PhD Position F/M Pretrained models of multimodal neuroimaging for predicting individual cognition

Inria Palaiseau; Palaiseau; On-site; EUR 16,000 - 23,000; Full time

Job summary

1000scholars seeks a PhD candidate to join the MIND Inria team at CEA NeuroSpin on the Paris-Saclay campus to develop pretrained multimodal neuroimaging models with calibrated uncertainty.

The PhD will build data infrastructure for large datasets (HCP, UK Biobank), design deep generative and self-supervised models, and compare transfer learning strategies across datasets, with results shared openly through benchmarks and released code.

Qualifications

• PhD in neuroscience, computer science or related field with strong ML background.
• Experience with neuroimaging data and Python ML stack is desirable.
• Ability to collaborate with international teams and present at conferences.

Responsibilities

• Build and release open preprocessing and harmonisation pipelines for large multimodal neuroimaging databases.
• Design, train and benchmark deep generative and self-supervised models of the multimodal brain phenotype.
• Evaluate transfer learning strategies on small to medium datasets and release open benchmarks.
• Validate research results and publish at leading conferences and journals.
• Release models and code openly and collaborate with partner teams.

Skills

Python programming

Deep learning (PyTorch/JAX)

ML theory (probability / statistics)

Neuroimaging data analysis

Data pipelines

Education

PhD in Neuroscience / ML / AI

Tools

PyTorch

JAX

MRtrix

DIPY

Nilearn

Job description

Context

Within the framework of a partnership (you can choose between) public with French National Research Agency (ANR), and the Neurofunctional Imaging Group in Bordeaux, France

Is regular travel foreseen for this post ? Yes. The consortium meets in person quarterly, alternating between Saclay and Bordeaux, and the student will present the work at international conferences (NeurIPS, ICML, MICCAI, OHBM). Travel expenses are covered within the limits of the scale in force.

The student will become a member of the MIND Inria team, hosted at CEA NeuroSpin on the Paris-Saclay campus. He or she will have office space at NeuroSpin and be provided the necessary materials (workstation with GPU, access to the Inria Saclay and NeuroSpin GPU clusters, and to the national Jean Zay supercomputer) to conduct the described research. The student will work in close interaction with the GIN Bordeaux team, which brings its expertise in white matter anatomy, lesion mapping and cognitive neuroscience to the project.

Assignment

With the help of D. Wassermann the recruited person will conduct a PhD thesis on the following research: After fifty years of neuroimaging, the relationship between brain organisation and primary sensorimotor function is well characterised: replicable, lesion-validated and spatially precise. Higher-order cognition is a different matter. Working memory, executive control, spatial attention and language recruit the same distributed regions across entirely different paradigms, producing overlapping activation maps that resist segregation. Large-scale databases such as the Human Connectome Project (n ≈ 1,200) and the UK Biobank (n > 40,000) show that multimodal neuroimaging phenotypes predict composite cognitive scores with correlations approaching r = 0.5. However, the experiments with the highest cognitive specificity rarely exceed 50–200 participants, a regime in which deep learning models cannot be trained from scratch. Cognitive neuroscience needs pretrained neuroimaging models that transfer to any dataset, however small.

The MIND team and the GIN Bordeaux team are building such models within an ANR-funded collaboration. The recruited PhD student will develop the machine learning core of this effort: pretrained models of multimodal neuroimaging (functional MRI, diffusion MRI and structural MRI) that predict individual cognitive phenotypes and quantify the uncertainty of their predictions.

The thesis has three objectives. First, the student will build the data infrastructure on which the models are trained: open preprocessing and harmonisation pipelines for large multimodal databases such as the Human Connectome Project and the UK Biobank, covering functional, diffusion and structural MRI together with their cognitive batteries. Second, the student will design and benchmark deep generative and self-supervised models that learn representations of the multimodal brain phenotype from these databases and predict cognitive outcomes from them, with principled uncertainty quantification. Third, the student will study how these representations transfer to the small and medium-sized datasets that make up most of cognitive neuroscience, comparing transfer learning and domain adaptation strategies under rigorous cross-validation, and release the resulting benchmarks openly.

The central methodological challenge of the thesis is to learn representations that are both faithful to the neuroimaging signal and relevant to cognition, at the scale of tens of thousands of subjects, while keeping the uncertainty of the resulting predictions calibrated. Reaching this goal draws on the MIND team's experience in amortised variational inference (PAVI), likelihood-free inference for brain microstructure, and transfer learning of individualised functional parcellations, and on the joint work of the two teams on the geometry of brain–cognition organisation (Pacella et al., 2024).

At the end of the thesis, the student will have produced openly released pretrained multimodal models with uncertainty quantification, the open preprocessing and training code that reproduces them, and an open transfer learning benchmark with practical recommendations for deploying the models on small cognitive neuroscience datasets.

A short video presenting the overall project is available at https://lnkd.in/p/efNwKsm3.

For a better knowledge of the proposed research subject we recommend the following literature:

• Pacella, V.; Thiebaut de Schotten, M.; Wassermann, D. et al. The morphospace of the brain–cognition organisation. Nature Communications 2024, 15, 8452, DOI: 10.1038/s41467-024-52186-9.
• Rouillard, L.; Moreau, T.; Wassermann, D. PAVI: Plate-Amortised Variational Inference. Transactions on Machine Learning Research 2022, DOI: 10.48550/arXiv.2206.05111.
• Le Bris, A. et al. Improving Individual-Specific Functional Parcellation Through Transfer Learning. Preprint 2024.
• Jallais, M.; Rodrigues, P. L. C.; Gramfort, A.; Wassermann, D. Cytoarchitecture Measurements in Brain Gray Matter Using Likelihood-Free Inference. In Information Processing in Medical Imaging (IPMI), Springer, 2021, pp 191–202, DOI: 10.1007/978-3-030-78191-0_15.
• Abdallah, M.; Zanitti, G. E.; Iovene, V.; Wassermann, D. Functional Gradients in the Human Lateral Prefrontal Cortex Revealed by a Comprehensive Coordinate-Based Meta-Analysis. eLife 2022, 11, e76926, DOI: 10.7554/eLife.76926.
• Menon, V.; Gallardo, G.; Pinsk, M. A.; Nguyen, V.-D.; Li, J.-R.; Cai, W.; Wassermann, D. Microstructural Organization of Human Insula Is Linked to Its Macrofunctional Circuitry and Predicts Cognitive Control. eLife 2020, 9, e53470, DOI: 10.7554/eLife.53470.
• Ooi, L. Q. R. et al. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. NeuroImage 2022, 263, 119636, DOI: 10.1016/j.neuroimage.2022.119636.
• Ooi, L. Q. R. et al. Longer scans boost prediction and cut costs in brain-wide association studies. Nature 2025.
• Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 2016, 536, 171–178, DOI: 10.1038/nature18933.
• Tavor, I. et al. Task-free MRI predicts individual differences in brain activity during task performance. Science 2016, 352, 216–220, DOI: 10.1126/science.aad8127.
• Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 2013, 80, 62–79, DOI: 10.1016/j.neuroimage.2013.05.041.
• Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience 2016, 19, 1523–1536, DOI: 10.1038/nn.4393.
• Poldrack, R. A. Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences 2006, 10, 59–63, DOI: 10.1016/j.tics.2005.12.004.

Main activities

Main activities (5 maximum) :

• Build and release open preprocessing and harmonisation pipelines for large multimodal neuroimaging databases (fMRI, diffusion MRI, structural MRI and cognitive batteries)
• Design, train and benchmark deep generative and self-supervised models of the multimodal brain phenotype that predict cognitive outcomes with calibrated uncertainty
• Evaluate transfer learning and domain adaptation strategies on small and medium-sized cognitive neuroscience datasets and release an open benchmark suite
• Validate the advances and write scientific literature on them, targeting NeurIPS, ICML, MICCAI, OHBM and journals such as Nature Methods or NeuroImage
• Release models and code openly and interact with the GIN Bordeaux partner team

Skills

Technical skills and level required :

• Good mastery of Python programming and of at least one deep learning framework (PyTorch or JAX)
• Comfortable with mathematical formalisms and the formal background of machine learning and AI: probability, statistics, variational inference and representation learning
• Experience with neuroimaging data (fMRI, diffusion MRI) and their analysis tools (e.g. nilearn, fMRIPrep, MRtrix, DIPY) is desirable
• Experience with large-scale computing (SLURM clusters, multi-GPU training) is desirable

Languages

The candidate is expected to be able to communicate proficiently in English. French is not required.