This code contains the method for the paper:
- Functional organization of the neonatal thalamus across development depicted by functional MRI by Hamza Kebiri, Farnaz Delavari, Dimitri Van De Ville, João Jorge* and Meritxell Bach Cuadra* (*: Equal contribution), Imaging Neuroscience 2025
1- A parcellation of the different brain ROIs and a thalamus mask must exist for each subject (i.e both in native subject space)
The parcellation in subject space can be created for e.g. with ITK-Snap by running a registration between the anatomical image (e.g. T2-w) and the corresponding average BOLD (across time)
2- Run the script run_fc.py to get the functional connectivity (FC) between each thalamic voxel and the ROIs (40 cortical and sub-cortical regions in the case of our work) for all subjects that are present in the folder path root_dir
This step is the most time consuming and takes time (~100 min) per subject in a local computer (if a parcellation has less ROIs, it will of course take proportionally less time)
3- Create a population average of the thalamus (i.e. a “new” template) in which the FCs of each subject (output of step 2) should be aligned to: (i) this new template creation can be performed by running the script tasks.py and putting avgFCtoGetPopulationFCtemplate to True, this will output both the average FC map (average_fc_map.nii.gz) and a corresponding thalamus mask (average_fc_map_mask.nii.gz) to be used in the next steps. (ii) individual FC maps should be aligned with this new template (use registration tools from ITK-Snap for example)
4- Run the script tasks.py by putting AvgSubjectsFC to True to get an average of the functional connectivity of the different subjects (put into average thalamus template from step 3)
5- Run the script generate_clusters.py to create the thalamic clusters by choosing the number of clusters - Once they are generated, they can be visualized in ITK-Snap by loading them as a segmentation map (on top of the average average_fc_map.nii.gz generated in step 3 for example).
The data used in this study are from the publicly available dataset of the Developing Human Connectome Project (dHCP)
We gratefully acknowledge the CIBM Center for Biomedical Imaging (Centre d'Imagerie BioMédicale), a Swiss research center of excellence supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), École Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE), Geneva University Hospitals (HUG), and the Leenaards and Jeantet Foundations.
This research was funded by grants from the Swiss National Science Foundation (grants 182602, 215641 and PZ00P2_185909). It was also supported by CSEM – Swiss Center for Electronics and Microtechnology, by the Translational Imaging Center (TIC) of the Swiss Institute for Translational and Entrepreneurial Medicine (SITEM). The authors are grateful to the TANGO consortium members (https://thalamicsegmentation.github.io/), Dr. Vinod Kumar and Prof. Manoj Saranathan, for the exceptional opportunities for scientific discussions which contributed toward this work.
If you find our work useful in your research, please consider citing:
@article{kebiri2025functional,
title={Functional organization of the neonatal thalamus across development depicted by functional MRI},
author={Kebiri, Hamza and Delavari, Farnaz and Van De Ville, Dimitri and Jorge, Jo{\~a}o and Cuadra, Meritxell Bach},
journal={Imaging Neuroscience},
year={2025},
publisher={MIT Press 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA~…}
}If you have any questions, please feel free to contact hamza (dot) kebiri (at) unige (dot) ch.