content can be found in appendix.

Statistics

statistics-surface - Surface-based mass-univariate analysis with SurfStat

Group comparison

groups/
└─ group-<group_label>/
   └─ statistics/
      ├─ participants.tsv
      └─ surfstat_group_comparison/
         ├─ group-<group_label>_<group_1>-lt-<group_2>_measure-<measure>_fwhm-<label>_correctedPValue.jpg
         ├─ group-<group_label>_<group_2>-lt-<group_1>_measure-<measure>_fwhm-<label>_correctedPValue.jpg
         ├─ group-<group_label>_<group_1>-lt-<group_2>_measure-<measure>_fwhm-<label>_correctedPValue.mat
         ├─ group-<group_label>_<group_2>-lt-<group_1>_measure-<measure>_fwhm-<label>_correctedPValue.mat
         ├─ group-<group_label>_participants.tsv
         ├─ group-<group_label>_output.log
         └─ group-<group_label>_glm.json

In the case above, _correctedPValue indicates that these are maps of corrected p-values. Other types of maps include, but are not limited to:

Name	Suffix	Description
Corrected p-value	_correctedPValue	Corrected P-values for vertices and clusters level based on random field theory
Uncorrected p-value	_uncorrectedPValue	Uncorrected P-value for a generalized linear model
T-statistics	_TStatistics	T statistics for a generalized linear model
FDR	_FDR	Q-values for False Discovery Rate of resels

The <group_1>-lt-<group_2> means that the tested hypothesis is: the measurement of <group_1> is lower than (lt) that <group_2>.

Value for measure can be ct (cortical thickness from t1-freesurfer), fdg (from pet-surface) or user-defined maps. The value for fwhm corresponds to the size of the surface-based smoothing in mm and can be 5, 10, 15 or 20.

The JPEG files are simple snapshots. The *.mat files can be read later by tools like PySurfer and Surfstat.

!!! Example Text groups/ └─ group-ADvsHC/ └─ statistics/ ├─ participants.tsv └─ surfstat_group_comparison/ ├─ group-ADvsHC_AD-lt-HC_measure-ct_fwhm-20_correctedPValue.jpg ├─ group-ADvsHC_participants.tsv ├─ group-ADvsHC_output.log └─ group-ADvsHC_glm.json

Group comparison between patients with Alzheimer’s Disease (`group_1` = `AD`) and healthy subjects (`group_2` = `HC`). `ADvsHC` defines the `group_label`.

The `group-ADvsHC_glm.json` contains the information for your generalized linear model, for example:

```javascript
{
    "DesignMatrix": "1 + age + sex + group",
    "StringFormatTSV": "%s %f %f",
    "Contrast": "group",
    "ClusterThreshold": 0.001
}
```

This file describes the model that you want to create, you should include the factor and covariates in your generalized linear model as a column name in this TSV file. For example, the linear model formula is: `CorticalThickness = 1 + age + sex + group`, the contrasts (factors) `group`, `age` and `sex` are the covariates. Additional information is included in the log file.

The content of `group-ADvsHC_participants.tsv` is:
```Text
participant_id   session_id   sex      group   age
sub-CLNC0001     ses-M00      Female   CN      71.1
sub-CLNC0002     ses-M00      Male     CN      81.3
sub-CLNC0003     ses-M00      Male     CN      75.4
sub-CLNC0004     ses-M00      Female   CN      73.9
sub-CLNC0005     ses-M00      Female   AD      64.1
sub-CLNC0006     ses-M00      Male     AD      80.1
sub-CLNC0007     ses-M00      Male     AD      78.3
sub-CLNC0008     ses-M00      Female   AD      73.2
```

(Note that to make the display clearer, the rows contain successive tabs, which should not happen in an actual TSV file.)

The `group-<group_label>` key/value stands for the `group_label` for your analysis. It can be used to run different analyses for different subjects or different analyses for the same subjects.

The example image here maps statistically significant differences in cortical thickness between a group of patients with Alzheimer’s disease and a group of healthy controls (yellow: correction at the vertex level; blue: correction at the cluster level).

![](../img/StatsSurfStat_images/ContrastNegative-CorrectedPValue.jpg)

Correlation analysis

groups/
└─ group-<group_label>/
   └─ statistics/
      ├─ participants.tsv
      └─ surfstat_correlation_analysis/
         ├─ group-<group_label>_correlation-<label>_contrast-{negative|positive}_measure-<measure>_fwhm-<label>_correctedPValue.jpg
         ├─ group-<group_label>_correlation-<label>_contrast-{negative|positive}_measure-<measure>_fwhm-<label>_correctedPValue.mat
         ├─ group-<group_label>_output.log
         ├─ group-<group_label>_participants.tsv
         └─ group-<group_label>_glm.json

The correlation-<label> here describes the factor of the model which can be, for example, age. The contrast-{negative|positive} is the sign of the correlation you want to study, which can be negative or positive.

All other key/value pairs are defined in the same way as in the previous section.

statistics-volume - Volume-based mass-univariate analysis with SPM

groups/
└─ group-<group_label>/
   ├─ group-<group_label>_participants.tsv
   └─ statistics_volume/
      └─ group_comparison_measure-{graymatter|fdg|av45|<custom_user>}/
         ├─ group-<group_label>_{RPV|mask}.nii
         ├─ group-<group_label>_covariate-<covariate>_measure-<label>_fwhm-<n>_regressionCoefficient.nii
         ├─ group-<group_label>_<group_1>-lt-<group_2>_measure-<label>_fwhm-<n>_{TStatistics|contrast}.nii
         ├─ group-<group_label>_<group_2>-lt-<group_1>_measure-<label>_fwhm-<n>_{TStatistics|contrast}.nii
         ├─ group-<group_label>_report-1.png
         ├─ group-<group_label>_report-2.png
         └─ group-<group_label>_{mask|RPV|VarianceError}.nii

Suffixes are described in the table below:

Name	Suffix	Description
T-statistics	_TStatistics	T statistics for a generalized linear model
Resels per voxels	_RPV	Image of the estimated resels per voxels (known as RPV.nii in SPM)
Variance of the error	_VarianceError	Image of the variance of the error (known as ResMS.nii in SPM)
Weighted parameters	_contrast	Image of the estimated weighted parameters (known as con_000X.nii in SPM)
Regression coefficient	_regressionCoefficient	Image of the estimated regression coefficient for (known as beta_000X.nii in SPM)
Report	`_report-{1	2}`

The <group_1>-lt-<group_2> means that the tested hypothesis is: "the measurement of <group_1> is lower than (lt) that of <group_2>".

The value for measure can be graymatter (output of t1-volume), fdg or av45 (output of pet-volume), or user-defined maps. The value for fwhm corresponds to the size of the volume-based smoothing in mm.

Corrected results are stored under the following hierarchy:

groups/
└─ group-<group_label>/
   └─ statistics_volume/
      └─ group_comparison_measure-<label>/
         └─ group-<group_label>_<group_1>-lt-<group_2>_measure-<label>_fwhm-<n>_{FDRc|FDRp|FWEc|FWEc}
            ├─ group-<group_label>_<group_1>-lt-<group_2>_measure-<label>_fwhm-<n>_desc-{FDRc|FDRp|FWEc|FWEc}_axis-{x|y|z}_TStatistics.png
            └─ group-<group_label>_<group_1>-lt-<group_2>_measure-<label>_fwhm-<n>_desc-{FDRc|FDRp|FWEc|FWEc}_GlassBrain.png

FWEp (resp. FDRp) corresponds to correction for multiple comparisons with family-wise error (FWE) (resp. false discovery rate [FDR]) correction at the peak (=voxel) level with a statistical threshold of P < 0.05.

FWEc (resp. FDRc) corresponds to correction for multiple comparisons with family-wise error (FWE) (resp. false discovery rate [FDR]) correction. A statistical threshold of P < 0.001 was first applied (height threshold). An extent threshold of P < 0.05 corrected for multiple comparisons was then applied at the cluster level.

deeplearning-prepare-data - Prepare input data for deep learning with PyTorch

In the following subsections, files with the .pt extension denote tensors in PyTorch format.

Image-based outputs

subjects/
└─ <subject_id>/
   └─ <session_id>/
      └─ deeplearning_prepare_data/
         └─ image_based/
            └─ <pipeline_name>/
               └─ <source_file>_key1-<value_1>...keyN-<value_N>_suffix.pt

Given a file generated by a Clinica pipeline, the PyTorch tensor of the 3D image follows the same naming convention as the generated file except the extension that is replaced by .pt.

!!! Example "Example for the t1-linear pipeline" Text subjects/ └─ <subject_id>/ └─ <session_id>/ └─ deeplearning_prepare_data/ └─ image_based/ └─ t1_linear/ └─ <source_file>_space-MNI152NLin2009cSym[_desc-Crop]_res-1x1x1_T1w.pt

Patch-based outputs

subjects/
└─ <subject_id>/
   └─ <session_id>/
      └─ deeplearning_prepare_data/
         └─ patch_based/
            └─ <pipeline_name>/
               └─ <source_file>_key1-<value_1>...keyN-<value_N>_patchsize-<N>_stride-<M>_patch-<index>_suffix.pt

Entity	Description
patchsize-<N>	Isotropic patch size
stride-<M>	Patch stride
patch-<index>	Patch index

Given a file generated by a Clinica pipeline, the PyTorch tensor of the 3D patches follows the same naming convention except that _patchsize-<N>_stride-<M>_patch-<index> is inserted between keyN-<value> and _suffix and the extension is replaced by .pt.

!!! Example "Example for the t1-linear pipeline using a patch size and patch stride of 50" Text subjects/ └─ <subject_id>/ └─ <session_id>/ └─ deeplearning_prepare_data/ └─ patch_based/ └─ t1_linear/ └─ <source_file>_space-MNI152NLin2009cSym[_desc-Crop]_res-1x1x1_patchsize-50_stride-50_patch-<index>_T1w.pt

Slice-based outputs

subjects/
└─ <subject_id>/
   └─ <session_id>/
      └─ deeplearning_prepare_data/
         └─ slice_based/
            └─ <pipeline_name>/
               └─ <source_file>_key1-<value_1>...keyN-<value_N>_axis-{sag|cor|axi}_channel-{single|rgb}_slice-<index>_suffix.pt

Entity	Description
`axis-{sag	cor
`channel-{single	rgb}`
slice-<index>	Slice index

Given a file generated by a Clinica pipeline, the PyTorch tensor of the 2D slices follows the same naming convention except that _axis-{sag|cor|axi}_channel-{single|rgb}_slice-<index> is inserted between keyN-<value> and _suffix and the extension is replaced by .pt.

!!! Example "Example for the t1-linear pipeline using slices in sagittal plane and rgb channel" Text subjects/ └─ <subject_id>/ └─ <session_id>/ └─ deeplearning_prepare_data/ └─ slice_based/ └─ t1_linear/ └─ <source_file>_space-MNI152NLin2009cSym[_desc-Crop]_res-1x1x1_axis-sag_channel-rgb_slice-<index>_T1w.pt

Machine Learning

machinelearning-prepare-spatial-svm - Prepare input data for spatially regularized SVM

subjects/
└─ sub-<participant_label>/
   └─ ses-<session_label>/
      └─ machine_learning/
         └─ input_spatial_svm/
            └─ group-<group_label>/
               ├─ <source_file_t1w>_segm-{graymatter|whitematter|csf}_space-Ixi549Space_modulated-on_spatialregularization.nii.gz
               └─ <source_file_pet>_space-Ixi549Space[_pvc-rbv]_suvr-<suvr>_spatialregularization.nii.gz

groups/
└─ group-<group_label>/
   └─ machine_learning/
      └─ input_spatial_svm/
         ├─ group-<group_label>_space-Ixi549Space_gram.npy
         └─ group-<group_label>_space-Ixi549Space_parameters.json

At the subject level, it contains SVM regularization of gray matter/white matter/CSF maps or PET data that accounts for the spatial and anatomical structure of neuroimaging data.

At the group level, it contains the Gram matrix with respect to gray matter/white matter/CSF maps needed for the SVM regularization and the information regarding the regularization. An example of JSON file is:

{
    "MaxDeltaT": "0.0025",
    "Alpha": "0.0025", // Alpha such that: delta_t = MaxDeltaT * Alpha
    "Epsilon": "10E-6",
    "BoundaryConditions": "TimeInvariant",
    "SigmaLoc": "10",
    "TimeStepMax": "0.07760115580830161",
    "SpatialPrior": "Tissues (GM,WM,CSF)",
    "RegularizationType": "Fisher",
    "FWHM": "4",
}

Appendix - Content of a statistic file

<source_file>_space-<space>_map-<map>_statistics.tsv

Statistic file for a given atlas. The TSV file summarizes regional volumes or averages for a given parametric map. With the help of pandas (Python library), it can be easily parsed for machine learning purposes.

Possible values for _map-<map> key/value are:

• For T1: graymatter (gray matter), whitematter (white matter), csf (CSF) and ct (cortical thickness)

• For DWI: FA (fractional anisotropy), MD (mean diffusivity, also called apparent diffusion coefficient), AD (axial diffusivity), RD (radial diffusivity), NDI (neurite density index), ODI (orientation dispersion index) and FWF (free water fraction).

• For PET: fdg (¹⁸F-Fluorodeoxyglucose), av45 (¹⁸F-Florbetapir).

!!! Example Content of sub-CLNC01_ses-M00_T1w_space-Hammers_map-graymatter_statistics.tsv: Text index label_name mean_scalar 0.0 Background 0.0011357992189 1.0 Left Hippocampus 0.576250553131 2.0 Right Hippocampus 0.681780695915 3.0 Left Amygdala 0.577247679234 ... (Note that to make the display clearer, the rows contain successive tabs, which should not happen in an actual TSV file.)