The mission of the cell is to meet the various needs of research teams in support of software development and brain data analysis, including:

→The development of ad hoc software tools to meet the specific needs of research teams based in or using the NeuroSpin platform.
→The development of brain data analysis pipelines based on standard tools used by the scientific community.
→The development of webservices that allow remote control of the developed pipelines and the access to the data produced.
→The deployment of these tools on the high performance computing resources of NeuroSpin (CPU and GPU clusters) and of the CEA Très Grand Centre de Calcul (via the EBRAINS/FENIX infrastructure).
→The availability of a catalogue listing all the tools produced by the cell. More specifically, within the framework of various research projects conducted by the BAOBAB unit, the cell analyses population imaging data from small, medium and large monocentric, multicentric, national, European or international cohorts acquired on healthy subjects (IMAGEN, HCP, UK BioBank, …) or patients (R-link, EUAIMS, HBN, …) in order to develop population stratification approaches and thus be able to identify the predisposition of each individual to develop brain pathologies. This work is carried out in close collaboration with the UMS CATI.

Members

Research Engineers

• Dimitri Papadopoulos - Research Engineer, PhD

• Antoine Grigis - Research Engineer, PhD, HdR

• Aymeric Gaudin - Research Engineer

• Oneeb Nasir - Engineer 2025- (under co-superv. A Gaudin, A Grigis)


Alumni

• Robin Cherbonnier - Engineer 2014-2018 (under co-superv. V Frouin, A Grigis)

• Thomas Gareau - Engineer 2014-2017 (under co-superv. V Frouin, A Grigis)

• David Goyard - Engineer 2014-2017 (under co-superv. V Frouin, A Grigis)

• Hélène Urien - Engineer 2017-2019 (under co-superv. V Frouin, A Grigis)

• Julie Victor - Engineer 2019-2022 (under co-superv. E Duchesnay, A Grigis)

• Lisa Perus - Engineer 2019-2019 (under superv. A Grigis)

• Loic Dorval - Engineer 2021-2024 (under co-superv. E Duchesnay, A Grigis)

• Bérangère Dollé - Engineer 2022-2024 (under co-superv. E Duchesnay, A Grigis)


Gallery

Revisiting the standard for modeling functional brain network activity - Application to consciousness

This paper presents a novel framework for identifying and quantifying resting-state networks using resting-state fMRI recordings. The study employs a linear latent variable model to generate spatially distinct brain networks and their associated activities. It specifically addresses the atlas selection problem, and the statistical inference and multivariate analysis of the obtained brain network activities.

Cortical brain surface and deep learning

Surfify is an open source Python module that simplifies the development of neural network architectures that relies on cortical surfaces. It provides common architectures, icosahedral mesh operators, and cortical augmentations.

A Large-Scale Multi-Site Brain MRI Data-set for Age Prediction and Debiasing

BHB aggregates 10 publicly available datasets. Currently, it is focused only on Healthy Controls (HC) since the main challenge consists in modeling the (normal) brain development by building a robust brain age predictor. BHB contains N=5330 3D T1 brain MRI scans from HC acquired on 71 different acquisition sites coming from European-American, European, and Asian individuals. BHB provides the participants phenotype as well as site and scanner information associated with each image. Some widespread confounds are also proposed, such as the Total Intracranial Volume (TIV), the CerebroSpinal Fluid Volume (CSFV), the Gray Matter Volume (GMV), and the White Matter Volume (WMV).