Detailed file descriptions¶

This document present the specifications of the CAPS format version 1.0.0.

Versions of Clinica, BIDS, and CAPS specifications¶

Clinica version	BIDS version supported	CAPS version supported
0.9.x	1.7.0	1.0.0
0.8.x	1.7.0	no version

The dataset_description.json file¶

Specifications¶

This file MUST be present at the root of a CAPS dataset and MUST contain the following minimal information:

{
    "Name": "name identifier for the dataset",
    "BIDSVersion": "1.7.0",
    "CAPSVersion": "1.0.0",
    "DatasetType": "derivative"
}

Name: String identifier of the dataset. It can be the name of your study for example. By default Clinica generates a random UUID for this field. When running a pipeline which will create a new CAPS dataset, you can use the --caps-name option to provide a name. If the CAPS dataset already exist, the existing name will be kept.
BIDSVersion: The version number of the BIDS specifications that the BIDS input dataset is using when this CAPS dataset was generated.
CAPSVersion: The version number of the CAPS specifications used for this dataset.
DatasetType: Either "raw" or "derivative". For a CAPS dataset this should always be "derivative" as it contains processed data.

In addition, the dataset_description.json file MAY contain a Processing key which is a list of objects describing the different processing pipelines that were run on this CAPS. Here is an example for a CAPS dataset containing the outputs of two pipelines: t1-linear and pet-linear:

{
    "Name": "e6719ef6-2411-4ad2-8abd-da1fd8fbdf32",
    "BIDSVersion": "1.7.0",
    "CAPSVersion": "1.0.0",
    "DatasetType": "derivative",
    "Processing": [
        {
            "Name": "t1-linear",
            "Date": "2024-08-06T10:28:21.848950",
            "Author": "ci",
            "Machine": "ubuntu",
            "InputPath": "/mnt/data_ci/T1Linear/in/bids"
        },
        {
            "Name": "pet-linear",
            "Date": "2024-08-06T10:36:27.403373",
            "Author": "ci",
            "Machine": "ubuntu",
            "InputPath": "/mnt/data_ci/PETLinear/in/bids"
        }
    ]
}

A Processing is described with the following fields:

Name: The name of the processing. For Clinica pipelines, this is the name of the pipeline.
Date: This date is in iso-format and indicates when the processing was run.
Author: This indicates the user name which triggered the processing.
Machine: This indicates the name of the machine on which the processing was run.
InputPath: This is the full path (on the machine on which the processing was run) to the input dataset of the processing.

Potential problems¶

The dataset_description.json file for CAPS datasets was introduced in Clinica 0.9.0.

This means that results obtained with prior versions of Clinica do not have this file automatically generated. Clinica will interpret this as a <1.0.0 dataset and should error with a suggestion of a minimal dataset_description.json that you should add to your dataset. In this situation, create this new file with the suggested content and re-start the pipeline.

You might also see the following error message:

Impossible to write the 'dataset_description.json' file in <FOLDER> because it already exists and it contains incompatible metadata.

This means that you have version mismatch for the BIDS and/or CAPS specifications. That is, the versions indicated in the input (or output) dataset(s) does not match the versions currently used by Clinica. If this happens, it is recommended to re-run the conversion or the pipeline which initially generated the dataset with the current version of Clinica.

Subjects and Groups folders¶

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).

Finally:

participant_id denotes participant-<participant_label>;
session_id denotes session-<session_label>;
group_id denotes group-<group_label>;
long_id denotes long-<long_label>.

T1 MRI data¶

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

dataset_description.json
subjects/
└─ <participant_id>/
    └─ <session_id>/
      └─ 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¶

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ 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¶

dataset_description.json
groups/
└─ <group_id>/
   ├─ <group_id>_subjects_visits_list.tsv
   └─ t1/
      ├─ <group_id>_iteration-<index>_template.nii.gz
      └─ <group_id>_template.nii.gz

The final group template is <group_id>_template.nii.gz.
The <group_id>_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 for SPM experts

The original name of <group_id>_iteration-<index>_template.nii.gz is Template<index>.nii.

DARTEL to MNI¶

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ t1/
         └─ spm/
            └─ dartel/
               └─ <group_id>/
                  ├─ <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¶

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ t1/
         └─ spm/
            └─ dartel/
               └─ <group_id>/
                  └─ 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 sub-folders, the first one containing the FreeSurfer outputs and a second with additional outputs specific to Clinica.

FreeSurfer outputs:

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ t1/
         └─ freesurfer_cross_sectional/
            └─ <participant_id>_<session_id>/
               ├─ label/
               ├─ mri/
               ├─ scripts/
               ├─ stats/
               └─ surf/

Clinica additional outputs:

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ 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 - Content of the TSV files

Content of sub-CLNC01_ses-M000_T1w_segmentationVolumes.tsv:

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-M000_T1w_parcellation-wm_volume.tsv:

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-M000_hemi-left_parcellation-desikan_thickness.tsv:

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.

`t1-freesurfer-longitudinal` – FreeSurfer-based longitudinal processing of T1-weighted MR images¶

FreeSurfer unbiased templates¶

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <long_id>/
      └─ freesurfer_unbiased_template/
         └─ <participant_id>_<long_id>/
            ├─ label/
            ├─ mri/
            ├─ scripts/
            ├─ stats/
            └─ surf/

FreeSurfer longitudinal outputs¶

The outputs are split into two sub-folders, the first containing the FreeSurfer longitudinal outputs and a second with additional outputs specific to Clinica.

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ t1/
         └─ <long_id>/
            └─ freesurfer_longitudinal/
               └─ <participant_id>_<session_id>.long.<participant_id>_<long_id>/
                  ├─ label/
                  ├─ mri/
                  ├─ scripts/
                  ├─ stats/
                  └─ surf/

Clinica additional outputs:

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ t1/
         └─ <long_id>/
            └─ freesurfer_longitudinal/
               └─ regional_measures/
                  ├─ <participant_id>_<session_id>_<long_id>_parcellation-wm_volume.tsv
                  ├─ <participant_id>_<session_id>_<long_id>_segmentationVolumes.tsv
                  └─ <participant_id>_<session_id>_<long_id>_hemi-{left|right}_parcellation-<parcellation>_thickness.tsv

where each file is explained in the t1-freesurfer subsection.

Note

The naming convention <subject_name>.long.<template_name> is imposed by FreeSurfer.

Diffusion imaging data¶

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

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ dwi/
         └─ preprocessing/
            ├─ <source_file>_space-<space>_desc-preproc_dwi.bval
            ├─ <source_file>_space-<space>_desc-preproc_dwi.bvec
            ├─ <source_file>_space-<space>_desc-preproc_dwi.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)
b0 (dwi-preprocessing-using-fieldmap pipeline)

A brain mask of the preprocessed file is provided.

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

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ 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:

FA: fractional anisotropy.
MD: mean diffusivity.
AD: axial diffusivity.
RD: radial diffusivity.
DECFA: directionally-encoded colour (DEC) FA.

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¶

dataset_description.json
subjects/
└─ <participant_id>/
    └─ <session_id>/
        └─ 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¶

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ pet/
         └─ preprocessing/
            └─ <group_id>/
               ├─ <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

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ pet/
         └─ preprocessing/
            └─ atlas_statistics/
               └─ <source_file>_space-<space>[_pvc-rbv]_suvr-<suvr>_statistics.tsv

The _trc-<label> key/value describes the radiotracer used for the PET acquisition (currently supported: 18FFDG and 18FAV45).
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¶

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ pet/
         └─ surface/
            ├─ atlas_statistics/
            │  └─ <source_file>_trc-<label>_pet_space-<space>_pvc-iy_suvr-<suvr>_statistics.tsv
            ├─ <source_file>_hemi-{left|right}_midcorticalsurface
            └─ <source_file>_hemi-{left|right}_trc-<label>_pet_space-<space>_suvr-<suvr>_pvc-iy_hemi-{left|right}_fwhm-<label>_projection.mgh

The trc-<label> key/value describes the radiotracer used for the PET acquisition (currently supported: 18FFDG and 18FAV45).
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.

`pet-surface-longitudinal` - Surface-based longitudinal processing of PET images¶

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ pet/
         └─ <long_id>/
            └─ surface_longitudinal/
               ├─ atlas_statistics/
               │  └─ sub-<label>_ses-<lalbel>_long-<label>_trc-<label>_pet_space-<space>_pvc-iy_suvr-<suvr>_statistics.tsv
               ├─ sub-<label>_ses-<lalbel>_long-<label>_hemi-{left|right}_midcorticalsurface
               └─ sub-<label>_ses-<lalbel>_long-<label>_trc-<label>_pet_space-<space>_suvr-<suvr>_pvc-iy_hemi-{left|right}_fwhm-<label>_projection.mgh

Explanations on the key/values can be found on the pet-surface section.

`pet-linear` - Affine registration of PET images to the MNI standard space¶

dataset_description.json
subjects/
└─ <participant_id>/
    └─ <session_id>/
      └─ pet_linear/
         ├─ <source_file>_space-T1w_rigid.mat
         ├─ <source_file>_space-T1w_pet.nii.gz
         ├─ <source_file>_space-MNI152NLin2009cSym_res-1x1x1_suvr-<suvr_label>_pet.nii.gz
         └─ <source_file>_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_suvr-<suvr_label>_pet.nii.gz

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

Statistics¶

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

Group comparison¶

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

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:

{
    "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:

participant_id   session_id   sex      group   age
sub-CLNC0001     ses-M000      Female   CN      71.1
sub-CLNC0002     ses-M000      Male     CN      81.3
sub-CLNC0003     ses-M000      Male     CN      75.4
sub-CLNC0004     ses-M000      Female   CN      73.9
sub-CLNC0005     ses-M000      Female   AD      64.1
sub-CLNC0006     ses-M000      Male     AD      80.1
sub-CLNC0007     ses-M000      Male     AD      78.3
sub-CLNC0008     ses-M000      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_id> 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).

Correlation analysis¶

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

dataset_description.json
groups/
└─ <group_id>/
   ├─ <group_id>_participants.tsv
   └─ statistics_volume/
      └─ group_comparison_measure-{graymatter|18FFDG|18FAV45|<custom_user>}/
         ├─ <group_id>_{RPV|mask}.nii
         ├─ <group_id>_covariate-<covariate>_measure-<label>_fwhm-<n>_regressionCoefficient.nii
         ├─ <group_id>_<group_1>-lt-<group_2>_measure-<label>_fwhm-<n>_{TStatistics|contrast}.nii
         ├─ <group_id>_<group_2>-lt-<group_1>_measure-<label>_fwhm-<n>_{TStatistics|contrast}.nii
         ├─ <group_id>_report-1.png
         ├─ <group_id>_report-2.png
         └─ <group_id>_{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}`	SPM report containing FWE/FDR peak/cluster thresholds to report for subsequent corrections

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), 18FFDG or 18FAV45 (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:

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

Machine Learning¶

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

dataset_description.json
subjects/
└─ <participant_id>/
   └─ <session_id>/
      └─ machine_learning/
         └─ input_spatial_svm/
            └─ <group_id>/
               ├─ <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

dataset_description.json
groups/
└─ <group_id>/
   └─ machine_learning/
      └─ input_spatial_svm/
         ├─ <group_id>_space-Ixi549Space_gram.npy
         └─ <group_id>_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",
    "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: 18FFDG (¹⁸F-Fluorodeoxyglucose), 18FAV45 (¹⁸F-Florbetapir), 18FAV1451 (¹⁸F-Flortaucipir), 11CPIB (¹¹C-Pittsburgh Compound-B), 18FFBB (¹⁸F-Florbetaben) and 18FFMM (¹⁸F-Flutemetamol).

Example

Content of sub-CLNC01_ses-M000_T1w_space-Hammers_map-graymatter_statistics.tsv:

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.)

Contact us !¶

Check for past answers on Clinica Google Group
Start a discussion on GitHub
Report an issue on Github

Detailed file descriptions¶

Versions of Clinica, BIDS, and CAPS specifications¶

The dataset_description.json file¶

Specifications¶

Potential problems¶

Subjects and Groups folders¶

T1 MRI data¶

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

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

Segmentation¶

DARTEL¶

DARTEL to MNI¶

Atlas statistics¶

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

t1-freesurfer-longitudinal – FreeSurfer-based longitudinal processing of T1-weighted MR images¶

FreeSurfer unbiased templates¶

FreeSurfer longitudinal outputs¶

Diffusion imaging data¶

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

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

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

PET imaging data¶

pet-volume - Volume-based processing of PET images¶

pet-surface - Surface-based processing of PET images¶

pet-surface-longitudinal - Surface-based longitudinal processing of PET images¶

pet-linear - Affine registration of PET images to the MNI standard space¶

Statistics¶

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

Group comparison¶

Correlation analysis¶

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

Machine Learning¶

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

Appendix - Content of a statistic file¶

Contact us !¶

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

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

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

`t1-freesurfer-longitudinal` – FreeSurfer-based longitudinal processing of T1-weighted MR images¶

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

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

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

`pet-volume` - Volume-based processing of PET images¶

`pet-surface` - Surface-based processing of PET images¶

`pet-surface-longitudinal` - Surface-based longitudinal processing of PET images¶

`pet-linear` - Affine registration of PET images to the MNI standard space¶

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

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

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