chimera-brainparcellation 0.3.0

An open source framework for combining multiple parcellations of the human brain

CHIMERA: An open source framework for combining multiple parcellations

Creating multi-source parcellations of the human brain is a fundamental task at several steps of the MRI analysis research workflow. Chimera facilitates this otherwise difficult operation with an intuitive and flexible interface for humans and machines, thereby assisting in the construction of sophisticated and more reliable processing pipelines. This repository contains the source code and atlases needed by Chimera.

📖 Documentation

Full documentation is available at: https://chimera-brainparcellation.readthedocs.io

The documentation includes:

• Complete API reference
• Installation guide
• Usage examples
• Parcellation methodology details

Parcellations fusion

Chimera defines ten different supra-regions (cortex, basal ganglia, thalamus, amygdala, hippocampus, hypothalamus, cerebellum, brainstem, gyral white matter, and white-matter). Basal ganglia includes only the regions that are not labeled as supra-regions. Subdivisions in each supra-region will be populated with the parcellation information of a single source. The available parcellation sources per supra-region, as well as one corresponding parcellation name, and a one-character unique identifier are configured in a JSON (JavaScript Object Notation) file.
Chimera code: A sequence of ten one-character identifiers (one per each supra-region) unambiguosly denotes a single instance of combined parcellation (Figure. 1B). Given the sequence of ten identifier characters, Chimera selects the atlas and/or applies the corresponding methodology to obtain the parcellation for each supra-region. These supra-region-specific parcellations are finally integrated to obtain the combined volumetric parcellation for each input subject, as well as its corresponding tab-separated values table of labels, region names, and rendering colors for visualization. Chimera uses FreeSurfer to map cortical templates from fsaverage to individual space. It also applies different methods to obtain the hippocampal subfields and brainstem parcellations as well as the thalamic, amygdala and hypothalamic nuclei segmentations. FIRST and ANTs are also used for segmenting subcortical structures and thalamic nuclei respectively.

Requirements

Required Python Packages

Standard Library (Built-in, no installation required)

• argparse - Command-line argument parsing
• csv - CSV file reading and writing
• datetime - Date and time handling
• json - JSON encoder and decoder
• operator - Standard operators as functions
• os - Operating system interface
• pathlib - Object-oriented filesystem paths
• shutil - High-level file operations
• subprocess - Subprocess management
• sys - System-specific parameters and functions
• time - Time access and conversions
• typing - Support for type hints

Data Science & Analysis

• numpy - Fundamental package for scientific computing
• pandas - Data manipulation and analysis library
• scipy - Scientific computing library

Neuroimaging & Medical Data

• nibabel - Access to neuroimaging file formats
• pybids - BIDS (Brain Imaging Data Structure) toolkit
• templateflow - Neuroimaging template management

CLI & User Interface

• rich - Rich text and beautiful formatting for terminals

Specialized Tools

• clabtoolkit - Connectomics Lab Toolkit

Installation

Install from PyPI (Recommended)

The easiest way to install CHIMERA is using pip:

pip install chimera-brainparcellation

This will automatically install all required dependencies including:

• pandas
• pybids
• numpy
• nibabel
• rich
• scipy
• templateflow
• clabtoolkit (NB: Clabtoolkit requires the dev version as of 20250916)

Manual Installation

Alternatively, you can install all required external packages manually:

pip install pandas pybids numpy nibabel rich scipy templateflow clabtoolkit

Or using a requirements.txt file:

pip install -r requirements.txt

requirements.txt content:

pandas
pybids
numpy
nibabel
rich
scipy
templateflow
clabtoolkit

Or using the yaml file:

conda env create -f environment.yaml -n chimera --solver=libmamba

Or on Mac:

conda env create -f environment-mac.yaml -n chimera --solver=libmamba

Required image processing packages:

• FreeSurfer (version>7.2.0), FSL, ANTs

---

Options:

Brief description of input options:

Option	Description
--regions, -r	List available parcellations for each supra-region.
--bidsdir, -b	BIDs dataset folder. Different BIDs directories could be entered separating them by a comma.
--derivdir, -d	Derivatives folder. Different directories could be entered separating them by a comma.
--parcodes, -p	Sequence of ten one-character identifiers (one per each supra-region).
--freesurferdir, -fr	FreeSurfer subjects dir. If the folder does not exist it will be created.
--scale, -s	Scale identification. This option should be supplied for multi-resolution cortical parcellations (e.g. Lausanne or Schaeffer).
--seg, -e	Segmentation identifier.
--nthreads, -n	Number of processes to run in parallel (default= Number of cores - 4).
--growwm, -g	Grow of GM labels inside the white matter (mm).
--subjids, -ids	Subject IDs. Multiple subject ids can be specified separating them by a comma.
--mergectx,, -mctx	Join cortical white matter and cortical gray matter regions.
--force, -f	Overwrite the results.
--verbose, -v	Verbose (0, 1 or 2).
--help, -h	Help.

---

Usage

General command line to use Chimera:

    $ chimera -b <BIDs directory> -d <Derivatives directory> -p <Chimera code>

This command will run Chimera for all the subjects in the BIDs directory.

Simple examples

1. Running Chimera for 3 different parcellation codes (LFMFIIFIF,SFMFIIFIF,CFMFIIFIF). This will obtain the combined parcellations for all the T1-weighted images inside the BIDs dataset.

    $ chimera -b <BIDs directory> -d <Derivatives directory> -p LFMFIIFIF,SFMFIIFIF,CFMFIIFI

2. Running Chimera for T1-weighted images included in a txt file:

    $ chimera -b <BIDs directory> -d <Derivatives directory> -p LFMFIIFIF -ids <t1s.txt>

Example of t1s.txt file | sub-00001_ses-0001_run-2 | sub-00001_ses-0003_run-1 | sub-00001_ses-post_acq-mprage

3. Cortical volumes will grow 0 and 2 mm respectively inside the white matter for the selected cortical parcellations.

    $ chimera -b <BIDs directory> -d <Derivatives directory> -p LFMFIIFIF -g 0,2

Main files in the repository

1. chimera.py__: Main python library for performing Chimera parcellations.
2. supraregions_dictionary.json__: JSON file especifying the available parcellation sources per supra-region.
3. annot_atlases and gcs_atlases: Folder containing cortical atlases in .annot and .gcs file formats.

Parcellations and methodologies for each supra-region

1. Cortical (Supra-region: Cortical)

Code	Name	Citation	Code	Name	Citation
A	AALv2	Rolls et al, 2015	B	Brainnetome	Fan et al, 2016
C	Campbell	Campbell, 1905	D	Desikan-Killiany	Desikan et al, 2006
F	Flechsig	Flechsig, 1920	H	HCP-MMP1	Glasser et al, 2016
K	Kleist	Kleist, 1934	L	Lausanne	Symmetric version of Cammoun et al, 2012
M	Smith	Smith et al, 1907	R	Broadmann	Broadmann, 1909
S	Schaefer	Schaefer et al, 2018	T	Desikan-Killiany-Tourville	Klein and Tourville, 2012
V	vonEconomo	von Economo and Koskinas, 1925	X	Destrieux	Destrieux et al, 2009
Y	Yeo	Yeo et al, 2011

2. Subcortical (Supra-region: Subcortical)

Code	Name	Citation
F	Aseg	Fischl et al, 2002
R	FIRST	Patenaude et al, 2011

3. Thalamus (Supra-region: Thalamus)

Code	Name	Citation	Code	Name	Citation
F	Aseg	Fischl et al, 2002	I	FSThalParc	Iglesias et al, 2018
M	MIALThalParc	Najdenovska and Aleman-Gomez et al, 2018	R	FIRST	Patenaude et al, 2011

4. Amygdala (Supra-region: Amygdala)

Code	Name	Citation
F	Aseg	Fischl et al, 2002
I	FSAmygHippoParc	Saygin et al, 2017
R	FIRST	Patenaude et al, 2011

5. Hippocampus (Supra-region: Hippocampus)

Code	Name	Citation	Code	Name	Citation
F	Aseg	Fischl et al, 2002	I	FSAmygHippoParc	Iglesias et al, 2015
H	HBT	Iglesias et al, 2015	R	FIRST	Patenaude et al, 2011

6. Hypothalamus (Supra-region: Hypothalamus)

Code	Name	Citation
F	Aseg	Based on in-house protocol
I	FSHypoThalParc	Billot et al, 2020

7. Cerebellum (Supra-region: Cerebellum)

Code	Name	Citation
A	AALv2	Rolls et al, 2015
F	Aseg	Fischl et al, 2002
S	SUIT	Diedrichsen, J. et al, 2009

8. Brainstem (Supra-region: Brainstem)

Code	Name	Citation
F	Aseg	Fischl et al, 2002
I	FSBrainStemParc	Iglesias et al, 2015
R	FIRST	Patenaude et al, 2011

9. Gyral White Matter (Supra-region: GyralWM)

Code	Name	Citation
F	Cortical	Depends on the cortical parcellation

10. White Matter (Supra-region: WhiteMatter)

Code	Name	Citation
F	Aseg	Fischl et al, 2002
J	JHU	Hua et al, 2008

Results

Chimera parcellations were generated using the following codes: LFMIIIFIF, HFIIIIFIF, BFIIHIFIF (162, 492 and 314 regions respectively). Figure 2A shows the corresponding results of the fused parcellations for a single subject. By filtering each individual's tractogram with the corresponding Chimera parcellations, we generated connectivity matrices (Figure 2B).

License