MIND - 2023

2023Activity reportProject-TeamMIND

RNSR: 202224253W
  • Research center Inria Saclay Centre
  • In partnership with:Centre CEA-Saclay
  • Team name: Models and Inference for Neuroimaging Data
  • In collaboration with:CEA Neurospin
  • Domain:Digital Health, Biology and Earth
  • Theme:Computational Neuroscience and Medicine


Computer Science and Digital Science

  • A3.1.1. Modeling, representation
  • A3.4.1. Supervised learning
  • A3.4.2. Unsupervised learning
  • A3.4.4. Optimization and learning
  • A3.4.5. Bayesian methods
  • A3.4.6. Neural networks
  • A3.4.8. Deep learning
  • A6.2.4. Statistical methods
  • A6.2.6. Optimization
  • A9.2. Machine learning
  • A9.3. Signal analysis
  • A9.7. AI algorithmics

Other Research Topics and Application Domains

  • B1.2. Neuroscience and cognitive science
  • B1.2.1. Understanding and simulation of the brain and the nervous system
  • B1.2.2. Cognitive science
  • B1.2.3. Computational neurosciences
  • B2.6.1. Brain imaging

1 Team members, visitors, external collaborators

Research Scientists

  • Philippe Ciuciu [Team leader, CEA, Senior Researcher, HDR]
  • Chaithya Giliyar Radhkrishna [CEA, Researcher, from Nov 2023, NeuroSpin]
  • Thomas Moreau [INRIA, Researcher]
  • Bertrand Thirion [INRIA, Senior Researcher, Head of Science at Inria Saclay Ile-de-France, HDR]
  • Demian Wassermann [INRIA, Senior Researcher, HDR]

Post-Doctoral Fellows

  • Antoine Collas [INRIA, Post-Doctoral Fellow]
  • Fatemeh Ghayyem [INRIA, Post-Doctoral Fellow, from Mar 2023]
  • David Sabbagh [INRIA, Post-Doctoral Fellow, until Jan 2023]
  • Guillaume Staerman [INRIA, Post-Doctoral Fellow]
  • Matthieu Terris [INRIA, Post-Doctoral Fellow, from Apr 2023]
  • Sheng Wang [CEA, Post-Doctoral Fellow, from Jul 2023]
  • Penghzou Wu [INRIA, Post-Doctoral Fellow]
  • Houssam Zenati [INRIA, Post-Doctoral Fellow, from Nov 2023]

PhD Students

  • Cedric Allain [INRIA]
  • Zaineb Amor [CEA]
  • Pierre-Louis Barbarant [Inria, from Nov 2023]
  • Charlotte Caucheteux [FACEBOOK, until May 2023]
  • Ahmad Chamma [INRIA]
  • Thomas Chapalain [ENS PARIS-SACLAY]
  • L'Emir Omar Chehab [INRIA, until Nov 2023]
  • Pierre-Antoine Comby [UNIV PARIS SACLAY]
  • Mathieu Dagreou [INRIA]
  • Merlin Dumeur [UNIV PARIS SACLAY, until Nov 2023]
  • Theo Gnassounou [ENS PARIS-SACLAY]
  • Gabriela Pilar Gomez Jimenez [INRIA, from Oct 2023]
  • Ambroise Heurtebise [UNIV PARIS SACLAY]
  • Alexandre Le Bris [INRIA]
  • Julia Linhart [ENS PARIS-SACLAY]
  • Virginie Loison [INRIA, from Oct 2023]
  • Benoit Malezieux [INRIA, until Oct 2023]
  • Elsa Manquat [AP/HP]
  • Apolline Mellot [INRIA]
  • Raphael Meudec [INRIA]
  • Florent Michel [UNIV PARIS SACLAY]
  • Dennis Hernando NUNEZ FERNANDEZ [CEA]
  • Alexandre Pasquiou [INRIA, until Jun 2023]
  • Louis Rouillard–Odera [INRIA]
  • Nicolas Salvy [Inria, from Jul 2023 until Nov 2023]
  • Alexis Thual [CEA, until Oct 2023]
  • Gaston Zanitti [INRIA, until Mar 2023]

Technical Staff

  • Majd Abdallah [INRIA, Engineer, until Mar 2023]
  • Himanshu Aggarwal [INRIA, Engineer]
  • Liza Al-Shikhley [INRIA, Engineer, from Apr 2023]
  • Ziad Ahmed Abdelmotagaly Mohamed [INRIA, Engineer, from Mar 2023]
  • Yasmin Mzayek [INRIA, Engineer]
  • Ana Ponce Martinez [INRIA, Engineer]
  • Kumari Pooja [CEA, Engineer, until Jul 2023]

Interns and Apprentices

  • Pierre-Louis Barbarant [INRIA, Intern, from Apr 2023 until Sep 2023]
  • Alban Bourbon [INRIA, Intern, from May 2023 until Jun 2023]
  • Katia Chardon [INRIA, Intern, from May 2023 until Aug 2023]
  • Denis Fouchard [INRIA, Intern, from Oct 2023]
  • Alexandre Le Bris [INRIA, Intern, until Jan 2023]
  • Yvann Le Fay [INRIA, Intern, until Mar 2023]
  • Christopher Marouani [INRIA, Intern, from May 2023 until Jul 2023]
  • Morgane Marzulli [CEA, Intern, from May 2023 until Aug 2023]
  • Melvine Nargeot [INRIA, Apprentice, from Sep 2023]
  • Léna Oudjman [CEA, Apprentice, from Oct 2023]
  • Joseph Paillard [Roche]
  • Nicolas Salvy [INRIA, Intern, from Apr 2023 until Jul 2023]

Administrative Assistant

  • Marie Enee [INRIA]

Visiting Scientist

  • David Degras [Univ Massachusetts Boston, Associate Professor, from Sep 2023]

External Collaborators

  • Pierre Bellec [University of Montreal]
  • Guillaume Daval-Frerot [Chipiron, Paris]
  • Samuel Davenport [UNIV CALIFORNIE]
  • Elizabeth Dupre [UNIV STANFORD]
  • Denis-Alexander Engemann [Roche, Basel, HDR]
  • Guillaume Favelier [INRIA, Paris]
  • Remi Flamary [LIX, from Mar 2023, HDR]
  • Alexandre Gramfort [Ministère Economie, HDR]
  • Hubert Jacob Banville [Meta]
  • Karim Jerbi [University of Montreal]
  • Matias Palva [Aalto University, Helsinki, Finland]
  • Jade Perdereau [AP/HP, from Oct 2023]
  • Zaccharie Ramzi [CNRS, until Aug 2023]

2 Overall objectives

The Mind team, which finds its origin in the Parietal team, is uniquely equipped to impact the fields of statistical machine learning and artificial intelligence (AI) in service to the understanding of brain structure and function, in both healthy and pathological conditions.

AI with recent progress in statistical machine learning (ML) is currently aiming to revolutionize how experimental science is conducted by using data as the driver of new theoretical insights and scientific hypotheses. Supervised learning and predictive models are then used to assess predictability. We thus face challenging questions like Can cognitive operations be predicted from neural signals? or Can the use of anesthesia be a causal predictor of later cognitive decline or impairment?

To study brain structure and function, cognitive and clinical neuroscientists have access to various neuroimaging techniques. The Mind team specifically relies on non-invasive modalities, notably on one hand, magnetic resonance imaging (MRI) at ultra-high magnetic field to reach high spatial resolution and, on the other hand, electroencephalography (EEG) and magnetoencephalography (MEG), which allow the recording of electric and magnetic activity of neural populations, to follow brain activity in real time. Extracting new neuroscientific knowledge from such neuroimaging data however raises a number of methodological challenges, in particular in inverse problems, statistics and computer science. The Mindproject aims to develop the theory and software technology to study the brain from both cognitive to clinical endpoints using cutting-edge MRI (functional MRI, diffusion weighted MRI) and MEG/EEG data. To uncover the most valuable information from such data, we need to solve a large panoply of inverse problems using a hybrid approach in which machine or deep learning is used in combination with physics-informed constraints.

Once functional imaging data is collected the challenge of statistical analysis becomes apparent. Beyond the standard questions (Where, when and how can statistically significant neural activity be identified?), Mind is particularly interested in addressing driving effect or the cause of such activity in a given cortical region. Answering these basic questions with computer programs requires the development of methodologies built on the latest research on causality, knowledge bases and high-dimensional statistics.

The field of neuroscience is now embracing more open science standards and community efforts to address the referenced to as “replication crisis” as well as the growing complexity of the data analysis pipelines in neuroimaging. The Mindteam is ideally positioned to address these issues from both angles by providing reliable statistical inference schemes as well as open source software that are compliant with international standards.

The impact of Mindwill be driven by the data analysis challenges in neuroscience but also by the fundamental discoveries in neuroscience that presently inspire the development of novel AI algorithms. The Parietal team has proved in the past that this scientific positioning leads to impactful research. Hence, the newly created Mind team formed by computer scientists and statisticians with a deep understanding of the field of neuroscience, from data acquisition to clinical needs, offers a unique opportunity to expand and explore more fully uncharted territories.

3 Research program

The scientific project of Mind is organized around four core developments (machine learning for inverse problems, heterogeneous data & knowledge bases, statistics and causal inference in high dimension, and machine Learning on spatio-temporal signals).

3.1 Machine learning for inverse problems


P. Ciuciu, A. Gramfort, T. Moreau, D. Wassermann

Inverse problems are ubiquitous in observational science. This necessitates the reconstruction of a signal/image of interest, or more generally a vector of parameters, from remote observations that are possibly noisy and scarce. The link between the parameters of interest and the observations is physics, and is commonly well understood. Yet, the recovery of parameters is challenging as the problem is often ill-posed due to the ill-conditioning of the forward model. Machine learning is now more frequently used to address such problems, using likelihood-free inference (LFI) to inverse nonlinear systems, or prior learning using bi-level optimization and reinforcement learning to guide the way to collect observations.

3.1.1 From linear inverse problems to simulation based inference

Expected breakthrough: Boosts in MR image quality and reconstruction speed and in spatio-temporal resolution of M/EEG source imaging
Findings: Development of data-driven regularizing functions for inverse problems, as well as deep invertible and cost-effective network architectures amenable to solve nonlinear inverse problems on neuroscience data.

Solving an inverse problem consists in estimating the unobserved parameters at the origin of some measurements. Typical examples are image denoising or image deconvolution, where, given noisy or low resolution data, the objective is to obtain an underlying high-quality image. Inverse problems are pervasive in experimental sciences such as physics, biology or neuroscience. The common problem across these fields is that the measurements are noisy and generally incomplete.

Mathematically speaking, these inverse problem can be formulated as estimating 𝐱 from 𝐲=Γ(𝐱)+𝐛. Here, 𝐛 is an additive noise and Γ is a (generally non-injective) mapping to a lower-dimensional space. For example, in magneto- and electroenchephalography (M/EEG), Γ is a real linear mapping ΓM/ EEG :NM and 𝐛 is considered white and Gaussian, while in magnetic resonance imaging (MRI), Γ is a complex linear mapping Γ MRI :NM and 𝐛 is circular complex white Gaussian. Despite the linearity of ΓM/ EEG and Γ MRI , estimating 𝐱 is a challenging task when the measurements are incomplete, i.e., MN and the problem is ill-posed. This is often the case due to physical limitations on the measurement device (M/EEG) or the acquisition time (MRI). Moreover, the linear Fourier operator Γ MRI only reflects an ideal acquisition process and part of the acquisition artifacts (e.g. B0 inhomogeneity) can be compensated by considering nonlinear models at the cost of estimating additional parameters along with the MR image.

To tackle these inverse problems, using adequate regularization will promote the right structure for the data to be recovered. Over the last decade the members of Mind have proposed state-of-the-art models and efficient algorithms based on sparsity assumptions  81, 131, 103, 86, 132, 116, 115, 101, 106, 102. MNE is the reference software developed by the team that implements these methods for MEG/EEG data while pysap-mri proposes solvers for MR image reconstruction.

The field is now progressing with novel approaches based on deep learning by either learning the regularization from data in the context of MRI reconstruction  155, 153, or by considering nonlinear models grounded in the physics underlying the data. The team has started to explore this direction using so-called Likelihood-Free Inference (LFI) techniques built on deep invertible networks  156, 114. A particular application has been on diffusion MRI (dMRI), where we have linked the dMRI signal with physiological tissue models of grey matter tissue  114. Still in MRI but in susceptibility weighted imaging, another approach  95 has consisted in directly estimating the B0 field map from non-Cartesian k-space data to correct for off-resonance effects in non-Fourier operators Γ MRI . The Mind project will continue along this direction studying nonlinear simulators of imaging data as building blocks. A key aspect of the work proposed is to exploit knowledge on the physics of the data generation mechanisms.

3.1.2 Bi-level optimization

Expected breakthrough: Efficient algorithms to select hyper-parameters and priors for source localisation in MEG and image reconstruction in MRI/fMRI.
Findings: Bi-level optimization solvers exploiting gradients to scale with the large number of samples and hyper-parameters.

In recent years, bi-level optimization – minimizing over a parameter which is itself the solution of another optimization problem – has raised great interest in the machine learning community. Indeed, many methods in ML reduce to this bi-level framework, typically the problem of hyper-parameter optimization.

In most practical cases, hyper-parameter selection is done using cross-validation (CV), which basically consists in splitting the whole dataset in training and validation sets. The parameters of the method are computed by minimizing a loss function on the training set, and the hyper-parameters are then set by minimizing the loss function on the validation set. This approach is a bi-level optimization problem.

Other instances of such problems can be found in dictionary learning, robust training of neural networks or the use of implicit layers in deep learning. In all these applications, the model or the latent variables are learned by minimizing some loss while the parameters or the dictionary are updated by minimizing a second optimization problem depending on the outcome of the first problem. While theoretical results were produced in the early 70's 94, there are still many challenges related to bi-level optimization that need to be addressed to produce methods that are both theoretically well grounded and computationally efficient. Recently, the members of Mind have published several works related to the subject 64, 65, 76, 88. We intend to pursue this effort in the following directions.

Stochastic bi-level solvers.

Bi-level solvers require the use of the whole training set before doing an update on an outer-level problem: In this sense, they are full-batch methods  76. We propose to study stochastic methods for this task, where some improvement on the optimization can be achieved using only a few samples from the training data. Stochastic algorithms are notoriously faster than full-batch methods for large datasets, but are also generally harder to analyse from a theoretical standpoint. In addition to being fast, the proposed algorithm should come with some statistical guarantees. These solvers can have many applications, from stochastic prior learning for inverse problem to hyper-parameters tuning in general machine learning.

Neural Dictionary Learning.

Bi-level optimization framework offers a canvas to advance the state of the art in dictionary and prior learning. Indeed, dictionary learning has long been seen as a bi-level optimization problem  130. Practical algorithms are mainly based on alternate minimization and rarely account for the sub-optimality of each sub-problem. With advances in bi-level optimization and algorithm unrolling  64, we aim at providing efficient and theoretically justified dictionary learning algorithms, that will be able to leverage the technologies of differentiable programming  62, 149.

Deep Equilibrium Models.

The use of Deep learning, and in particular unrolled algorithms  110, has introduced a quantum leap in the resolution of inverse problems compared to variational approaches, specifically in terms of computing efficiency and image/signal recovery performance. However, these networks are very demanding in memory for the training, which currently limits their potential. Different methods exist to alleviate this problem both on the modeling (gradient check-pointing, reversible networks) and the implementation side (model parallelism, mixed precision), but come at the expense of larger computational cost. However, a promising research avenue, illustrated by  108, is the use of Deep Equilibrium Models. These models are defined implicitly and amount to unrolling an infinite number of iterations, thereby using much less memory. These implicit layers constitute another instance of bi-level optimization problem and we plan to work on these directions in the near future as a means to address DL image reconstruction in realistic 3D and 4D multi-coil MRI setting, both for structural and functional imaging.

3.1.3 Reinforcement learning for active k-space sampling

Expected breakthrough: New hardware compliant under-sampling patterns in MRI k-space that accelerate anatomical and functional scans while optimizing MR image quality.
Findings: Develop novel principles of active sampling in the reinforcement learning framework which optimizes a sampling policy tightly linked to the reconstructed image quality.

Current under-sampling schemes in MRI allow for shorter scan acquisition times, however at the cost of artifacts in various regions of the reconstructed MR image. These artifacts arise due to uncertainties in some heavily under-sampled regions of the acquired Fourier space (i.e. also called k-space). Modern reconstruction algorithms, with the use of strong priors, either hand-crafted or learned, tend to reduce these uncertainties and behave as if the acquisition is fixed.

To go beyond the state of the art, we argue that there is a need to jointly learn an algorithm that designs the optimal under-sampling pattern in k-space as well as the reconstruction network.

As it can be summarized to learning a sequential decision algorithm, we will rely on reinforcement learning (RL) to build up optimal k-space sampling patterns while enforcing physical constraints on the MRI sequence, as originally proposed in  84, 79, 123.

The k-space acquisition can be modeled by a sampling policy and the rewards for the joint network are based on reconstructed image quality. Under this paradigm, after every fixed scan time, an instantaneous reconstruction can be obtained and the Fourier space uncertainty maps analysed in depth. Based on this, the scan can continue by actively sampling the k-space and enforcing denser samples in regions where uncertainty is larger. In this way, the learned k-space trajectories may become more patient and organ specific. Further, the trajectory can run and lead to instantaneous best results of reconstruction under a given variable scan time budget. These aspects define one of the core directions we will investigate to produce the next generation of state-of-the-art MR data sampling and image reconstruction algorithms. Recent contributions  170, 150 only approach the problem in the Cartesian framework and hence perform 1D variable density sampling along the phase encoding dimension. Given our expertise on non-Cartesian sampling in developing SPARKLING for both for 2D and 3D MR imaging  123, 124, 82, we plan to extend this framework to non-Cartesian acquisition setups while still remaining compatible with hardware constraints on the gradient system. The access to various MRI scanners at CEA/NeuroSpin is necessary and an added advantage to the success of the Mind team.

3.2 Heterogeneous Data & Knowledge Bases


B. Thirion, D. Wassermann

Inferring the relationship between the physiological bases of the human brain and its cognitive functions requires articulating different datasets in terms of their semantics and representation. Examples of these are spatio-temporal brain images, tabular datasets, structured knowledge represented as ontologies, and probabilistic datasets. Developing a formalism that can integrate all these modalities requires constructing a framework able to represent and efficiently perform computations on high-dimensional datasets as well as to combine hybrid data representations in deterministic and probabilistic settings. We will take on two main angles to achieve this task: on one hand, the automated inference of cross-dataset features, or coordinated representations and on the other hand, the use of probabilistic logic for knowledge representation and inference. The probabilistic knowledge representation part is now well advanced with the Neurolang project. It is yet a long-term endeavor. The learning of coordinated representations is less advanced.

3.2.1 Learning coordinated representations

Expected breakthrough: Process semantic information together with image data to bridge large-scale resources and knowledge bases
Findings: Set up a learning model that leverages heterogeneous data: Images, annotations, texts, and coordinate tables.

Inference is the pathway that leads from data to knowledge. One crucial aspect is that in the context of neuroscience, data comes in different forms: Full texts, images and tables. Annotations may be full texts or simply tags associated with observed images. One challenge is thus to develop automated techniques that learn coordinated representations across such heterogeneous data sources.

This learning endeavor rests on several key machine learning techniques: Compression, embeddings, and multi-layer networks. Compression (sketching) consists in building a reduced representation of some input that leads from large sparse and complex representation to low-dimension ones, while minimizing some distortion criterion. Embedding techniques also create representations, but possibly bias them to enhance some aspects of the data. It thus incorporates prior information on data distribution or the relevance of features. Finally, multi-layer networks create intermediate representation of data that are suitable to achieve a prediction goal. Such representations are rich enough in particular in multi-task settings, where the outputs of the network are multi-dimensional. Following 71, we call such latent data models coordinated representations.

Deep learning is well suited to the goal of learning intermediate representations. As an example, we plan to develop a framework that coalesces in one deep learning formulation, the task of estimating brain structures, cognitive concepts, and their relationships.

Brain structures and cognitive concepts will appear as intermediate representations responsible for linking brain activity to observed behavior. However deep learning cannot be considered as a standard means to understand coordinated representations, due to the limited data available, their poor signal-to-noise ratio (SNR) and their heterogeneity. Deep learning needs instead to be adapted by injecting our expertise on statistical structure of the data (see e.g. 112, 134). Since the challenge is to train such models on limited and noisy data, we will extend our recent work  69 that has developed regularization schemes for deep-learning models: it relies on structured stochastic regularizations (a.k.a. structured dropout). Such approaches are efficient, powerful and can be used in wide settings. We will enhance them with more generic, cross-layer, grouping schemes. Additionally, we will develop two strategies: i) aggregation of predictors for variance reduction and stability of the model  112 and ii) data augmentation – i.e. learning to augment, based on unlabeled data – to improve the fit with limited data. For this we will consider plausible generative mechanisms.

3.2.2 Probabilistic Knowledge Representation

Expected breakthrough: A domain-specific language (DSL) capable of articulating heterogeneous probabilistic data sources in neuroimaging is a way to relate physiology to cognition.
Findings: Self-optimizing probabilistic solvers for discrete and continuous hierarchical models able to scale for neuroimaging problems.

Neuroscientific data used to infer the relationships between physiology of the human brain and its cognitive function goes well beyond text, image, and tables. Knowledge graphs representing human knowledge, and the ability to encode reasoning strategies in neuroscience are also key to effectively bridge current data-centric approaches and decades-old domain knowledge. A main challenge in performing inferences combining demographic data-centric approaches, imaging measurements, and domain knowledge, is to be able to infer new knowledge soundly and efficiently taking into account the noisy nature of demographic and imaging measurements, and the common open-world assumption of ontologies and knowledge graphs. Such probabilistic hybrid logic approaches are known to be, in general, intractable in the deterministic  67 as well as in the probabilistic case  167. Nonetheless, there is an opportunity to be seized in identifying tractable segments of probabilistic hybrid logic representations able to solve open neuroscientific questions.

A noticeable opportunity to incorporate all statistical evidence gathered from noisy data into a usable knowledge base is to formalize the inferred relationships into probabilistic symbolic representations  113. These representations are much better suited to simultaneously handle data across topologies and logic systems, implementing inferential algorithms avoiding the brittleness of deterministic logic as well as causal probabilistic reasoning.

A typical application of such heterogeneous data processing is meta-analytic applications which combine neuroimaging data with results found in the scientific literature. Current tools to perform this task are NeuroSynth or Neuroquery (developed by the team). However, knowledge inferred by such tools is tremendously limited by the expressive power of the language used to query the data. Current meta-analytic tools are able to express queries relating test makers, article annotations, and their relationship with reported brain activations, support propositional logic only. Propositional logic requires the user to explicitly express every desired term with their characteristics and their relationships. Our goal is to extend the inference capabilities of such applications by leveraging current advances in probabilistic logic languages and embedding them in the Neurolang language. Neurolang enables the encoding of complex knowledge in terms of more expressive queries. Neurolang queries first-order logic segment, FO¬, with a tractable probabilistic extension allowing for high-dimensional and large dataset computations. Such segment of first order logic enables formalising questions such as “what brain areas are most likely reported active in a study specifically when terms related to consciousness are mentioned in such study”, hence being able to infer, amongst other tasks, specificity and causality  168 of diverse neuroscience phenomena. To disseminate our results allowing complex expressive searches of massively aggregated diverse data, we will leverage Neurolang. The latter produces a domain-specific language (DSL) for human neuroscience research, while being able to combine imaging data, anatomical descriptions and ontologies. Three main characteristics of the DSL are key to fulfilling this goal: First, it represents neuroimaging-derived information and spatial relationships in a syntax close to natural language used by neuroscientists  169. Second, through a back-end belonging to the Datalog± family, it allows querying ontologies with the same expressive power as current standards SPARQL and OWL  73. Finally, we will extend Neurolang to a probabilistic language able to express graphical models allowing the implementation of a wide variety of causal inference and machine learning algorithms  70 in high-dimensional settings which are specific to neuroimaging research. In sum, by leveraging recent advances in deductive database systems  73 and this novel DSL  169 we will provide a more flexible tool to express and infer knowledge on brain structure-function relationships.

3.3 Statistics and causal inference in high dimension


A. Gramfort, T. Moreau, B. Thirion, D. Wassermann

Statistics is the natural pathway from data to knowledge. Using statistics on brain imaging data involves dealing with high-dimensional data that can induce intensive computation and low statistical power. Besides, statistical models on large-scale data also need to take potential confounding effects and heterogeneity into account. To address these questions the Mind team will employ causal modeling and post-selection inference. Conditional and post-hoc inference are rather short-term perspectives, while the potential of causal inference stands as a longer-term endeavor.

3.3.1 Conditional inference in high dimension

Expected breakthrough: Obtain statistical guarantees on the parameters of very-high dimensional generalized linear or non-parametric models.
Findings: Develop computationally efficient procedures that allow inference for such models, by leveraging structural priors on the solutions.

Conditional inference consists of assessing the importance of a certain feature in a predictive model, while taking into account the information carried by alternative features. One motivation for using this inference scheme is that brain regions that sustain behavior and cognition are strongly interacting. Taking these interactions into account is critical to avoid confusing correlation with causation in brain/behavior analysis.

Technical difficulties come when the set of explanatory features 𝐗 becomes extremely large as frequently met in neuroimaging: Conditioning on many variables (or equivalently, high dimensional variables) is computationally costly and statistically inefficient. The main solutions to date are based either on linear model debiasing  118, as well as simulation-based approaches (knockoff inference  80 or conditional randomization tests  127). Importantly the latter involves simulating data with statistical characteristics described explicitly (in a parametric family) or implicitly (by samples). There remain two gaps to bridge for these methods: i) The computational gap, as the algorithmic complexity of these approaches is typically cubic in the number of samples, unless more efficient generative mechanisms are available; ii) the power gap, related to the limited number of available samples. The best solution thus far consists of associating these inference procedures with dimension reduction procedures  141. The next step is adaptation to more general settings: Conditional inference has been formulated in the linear framework, where it boils down to controlling that the corresponding coefficient is non-zero, hence it has to be generalized to nonlinear models: Non-parametric models like random forests, then possibly deep networks.

3.3.2 Post-selection inference on image data

Expected breakthrough: Statistical control of false discovery proportion (FDP) for data under arbitrary correlation structure.
Findings: A computationally efficient non-parametric statistical test procedure, and a benchmark against alternative techniques.

Large-scale statistical testing is pervasive in many scientific fields, where high-dimensional datasets are collected and compared with an outcome of interest. In such high-dimensional contexts, false discovery rate (FDR) control  75 is attractive because it yields reasonable power, while providing an explicit and interpretable control on false positives. Yet the FDR rate is the expectation of the FDP. Controlling the FDR does not mean that the FDP is controlled, a distinction that is most often ignored by practitioners. For the sake of scientific reproducibility, there is a need for methods controlling the FDP.

Such an approach has been developed in the context of neuroimaging, namely the all-resolution inference framework  157 based on classical multiple correction error control bounds. Yet, the empirical behavior of this method remains to be assessed. Moreover, it has been clearly established that the procedure is over-conservative in some settings 77. Indeed, it relies on the Simes statistical bound, that is not adaptive to the specific type of dependence for a particular data set. To bypass these limitations,  77 have proposed a randomization-based procedure known as λ-calibration, which yields tighter mathematical bounds that are adapted to the dependency observed in the dataset at hand. It rests on a non-parametric (permutation-based) estimation of the null distribution, leading to tight and valid inference under general assumptions.

In this research axis, we propose to fix some of the open issues with the approach described in  77, namely the choice of a template family to calibrate the error distribution in the permutation procedure. We hope to propose a practical choice for this family to avoid putting the burden of choice on practitioners.

We will characterize by simulations and theoretical arguments the behavior of these error control procedures and develop efficient computational methods for the use of these tools in brain imaging analysis.

3.3.3 Causal inference for population analysis

Expected breakthrough: Provide a reference methodology for causal and mediation analysis in high-dimensional settings.
Findings: Benchmark state-of-the-art techniques and further adapt them to the high-dimensional setting.

Modern health datasets present population characteristics with many variables and in multiple modalities. They can ground prediction and understanding of individual outcomes, using machine learning techniques. Still, heterogeneous variables have complex relationships, making it hard to tease apart each factor in an outcome of interest. Potential outcome theory  161 provides a valuable framework to evaluate the impact of treatment (interventions). Treatment effects can be heterogeneous. In particular, interactions between background and treatment variables have to be considered.

The statistical behavior (consistency and efficiency) under non-parametric models is actively investigated 68, 143. However, their behavior in high-dimensional settings, when both the number of features and the number of samples are large, is still poorly understood. Our objective is thus to extend the theory and algorithms of causal inference to noisy high-dimensional settings, where the noise level implies that effects sizes are proportionally small, and classic methods often become inefficient and potentially inaccurate due to overfitting. More specifically, we plan to explore the following directions.

Mediation analysis and conditional independence

Mediation analysis considers the question of whether a variable z mediates all the effect of another variable x onto a target variable y, a.k.a. outcome. It turns out that full-mediation analysis amounts to testing whether xy|z (x is independent from y given z), which is handled by a conditional independence test. When the dimensions of these variables (z in particular, but also x and to some extent y) grow, the underlying statistical inference procedures typically lose power, or even possibly error control. We propose to leverage our experience on such high-dimensional inference problems  89, 142 to set up computationally efficient and accurate solutions to this problem.

Latent variable models and confounders

The most important aspect of inferring causal effects from observational data is the handling of confounders, i.e., factors that affect both an intervention and its outcome. For instance, age has a clear impact on brain characteristics as well as on behavior, potentially biasing brain/behavior statistical associations. A carefully designed observational study attempts to measure all important confounders. When one does not have direct access to all confounders, there may exist noisy and uncertain measurements of proxies for confounders. A possible solution to this problem relies on generative modeling, such as Variational Autencoders (VAE) and Generative Adversarial Networks (GANs), to sample the unknown latent space summarizing the confounders on datasets with incomplete information; the seminal work of  129 is promising, but still requires improvements to become usable in realistic settings.

The quest of model selection and validation

In the classical potential outcome theory  161, causal effects are determined by both factual and counterfactual outcomes, ground-truth effects can never be measured in an observational study. In the absence of such measures, how can we evaluate the performance of causal inference methods? Addressing this question is an important step for practical problems, in which one has to determine if an effect can safely be considered non-zero, or heterogeneous through a population. We propose to revisit the promising work of  66 analysing in detail the shortcomings of the procedure (regarding both bias and variance), especially when the model becomes high-dimensional.

3.4 Machine Learning on spatio-temporal signals


P. Ciuciu, A. Gramfort, T. Moreau, D. Wassermann, B.Thirion

The brain is a dynamic system. A core task in neuroscience is to extract the temporal structures in the recorded signals as a means to linking them to cognitive processes or to specific neurological conditions. This calls for machine learning methods that are designed to handle multivariate signals, possibly mapped to some spatial coordinate system (e.g. like in fMRI).

3.4.1 Injecting structural priors with Physics-informed data augmentation

Expected breakthrough: Obtain models with more predictive power when trained on small datasets.
Findings: Efficient data-augmentation strategy tailored to brain signals.

Data augmentation consists of virtually increasing dataset size during learning by applying random, yet plausible, transformations to the input data. In computer vision, this means altering data by applying symmetries, rotations, geometric deformations etc. While such strategies are reasonable for natural or medical images  148, it is still unclear how neural or BOLD signals can be augmented in order to improve prediction performance and robustness.

Some purely data driven strategies have been proposed to augment EEG data using spectral transforms  128 or advanced strategies such as channel, time or frequency masking or phase randomizations  122, 125. Although dozens of transformations have been considered in the literature to augment EEG signals, it is now apparent that different augmentation strategies should be applied to the data as a function of the prediction task to be handled. For example when considering sleep stage classification or BCI applications, the spatial sampling of electrodes and the duration of signals varies considerably, with the consequence being that different augmentation parameters and even transformations need to be employed.

In this line of work we will develop algorithms that can quickly identify the relevant augmentation techniques, building for example on  92, 127. The aim is to provide a system that can automatically learn invariance within a class and across subjects in order to maximize the prediction performance on unseen data. The methodology developed will be relevant beyond neuroscience as long as a family of physics-informed transformations is available for prediction tasks at hand.

3.4.2 Learning structural priors with self-supervised learning

Expected breakthrough: Unveiling the latent structure of brain signals from large datasets without human supervision as well as improving the prediction performance when learning from limited data.
Findings: Self-supervised algorithms for multivariate brain signals.

Self-supervised learning (SSL) is a recently developed area of research that provides a compelling approach for exploiting large unlabeled datasets. With SSL, the structure of the data is used to turn an unsupervised learning problem into a supervised one, called a “pretext task”, such as solving Jigsaw puzzles from images  145 or learning how to color gray-scaled images. The representation learned on the pretext task can then be reused for unsupervised data exploration or on a supervised downstream task, with the potential to greatly reduce the number of labeled examples required to train a good predictive model.

In fields like computer vision  145, 136 and time series processing  146, SSL has shown great promise in terms of prediction performance but also in ease of use. Indeed, SSL simplifies model selection and evaluation as it relies on prediction scores and cross-validation, contrarily to unsupervised learning methods like ICA  63.

Recently the team has applied SSL to two large cohorts of clinical EEG data  72 revealing insights on the data without any human supervision. However many challenges remain. For example in Mind, we aim to explore novel SSL strategies applicable to electrophysiology as well as to haemodynamic signals measured with fMRI. As such, our goal is to expand the recent multivariate method we have introduced in the field for the blind deconvolution of BOLD signals in both task-related and resting-state experiments  87.

While rather small networks have been employed so far on EEG data  83, 160 due to limited sets of annotations, the use of SSL tasks opens the possibility to work with much larger labeled datasets, and therefore many more overparametrized models. We aim to explore these directions, hoping to reach a state where pre-trained models could be available for EEG or MEG signals as is presently the case for images or for natural language processing (NLP) tasks.

3.4.3 Revealing spatio-temporal structures with convolutional sparse coding and driven point processes

Expected breakthrough: A novel way to study and quantify temporal dependencies between neural processes, going beyond connectomes based on spectral analysis.
Findings: Temporal pattern finding algorithms that scale to massive MEG/EEG datasets with parallel processing and point-process inference algorithms.

The convolutional sparse linear model is one established unsupervised learning framework designed for signals. Using algorithms known as convolutional sparse coding (CSC), this framework allows for the learning of shift-invariant patterns to sparsely reconstruct a time series. These patterns, also called atoms, correspond to recurrent structures present in the data. While some of our recent advances have improved the computational tractability of these methods  138, 137 and adapted them to neurophysiological data 117, 100, 87, there are still many shortcomings that make them unpractical for applications beyond denoising.

Model validation

The main challenge for the evaluation of unsupervised convolutional models comes from current theoretical limitations: What can we guarantee statistically concerning the recovered atoms? Due to their non-convexity, existing algorithms can only guarantee convergence to local minima, which might be sub-optimal. In this setting, it is challenging to quantify if the model parameters are well estimated and if they are actually representative of the signals. In Mind, we aim to develop statistical quantification of the uncertainty associated with such models and in this regard, provide objective selection criteria for the model and its parameters. This topic of research will benefit from our other developments on bi-level optimization (cf. ssub:bilevel) and on FDR control (cf. ssub:postselection) as well as the expertise of the team members on dictionary learning  138, 135, 137.

Capturing temporal dependencies with point processes

Another shortcoming of these models is that they do not capture temporal dependencies between the occurrences of the different atoms. However, neural activity at level of the whole brain is highly distributed. Different brain regions form networks that are characterized by the presence of statistical dependencies in their activity  147. An interesting question to formulate is how one can model and learn these time dependencies between brain areas from the MEG or EEG recordings using an unsupervised event-based approach such as CSC. One of the approaches considered is based on point processes (PP; 78, 111). PP are classical tools to study event trains (e.g. sequence of spikes) and to model their dependency structure. We aim here to develop PP-based inference algorithms as a means to capture network effects in different brain areas, but also to quantify how experimental stimuli are affecting the temporal statistics of temporal patterns  147. To model this latter scenario, we will develop the so-called driven PP. In a second stage, we aim to design fully unsupervised methods to capture the connections between different brain areas leveraging the full temporal resolution of non-invasive electrophysiological signals.

4 Application domains

The four research axes we presented earlier have been thought of in tight interaction with four main applications (large-scale predictive modeling, mapping cognition & brain networks, modeling clinical endpoints, from brain images and bio-signals to quantitative biology and physics).

4.1 Population modeling, large-scale predictive modeling

4.1.1 Unveiling Cognition Through Population Modeling

Linking the human brain's structure and function with cognitive abilities has been a research epicenter for the past 40 years. The sophistication of brain mapping machinery such as MRI, EEG and MEG, has produced a treasure trove of data. Nonetheless, the effect size of the phenomena leading to understanding cognition is often drowned out by noise and inter-individual variability. A main goal of Mind is to simultaneously harness the power of large-scale general purpose datasets, such as the Human Connectome Project (HCP) and the Adolescent Brain Cognitive Development Study (ABCD), as well as small scale high precision ones, such as the Individual Brain Charting (IBC) dataset  152, to understand the link between the human brain's architecture and function, and cognition. Parietal's expertise has already been demonstrated in this field. Examples of this include using diffusion MRI (dMRI) to link the brain's macrostructure with language comprehension  85, tissue microstructure with cognitive control  133, functional gradients on the cortical surface 98 to functional territory segregation 151.

Mind project will continue this task by seizing our core methodological developments, described in the previous section, and our global collaborative network of cognitive scientists.

4.1.2 Imaging for health in the general population

Individual differences in brain function and cognition have historically been investigated by studies carried out by individual laboratories having access mainly to small sample sizes. The growing availability of public large-scale data of epidemiological dimensions curated by dedicated consortia (e.g. UK Biobank) has enabled studying the relationship between cognition and the brain with unparalleled granularity and statistical power. These resources now allow researchers to relate brain signals/images to rich descriptions of the participants including behavioral and clinical assessments in addition to social and lifestyle factors. Machine learning has proven essential when modeling biomedical outcomes from the large-scale and high-dimensional data brought by consortia and biobanks. It is used to to build predictive models of heterogenous biomedial outcomes (cognitive, social, clinical) based on different neuroscientific modalities. Taken together, this facilitates the study of lifestyle and health-related behavior in the general population, potentially revealing risk factors leading to biomarker discovery.

Mind will greatly contribute to this effort by focusing on population modeling as a tool for enhancing the analysis of clinical data and mental health.

4.1.3 Proxy measures of brain health

Clinical datasets tend to be small as sharing of data is not incentivized or institutional and economic resources are missing. As a consequence, the capacity of machine learning to learn functions that relate complex-to-grasp biomedical outcomes to heterogeneous data cannot be fully exploited. This has stimulated growing interest in proxy measures of neurological conditions derived from the general population, such as individual biological aging. One counter-intuitive aspect of the methodology is that measures of biological aging (e.g. via brain imaging) can be obtained by focusing on the age of a person, which is known in advance and is, in itself not interesting as a target. However, by predicting the age, machine-learning can capture the relevant information about aging. Based on a population of brain images, it extracts the best guess for the age of a person, indirectly positioning that person within the population. Individual-specific prediction errors therefore reflect deviations from what is statistically expected  166. The brain of a person can look similar to brains commonly seen in older (or younger) people. The resulting brain-predicted age reflects physical and cognitive impairment in adults  165, 90, 99 and reveals neurodegenerative processes  126, 109, which could be overlooked without using machine learning.

Mind will extend this line of research in two directions: 1) Assessment of brain age using EEG and non-brain data such as health-records and 2) proxy measures of mental health beyond aging.

4.1.4 Studying brain age using electrophysiology

MRI is not yet available in all clinical situations and certain aspects of brain function are better understood using electrophysiological modalities (M/EEG). Until recently, it was unclear if brain age can be meaningfully estimated from M/EEG. In a recent study  104, we demonstrated, using the Cam-CAN cohort (n=650), that combining MRI and MEG enhanced detection of cognitive dysfunction. The proposed approach not only achieved integration of brain signals from distinct modalities but explicitly handled the absence of MEG or MRI recordings, adapting ideas from  119. This is key for clinical translation where one cannot afford excluding cases because one modality is missing. In the clinical setting, EEG is predominantly used (and not MEG). Clinical recordings are far noisier than lab EEG and gold-standard source modeling with MRI is rarely done outside the lab. Supported by theoretical analysis and simulations, we found through empirical benchmarks  163 that Riemannian embeddings 1) capture individual head geometry 2) bring robustness to extreme noise and, 3) enable good age prediction from clinical 20-channel EEG (n=1300) with performance close to 306-channel lab MEG.

Mind will extend this line of research by translating EEG-based brain age measures into the hospital setting and probe these in different patient populations in which ageing-related differences in brain structure and function are part of the clinical picture, e.g., neurodevelopmental disorders, postoperative cognitive decline and dementia (cf. MIND:subsec:MCE).

4.1.5 Proxy measures of mental health beyond brain aging

Quantitative measures of mental health remain challenging despite substantial research efforts 120. Mental health, can only be probed indirectly through psychological constructs, e.g. intelligence or anxiety gauged by valid and statistically relevant questionnaires or structured examinations by a specialist. In practice, full neuropsychological evaluation is not an automated process but relies on expert judgment to confront multiple responses and interpret them in the context of a larger environmental context including the cultural background of the participant. Inspired by brain age, we set out to build empirical measures of mental health  93 by predicting traditional and broadly used psychological constructs such as fluid intelligence or neuroticism in the UK Biobank. Our results have shown that all proxies captured the target constructs and were more useful than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). In the long run, we anticipate that using proxies could complement psychometric assessments by corroborating data and potentially providing more accurate data faster and more efficiently for clinical populations.

Mind will expand this line of research by systematically searching for proxy measures of physical and mental health derived from large clinical population using electronic health records or transcripts from clinical interviews. We will propose a systematic causal analysis (treatment effect size and mediation) to provide a clearer understanding of the relationships between the many variables that characterize mental health. We will study more in detail the impact of general health markers on brain status, as this may well fit much of the unexplained variance on brain health.

4.2 Mapping cognition & brain networks

4.2.1 Problem statement

Cognitive science and psychiatry aim at describing mental operations: cognition, emotion, perception and their dysfunction. As an investigation device, they use functional brain imaging, that provides a unique window to bridge these mental concepts to the brain, neural firing and wiring. Yet aggregating results from experiments probing brain activity into a consistent description faces the roadblock that cognitive concepts and brain pathologies are ill-defined. Separation between them is often blurry. In addition, these concepts (a.k.a. psychological constructs) may not correspond to actual brain structures or systems. To tackle this challenge, we propose to leverage rapidly increasing data sources: text and brain locations described in neuroscientific publications, brain images and their annotations taken from public data repositories, and several reference datasets.

4.2.2 What machine learning can do for neuroscience

Recent works in computer vision  96 or natural language processing 91, 97 have tackled predictions on a large number of classes, getting closer to open-ended knowledge. These approaches, that rely on uncovering some form of relational structure across these classes, in effect capture the semantics of the domain  91, including the similarity structure of the relevant classes and the ambiguities across classes or the multiple aspects of a class. Broadly speaking, these contributions converge to the concept of representation learning 74, i.e. estimating latent factors that reformulate a learning problem into a new set of input features or output classes that are more natural for the data and help further analysis. These new tools enable extraction of knowledge, for instance ontology induction, with statistical learning 144. They are at the root of heterogeneous data integration, such as multi-modal machine learning 71. The machine learning challenges that we aim to tackle are three-fold:

  • Existing multi-modal machine learning techniques have been developed for relatively abundant data, with overall high SNR: text, natural images, videos, sound. These data are most often non-ambiguous, while brain data typically are, due to the low SNR per image and, more crucially, poor annotation quality. We propose to tackle this by adapting machine learning solutions to this low-SNR regime: introduction of priors, aggressive dimension reduction, aggregation approaches and data augmentation to reduce overfitting.
  • Leveraging implicit supervisory signals: While data sources contain lots of implicit information that could be used as targets in supervised learning, there is most often no obvious way to extract it. We propose to tackle this by using additional, ill- or not-annotated data, relying on self-supervision methods.
  • Model interpretability: Our goal is to provide clear assertions on the relationships between brain structures and cognition: the inference should always lead to an updated knowledge base, i.e. updated relationships between concepts pertaining to neuroscience on one hand, psychology on the other hand. Specifically, one should be able to reason about the information extracted within Mind. For this, we will develop dedicated statistical, causal and formal (ontology-based) data analysis schemes.

Associating knowledge engineering with statistical learning to boost cognitive neuroimaging, requires tackling the challenge of multimodal machine learning under noisy conditions with limited data. Doing so, it will capture links between behavior and brain activity, and enable aggregating the information carried by neuroimaging data to redefine and link concepts in psychology and psychiatry.

4.2.3 Perspective taken: combine distributional semantics with brain images

In natural language processing (NLP), distributional semantics capture meanings of words using similarities in the way they appear in their environment. We want to adapt these ideas to learn data-driven organizations of psychological concepts. Importantly, applying these techniques solely to the psychology literature merely captures the current status quo of the field. Including brain images is necessary to bring new information.

To link observed cognition to brain activity, two typical statistical learning problems arise: encoding, that seeks to describe brain activity from behavior; and decoding, that seeks the converse, predicting behavior from brain activity  121. In addition, statistical modeling of each aspect of the data on its own generates knowledge, typically spatial decompositions from resting-state data, and topic modeling on descriptions of behavior. The research strategy followed in this proposal is to combine the different statistical learning problems in a unified framework to extract core structures from the aggregation of neuroimaging data: on one side brain structures, and on the other side semantic relationships and concepts in psychological sciences.

Mind will in particular publish automated functional meta-analyses to give a systematic assessment of the publicly available data and question the limitations of the current conceptual framework of systems neuroscience as well as of these resources.

4.3 Modeling clinical endpoints

When sufficient data is available, machine learning can be employed to directly model various clinical endpoints (such as diagnosis, drug response, and neuropsychological scores) from brain signals without the need for proxy measures. This approach has the potential to significantly and meaningfully simplify statistical modeling in clinical research. Machine learning facilitates combining heterogeneous input data (different modalities) and does not need high confidence in underlying generative models linking the data to the clinical endpoint. As a consequence, the same class of models can be applied regardless of the endpoint. Its focus is on bounding the approximation error of the endpoint instead of correct parameter estimates. As such, it provides generalizing models that are more robust. Our team has pushed this type of research program through several important collaborations with our European clinical partners using EEG and MRI.

4.3.1 EEG-based modeling of clinical endpoints

Neurological and psychiatric disorders can show complex neurological patterns. Diagnosis is often performed clinically (based on cerebral signs and behavioral symptoms), leading to important variability across doctors. In clinical neuroscience, predicting diagnosis from brain signals is therefore a common application. In the clinical context, EEG is an economically viable option that can be applied in a wide array of circumstances. In collaboration with the Salpêtrière Hospital and the Paris Brain Institute (ICM) we have developed and validated an approach for an EEG-based modeling of diagnosis for severely brain injured patients suffering from consciousness disorders (DoC)  105. Expert-defined features from consciousness studies were rigorously combined using random forest classification. Sensitivity analysis and benchmarks showed robustness across EEG-configurations (channels, time points), protocols (resting state vs evoked responses), label noise and differences between recording sites. When changes in the signal are more subtle than they are in DoC patients (average power turned out to be one of the strongest stand-alone features) more general approaches are needed.

Our future activities will focus on extending this line of research to other clinical populations and other endpoints. We have started a collaboration with the Institut Pasteur (GHFC team, T Bourgeron, R Delorme) and the University of Montreal (PPSP team, G Dumas), to characterize differences between normally developing children and children diagnosed with autism spectrum disorders. A wide array of EEG tasks will be used and endpoints (i.e. developmental timepoints) will go beyond the usually accurate diagnosis, focusing on symptom severity and social developmental scores. With the anesthesiology department at the Lariboisière hospital (A Mebazaa, E Gayat, F Vallée) and the cognitive neurology unit (C Paquet) we aim at developing EEG-based models of cognitive decline and dysfunction in two different settings. Postoperative cognitive decline is an important complication after general anesthesia and its antecedents must be better understood. As this might be an indicator for a latent neurodegenerative condition, we plan to use our EEG-based models of both Alzheimer's Disease and Lewy body dementia in which disease progression is an important change over time.

This widening scope calls for a more general methodology as compared to our previous work on DoC. For example, in these conditions involving neurodegenerative problems, we have observed that both subtle and condition-specific spatial patterns matter more than strong and global amplitude changes. To approach these challenges we will draw on our latest M/EEG-methods that were recently developed for population-level modeling of brain health and brain aging  104. We found that frequency band-specific spatial patterns of M/EEG power spectra conveyed important information of cognitive function (memory and cognitive performance) that were not explained by MRI or fMRI. This was implemented by predicting from a filter-bank of frequency-band-specific source power and source connectivity features. Core challenges to enable clinical translation include lower SNR and absence of individual anatomical MRI scans needed for gold-standard source modeling. Through theoretical analysis, simulations and benchmarks we found  163, 162 that, in M/EEG sensor space, covariance matrices in combination with spatial filtering techniques and Riemannian embeddings provide good workarounds for absent anatomical MRI scans. This covariance-based approach allows to capture fine-grained spatial information related to power and connectivity without performing biophysics-based source localization. Moreover, Riemannian embeddings make predictive modeling from M/EEG covariance matrices more robust to noise, whereas their interpretability is more challenging than that of spatial filters, indicating a direction for further research. Another challenge is given by the limited numbers of labeled samples for supervised learning and EEG-devices with small channel numbers, such as monitoring or user-grade EEG with 2-4 electrodes for which random loss of electrodes can be frequent. In this context, we expect important enhancements from self-supervised learning approaches  72 and deep learning methods for data-augmentation for which we have obtained the first results on non-clinical data. In these settings, the previous elements from classical approaches such as Riemannian geometry or spatial filtering can be readily implemented alongside more involved computations and transformations.

4.3.2 MRI-based modeling of clinical endpoints

Image based biomarkers can be objectively measured and are a sign of normal or abnormal processes, of a condition or disease. Incorporating new potential imaging biomarkers requires several steps, often in parallel and complementary to each other, to be undertaken for translation into clinical practice. These can be divided into the following phases after identification: Development and evaluation, validation, implementation, qualification, and utilization. Our team aims to cross two main translational gaps, that is, the translation from patients first and then to practice. Our aim through our current and active projects is to ensure that potential biomarkers, like the clear delineation of subterritories of the subthalamic nucleus (STN) in pharmaco-resistant Parkinson's disease (PD) patients (i.e.candidates for implantation of a deep brain stimulator) are `fit for purpose' and associated with the clinical endpoint of interest with the overarching goal being to demonstrated efficacy and health impact. This process is key to the translation into clinical practice and widespread utilization.

Through the ANR VLFMRI grant we aim to derive new MR imaging-based biomarkers related to prematurity and abnormal neurodevelopment of hospitalized neonates at low magnetic field (20 mTesla). In this setup, the objective is to perform an almost continuous monitoring to detect early signs of adverse events including ischemic stroke or encephalopathy (collaboration with Prof. V. Biran, APHP Robert Debré Hospital). An additional collaboration is already underway with the AP-HP Henri Mondor Hospital (neuroradiologist Dr B. Bapst, doing part of her PhD at NeuroSpin), to achieve high-resolution susceptibility weighted imaging (600 µisotropic) in a scan time of 2m30s for an accurate delineation of the STN in PD patients prior to surgical planning. A database of 123 patients has already been collected using both the standard SWI imaging protocol and ours based on the SPARKLING technology. This annotated database will be key to compare the diagnosis power of our solution with that of the current care, analyse to what extent a higher image resolution is instrumental in providing a more accurate clinical diagnostic, and finally make our protocol more widely accepted in the clinical practice.

Our key contribution in these projects is to translate to the clinical realm both the SPARKLING technology on the acquisition side  124, 82 as well as our PySAP software  106 for MR image reconstruction. In this regard, the recently accepted CEA postdoc funding should help us move the technology to clinical 7T MR Systems (Magnetom Terra Siemens-Healthineers) in the University hospital of Poitiers through a nascent collaboration with Prof. Rémy Guillevin. Their interest is to use the high-resolution SPARKLING SWI protocol at 7T to better delineate the anomalies along the central vein for the diagnostic of multiple sclerosis as the number of anomalies predicts the grade/severity of this inflammatory pathology. On a longer perspective, we aim to generalize the use of our recently DL networks for MR image reconstruction  155, 153 to multiple acquisition setups and other downstream tasks (e.g. motion correction and correction of off-resonance artifacts related to B0 inhomogeneities).

4.4 From brain images and bio-signals to quantitative biology and physics

Thanks to the developments in MIND:subsec:MLIP and MIND:subsec:MLSTP we aim to approximate more accurately the biophysical models underlying MRI and electrophysiological signals. By estimating quantities grounded in the physics of the data (time, spatial localization, tissue properties) we ambition to offer more actionable outputs for cognitive, clinical and pharmacological applications.

Technologies like 4D SPARKLING should in the future allow us to carry out both fast high resolution multi-parametric quantitative imaging (e.g. T1, T2 and proton density mapping) and laminar (i.e. layer-based) functional imaging in BOLD-fMRI. First, in the mqMRI and fMRI setting, the fourth dimension is respectively the weighting contrast and time axis. mqMRI imaging enables a precise quantification of biomarkers such as iron stores in the pathological brain. Measuring these parameters intra-cortically in Parkinsonian patients defines one of the key challenges in the coming years, especially at 7 Tesla, to earlier stratify the PD patients and the evolution of their disease. Second, a particular attention will be paid to the impact of the developments performed in MIND:subsec:MLIP on the statistical sensitivity of brain activity detection, which eventually defines the final validation metric of the data acquisition/image reconstruction pipeline. For this purpose, robust experimental activation protocols such as retinotopic mapping will be used for validation on the 7T scanner and eventually on the 11.7T Iseult MR system. The finest target resolution is 500 μm isotropic in 3D.

Novel development on bi-level optimization for hyper-parameter selection from ssub:bilevel will bring state-of-the-art inverse methods to end users currently facing the difficulty of performing model selection on empirical data efficiently. This will lead to more accurate quantitative assessments, in sub-millimeters and milliseconds, of where neural activity occurs.

The line of work on inverse problems should also impact how non-invasive neuroimaging and electrophysiology, based on MRI, EEG and MEG, is considered by more traditional neurophysiologists working with animal data. By considering biophysical models of the data and aiming to estimate their parameters from empirical recordings our hope is to present estimates of physical quantities (tissue properties, neural interactions strengths, etc.). The line of work based on stochastic simulation based inference (SBI) can revolutionize the way MEG, EEG and MRI data are apprehended. For this line of work we will explore the inversion of the models as offered by major software such as The Virtal Brain (TVB)  164 or the Human Neocortical Neurosolver (HNN)  140. A student from the group of Prof. S. Jones at the origin of the HNN software visited the team in 2022.

5 Social and environmental responsibility

The MIND team has not yet implemented specific guidelines for measuring carbon emission related to its research activities. Team members maximize the use of train for travelling across Europe and try to minimize the number of oversea flights per individual.

6 Highlights of the year

Philippe Ciuciu was plenary speaker at the XXIX GRETSI conference in Grenoble (Aug, 28-31). GRETSI is the French congress for signal and image processing and gathers approximately 500 people every two years.

6.1 Awards

Two PhD students from the MIND team received an award from the STIC Doctoral school in 2023. These prizes were jointly awarded by Paris-Saclay University and Polytechnique Paris Institute.

Two first prizes were awarded, notably one to Mathieu DAGREOU from Inria Saclay (MIND) & Paris-Saclay University for his paper entitled “A framework for bilevel optimization that enables stochastic and global variance reduction algorithms”. Mathieu DAGREOU is currently a third year PhD student under the supervision of Thomas Moreau.

Five runners-up prizes were awarded, including one to Charlotte CAUCHETEUX from Inria Saclay (MIND) & Paris-Saclay University, for her paper entitled “Evidence of a predictive coding hierarchy in the human brain listening to speech”. Charlotte CAUCHETEUX defended her PhD thesis on May, 10 2023 under the joint supervision of Alexandre GRAMFORT and Jean-Rémy KING.

7 New software, platforms, open data

7.1 New software

7.1.1 MNE

  • Name:
  • Keywords:
    Neurosciences, EEG, MEG, Signal processing, Machine learning
  • Functional Description:
    Open-source Python software for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, and more.
  • Release Contributions:
  • URL:
  • Contact:
    Alexandre Gramfort
  • Partners:
    HARVARD Medical School, New York University, University of Washington, CEA, Aalto university, Telecom Paris, Boston University, UC Berkeley, Macquarie University, University of Oregon, Aarhus University

7.1.2 NeuroLang

  • Name:
  • Keywords:
    Neurosciences, Probabilistic Programming, Logic programming
  • Functional Description:
    NeuroLang is a probabilistic logic programming system specialised in the analysis of neuroimaging data, but not exclusively determined by it.
  • Release Contributions:
  • URL:
  • Contact:
    Demian Wassermann

7.1.3 Nilearn

  • Name:
    NeuroImaging with scikit learn
  • Keywords:
    Health, Neuroimaging, Medical imaging
  • Functional Description:
    NiLearn is the neuroimaging library that adapts the concepts and tools of scikit-learn to neuroimaging problems. As a pure Python library, it depends on scikit-learn and nibabel, the main Python library for neuroimaging I/O. It is an open-source project, available under BSD license. The two key components of NiLearn are i) the analysis of functional connectivity (spatial decompositions and covariance learning) and ii) the most common tools for multivariate pattern analysis. A great deal of efforts has been put on the efficiency of the procedures both in terms of memory cost and computation time.
  • Release Contributions:

    HIGHLIGHTS - Updated docs with a new theme using furo. - permuted_ols and non_parametric_inference now support TFCE statistic. - permuted_ols and non_parametric_inference now support cluster-level Family-wise error correction. - save_glm_to_bids has been added, which writes model outputs to disk according to BIDS convention.

    NEW - save_glm_to_bids has been added, which writes model outputs to disk according to BIDS convention. - permuted_ols and non_parametric_inference now support TFCE statistic. - permuted_ols and non_parametric_inference now support cluster-level Family-wise error correction. - Updated docs with a new theme using furo.

    See all details in https://nilearn.github.io/stable/changes/whats_new.html

  • URL:
  • Contact:
    Bertrand Thirion
  • Participants:
    Alexandre Abraham, Alexandre Gramfort, Bertrand Thirion, Elvis Dohmatob, Fabian Pedregosa Izquierdo, Gael Varoquaux, Loic Esteve, Michael Eickenberg, Virgile Fritsch

7.1.4 Benchopt

  • Keywords:
    Mathematical Optimization, Benchmarking, Reproducibility
  • Functional Description:

    BenchOpt is a package to simplify, make more transparent and more reproducible the comparisons of optimization algorithms. It is written in Python but it is available with many programming languages. So far it has been tested with Python, R, Julia and compiled binaries written in C/C++ available via a terminal command. If it can be installed via conda, it should just work!

    BenchOpt is used through a simple command line and ultimately running and replicating an optimization benchmark should be as easy a cloning a repo and launching the computation with a single command line. For now, BenchOpt features benchmarks for around 10 convex optimization problems and we are working on expanding this to feature more complex optimization problems. We are also developing a website to display the benchmark results easily.

  • Release Contributions:
  • News of the Year:

    We organized in July 2023 a benchmarking sprint with benchopt with more than 40 participants.

    During this sprint, we developed new benchmarks, gathered feedback, and saw which benchmarking tools were most needed. Most of the participants had never worked with benchopt. Their goal was to create new benchmarks using benchopt, ranging from optimization benchmarks – e.g. training a neural network with pytorch or jax on image classification – to more complex workflows – e.g. comparing different fMRI alignment or domain adaptation methods. The participants were all able to produce benchmarks within a few hours! Note that an important aspect of this benchmarking initiative is that the goal is not to re-code the various methods but to aggregate them from the different sources they are available from.

    Take aways from this sprint have been discussed in a blog post accessible here: https://notes.inria.fr/s/OuVJYf8bM#

  • Publication:
  • Contact:
    Thomas Moreau
  • Participants:
    Thomas Moreau, Alexandre Gramfort, Mathurin Massias, Badr Moufad

7.1.5 Scikit-learn

  • Keywords:
    Clustering, Classification, Regression, Machine learning
  • Scientific Description:
    Scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit scientific Python world. It aims to provide simple and efficient solutions to learning problems, accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering.
  • Functional Description:

    Scikit-learn can be used as a middleware for prediction tasks. For example, many web startups adapt Scikitlearn to predict buying behavior of users, provide product recommendations, detect trends or abusive behavior (fraud, spam). Scikit-learn is used to extract the structure of complex data (text, images) and classify such data with techniques relevant to the state of the art.

    Easy to use, efficient and accessible to non datascience experts, Scikit-learn is an increasingly popular machine learning library in Python. In a data exploration step, the user can enter a few lines on an interactive (but non-graphical) interface and immediately sees the results of his request. Scikitlearn is a prediction engine . Scikit-learn is developed in open source, and available under the BSD license.

  • URL:
  • Publications:
  • Contact:
    Olivier Grisel
  • Participants:
    Alexandre Gramfort, Bertrand Thirion, Gael Varoquaux, Loic Esteve, Olivier Grisel, Guillaume Lemaitre, Jeremie Du Boisberranger, Julien Jerphanion
  • Partners:
    Axa, BNP Parisbas Cardif, Dataiku, Nvidia, Chanel

7.1.6 joblib

  • Keywords:
    Parallel computing, Cache
  • Functional Description:
    Facilitate parallel computing and caching in Python.
  • News of the Year:

    We organized a dev sprint for joblib in February 2023, where we performed many maintenance changes and refactored a big part of the code necessary to provide asynchronous results collection in the library. This novel result collection paradigm was merged in March 2023 and released in the library.

    See more info in this thread: https://x.com/tomamoral/status/1648436595677331462

  • URL:
  • Contact:
    Gael Varoquaux


  • Keywords:
    MRI, Off-the-grid, Preconditioner, Brain MRI
  • Functional Description:

    MRI-NUFFT is a python package that extends various NUFFT (Non-Uniform Fast Fourier Transform) python bindings used for MRI reconstruction. It provides a unified interface with a large number of backends with implementations ranging from CPU to GPU.

    In particular, it provides a unified interface for all the methods, with extra features such as coil sensitivity, density compensated adjoint and off-resonance corrections (for static B0 inhomogeneities). Additionally, useful IO tools like reading a k-space sampling trajectory and writing a binary file for run on MR scanner is also offered. Finally, it helps algorithmically speed up MR image reconstruction algorithms through fast ways to estimate preconditioning weights, also known as density compensators for a given sampling pattern.

  • URL:
  • Contact:
    Philippe Ciuciu


  • Name:
    Spreading Projection Algorithm for Rapid K-space sampLING
  • Keywords:
    Brain MRI, MRI, Optimization
  • Scientific Description:
    This python package allows us to generate "SPARKLING" curves as a new type of non-Cartesian trajectories to perform a more efficient sampling in 2D and 3D for anatomical imaging while using the same number of samples for a limited time budget. These segmented curves are obtained using a projection method on measure sets which offers three main advantages: i) generating segmented Non-Cartesian trajectories along a chosen density, ii) meeting the hardware constraints on the magnetic field gradients (magnitude, slew rate), iii) performing a fast coverage of k-space.
  • Functional Description:
    This python package implements "SPARKLING": an optimization driven method to obtain hardware compliant sampling curves that globally satisfy a user specified target sampling density. The resulting non-cartesian sampling curves can be used to efficiently undersample and speed up acquisitions on an MR scanner. This method is generic enough that it can be applied to any of the imaging modalities in MR.
  • Publications:
  • Contact:
    Chaithya Giliyar Radhkrishna

7.1.9 PySAP

  • Name:
    Python Sparse data Analysis Package
  • Keywords:
    Image reconstruction, Image compression
  • Functional Description:

    The PySAP (Python Sparse data Analysis Package, https://github.com/CEA-COSMIC/pysap) open-source image processing software package has been developed for the 3 years between the Compressed Sensing group at Iniria-CEA Parietal team led by Philippe Ciuciu and the CosmoStat team (CEA/IRFU) led by Jean-Luc Statck. It has been developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image reconstruction problems in various research domains. In particular, PySAP offers fast wavelet transforms and a range of integrated optimisation algorithms. It also offers a variety of plugins for specific application domains: on top of Pysap-MRI and PySAP-astro plugins, several complementary modules are now in development for electron tomography and electron microscopy for CEA colleagues. In October 2019, PySAP has been released on PyPi (https://pypi.org/project/python-pySAP/, currently version 0.0.3) and in conda (https://anaconda.org/agrigis/python-pysap).

    The Pysap-MRI has been advertised through a specific abstract accepted to the next workshop of ISMRM on Data Sampling & Image Reconstruction in late January 2020. It will be presented during a power pitch session together wih an hands-on demo session using JuPyter notebooks.

  • Contact:
    Philippe Ciuciu
  • Partner:

8 New results

8.1 Accelerated acquisition in MRI

Participants: Main External Collaborators: Chaithya Giliyar Radhakrishna, Zaineb Amor, Philippe Ciuciu, Alexandre Vignaud [CEA/NeuroSpin], Aurélien Massire [Siemens-Healthineers, France], Guillaume Daval-Frérot [Chipiron, France].

MRI is a widely used neuroimaging technique used to probe brain tissues, their structure and provide diagnostic insights on the functional organization as well as the layout of brain vessels. However, MRI relies on an inherently slow imaging process. Reducing acquisition time has been a major challenge in high-resolution MRI and has been successfully addressed by Compressed Sensing (CS) theory. However, most of the Fourier encoding schemes under-sample existing k-space trajectories which unfortunately will never adequately encode all the information necessary. Recently, the Mind team has addressed this crucial issue by proposing the Spreading Projection Algorithm for Rapid K-space sampLING (SPARKLING) for 2D/3D non-Cartesian T2* and susceptibility weighted imaging (SWI) at 3 and 7 Tesla (T) 123, 124, 107. These advancements have interesting applications in cognitive and clinical neuroscience as we already have adapted this approach to address high-resolution functional and metabolic (Sodium 23Na) MR imaging at 7T – a very challenging feat  1. However, the original SPARKLING trajectories are prone to off-resonance effects due to susceptibility artifacts. Therefore, we have extended the original SPARKLING methodology and developed the MORE-SPARKLING (Minimized Off-Resonance Effects) approach to correct for these artifacts. The fondamental idea implemented in MORE-SPARKLING is to make different k-space trajectories more homogeneous in time, in the sense that samples supported by different trajectories that are close in k-space must be collected at approximately the same time point. This allows us to mitigate the issue of off-resonance effects (signal void, geometric distortions) without increasing the scan time, as MORE-SPARKLING trajectories have exactly the same duration as their SPARKLING ancestor. This approach has been published  2 and is illustrated in Fig. 1. A patent application has been filed by the CEA for MORE-SPARKLING.

Complementary to that, we have also extended SPARKLING in another direction, namely the way we sample the center of k-space and proposed the GoLF-SPARKLING version in the same paper  2. The core idea here is to reduce the over-sampling of the center of k-space and grid it to collect Cartesian data and make notably the estimation of sensitivity maps in multicoil acquisition easier. Similarly to MORE-SPARKLING, a patent application has been filed for GoLF-SPARKLING.

Figure 1
Figure1: Comparison between the original SPARKLING and the new MORE-SPARKLING 3D sampling patterns in k-space for accelerated MRI in presence of B0 inhomogeneities. The sampling patterns are shown on the left. (A): Cartesian reference acquisition. (B): ΔB0 field map that captures the local variations in space of the static magnetic field B0. (C): Reconstructed 3D MR volume using the SPARKLING acquisition. Signal losses are depicted by blue arrows. (D): Reconstructed 3D MR volume using the MORE-SPARKLING version. The recovered signal is pointed with blue arrows. Minor artifacts are still there and depicted with orange/red arrows.

Additionally, we have shown that 3D-SPARKLING is a viable imaging technique and good alternative to Echo Planar Imaging for resting-state and task-based fMRI  4. This is illustrated in Fig. 2 during a retinotopic mapping experiment which consists in mapping the retina to the primary visual cortex. These results have been obtained at a 1mm isotropic resolution both for EPI and SPARKLING acquisitions.

Figure 2
Figure2: Projection of the BOLD phase maps on the pial surface visualized on the inflated surface for participants V#3 (3D-SPARKLING run first) and V#4 (3D-EPI run first). 3D-SPARKLING yields improved projected BOLD phase maps for V#3 in comparison with 3D-EPI both on raw and spatially smoothed data. Opposite results were found in favor of 3D-EPI in V#4, notably on spatially smoothed data.

8.2 Deep learning for MR image reconstruction and artifact correction

Participants: Main External Collaborators: Guillaume Daval-Frérot, Chaithya Giliyar Radhakrishna, Philippe Ciuciu, Alexandre Vignaud [CEA/NeuroSpin], Mariappan Nadar [Siemens-Healthineers, USA], Boris Mailhé [Siemens-Healthineers, USA].

Although CS is used extensively, this approach suffers from a very slow image reconstruction process, which is detrimental to both patients and rapid diagnosis. To counteract this delay and improve image quality, as explained in Sec. 3.1 deep learning is used. In 2020 we secured the second spot in the 2020 brain fastMRI challenge (1.5 and 3T data)  139 with the XPDNet (Primal Dual Network where X plays the role of a magic card) deep learning architecture. Additionally, we assessed XPDNet's transfer learning capacity on 7T NeuroSpin T2 images. However this DL reconstruction process was limited to Cartesian encoding, thus incompatible with our SPARKLING related technological push. In 2022, we went therefore further by proposing the NCPD-Net deep learning architecture for non-Cartesian imaging. NCPD-Net stands for Non-Cartesian Primal Dual Network and is able to handle both 2D and 3D non-Cartesian k-space data such as those collected with the full 3D SPARKLING encoding scheme  154. This progress allowed us to make a significant leap in image quality when implementing high resolution imaging while maintaining a high acceleration rate.

In 2023, we published novel significant results based on the original NC-PDNet archiecture. We actually combined with physics-driven model to speed up the correction of off-resonance effects induced by the inhomogeneities of the static magnetic field B0  6. Fig. 3[left column] shows the signal void in the frontal region of the brain when not applying any correction. The CS correction yields a limited improvement when constraining its processing time to a little portion (actually 1/70) of the brute force correction shown in the top right column (Reference). Next, we show that the best correction of off-resonance artifacts is achieved by combining the NC-PDNet architecture (Network, 4th column in Fig. 3) with non Fourier encoding model in a 70-fold faster process compared to the reference correction (left column). In contrast, using a standard NC-PDNet architecture that is not physically informed by the degradation process causing these off-resonance artifacts leads to oversmoothed correction (cf. middle column in Fig. 3). Overall, this work has demonstrated how investigating into the combination of physics-informed deep learning architectures was instrumental in obtaining high image quality in clinically viable processing time.

Figure 3
Figure3: Deep learning physics-informed correction of off-resonance artifacts during MR image reconstruction using the NC-PDnet architecture. Illustration on a single SWI volume (high resolution: 600 μm). Left (red): Compressed Sensing (CS) reconstruction with no artifact correction, computed in 25min. Center left: CS reconstruction using a reduced non-Fourier forward model to correct for B0 inhomgoneities in approximately 10min. Middle (purple): Network or NC-PDnet based reconstruction without physics based knowledge to correct forB0 inhomgoneities (approx. 1min of computation). Center right (blue): NC-PDnet based reconstruction without physics based knowledge to correct forB0 inhomgoneities (7min of computation). Right (green): Classical CS reconstruction and off-resonance artifact correction based on an extended non-Fourier model, which costs 8 hours of computation. The best result is that in the blue frame.

MRI magnetic resonance imaging essentially involves the optimization of (1) the sampling pattern in k-space under MR hardware constraints and (2) image reconstruction from undersampled k-space data. Recently, deep learning methods have allowed the community to address both problems simultaneously, especially in the non-Cartesian acquisition setting. In 2023, the MIND has made major contribution  9 to this field by tackling some major concerns in existing approaches. Particularly, current state-of-the-art learning methods seek hardware compliant k-space sampling trajectories by enforcing the hardware constraints through additional penalty terms in the training loss. Through ablation studies, we rather show the benefit of using a projection step to enforce these constraints and demonstrate that the resulting k-space trajectories are more flexible under a projection-based scheme, which results in superior performance in reconstructed image quality. In 2D studies, our novel PROjection for Jointly lEarning non-Cartesian Trajectories while Optimizing Reconstructor (PROJeCTOR) trajectories present an improved image reconstruction quality at a 20-fold acceleration factor on the fastMRI data set with SSIM scores of nearly 0.92–0.95 in our retrospective studies as compared to the corresponding Cartesian reference and also see a 3–4 dB gain in PSNR as compared to earlier state-of-the-art methods. Finally, we extend the algorithm to 3D and by comparing optimization as learning-based projection schemes in Fig. 4, we show that data-driven joint learning-based PROJeCTOR trajectories outperform model-based methods such as SPARKLING through a 2 dB gain in PSNR and 0.02 gain in SSIM.

Figure 4
Figure4: A generic learning-based framework for joint optimization of the MRI acquisition and reconstruction models. Qualitative and quantitative comparisons of reconstructed images from 3D (B) SPARKLING and (C) PROJeCTOR trajectories at an acceleration factor AF = 20 as compared to (A) Cartesian reference. The reconstructed images are shown in the top row, while the residuals are shown in the bottom. Further, box plots of the SSIM and PSNR scores on 20 test data sets are shown in the bottom-left. The significance levels are marked through a paired samples Wilcoxon test.

8.3 Large Scale Bayesian Network Resolution with Applications to Neuroimaging

Participants: Demian Wassermann, Louis Rouillard, Alexandre Le Bris, Thomas Moreau.

Bayesian networks (BNs) have emerged as a powerful tool for modeling complex relationships among variables in neuroimaging data generatively. Their ability to capture causal and probabilistic dependencies makes them well-suited for representing the intricate and multifaceted nature of brain activity. However, the large scale of neuroimaging data presents a significant challenge for BNs. This data in a large database can scale to millions of random variables, if we consider 1,000 subjects with at least 32,000 measurements on the brain cortex per subject. As the number of variables increases, the complexity of the BN grows exponentially, making it computationally intractable to fit these models using existing generic algorithms. This computational bottleneck impedes the widespread application of BNs in neuroimaging research and hinders our ability to fully understand the intricate workings of the brain.

To address this problem we have porposed the PAVI method (Plate Amortized Variational Inference)  158 as a follow up of our previously proposed Automatic Dual Amortized Variational Inference (ADAVI)  159. These methods harness the symmetry of large neuroimaging models, specifically considering each cortical measurement and each subject as independent identically distributed realizations of the same random variables. In complement with stochastic optimization techniques, the PAVI algorithm is able to produce individualized parcellations of 1,000 of subjects at a time. Thus, we are now able to harness the descriptive and interpretable power of BNs to produce individualized cortical parcellations which we then test through the prediction of cognitive abilities.

Figure 5
Figure5: Results from individualized parcellation and cognitive function preduction using our method PAVI  158. On the left the population parcellation along with two individualized ones. On the right the aggregated correlation of cognitive score predictions for 1,000 subjects along with those currently in literature

8.4 Generalized parametric models for temporal point processes

Participants: Main External Collaborators: Cedric Allain, Guillaume Staerman, Virginie Loison, Thomas Moreau, Alexandre Gramfort [Meta].

Temporal point processes (TPP) are a natural tool for modeling event-based data. Among all TPP models, Hawkes processes have proven to be the most widely used, mainly due to their adequate modeling for various applications, particularly when considering exponential or non-parametric kernels. Although non-parametric kernels are an option, such models require large datasets. While exponential kernels are more data efficient and relevant for specific applications where events immediately trigger more events, they are illsuited for applications where latencies need to be estimated, such as in neuroscience. We developed an efficient solution to TPP inference using general parametric kernels with finite support. The developed solution consists of a fast 2 gradient-based solver leveraging a discretized version of the events. After theoretically supporting the use of discretization, the statistical and computational efficiency of the novel approach has been demonstrated through various numerical experiments. Finally, the method's effectiveness is evaluated by modeling the occurrence of stimuli-induced patterns from brain signals recorded with magnetoencephalography (MEG), as reported in Fig. 6. This result was presented in Staerman et al.  34. These results are prerequisites to scale the resolution of bi-level optimization problems to larger applications such as the one in neurosciences.

Figure 6

Spatial and temporal patterns of 2 atoms known to be associated with auditory and visual brain responses, and their estimated associated intensity function for auditory and visual external stimuli. Each response is associated with the correct stimuli and the intensity function gives an estimate of the delay between the stimuli and the response.

Figure6: Spatial and temporal patterns of 2 atoms from sample MEG dataset, and their respective estimated intensity functions after a stimulus (cue at time = 0 s), for auditory and visual stimuli with non-parametric (NP), Truncated Gaussian (TG) and Raised Cosine (RC) kernels. The parametric kernels recover cleaner estimates of the brain response to the stimuli compare to NP, and both estimate correctly the link between the stimuli and the response.

8.5 Statistically Valid Variable Importance Assessment through Conditional Permutations

Participants: Main External Collaborators: Bertrand Thirion, Ahmad Chamma, Denis Engemann [Roche].

Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-based importance assessment is currently the reference approach, particularly when statistical guarantees are sought to justify variable inclusion. It is often implemented with variable permutation schemes. On the flip side, these approaches risk misidentifying unimportant variables as important in the presence of correlations among covariates. Here we develop a systematic approach for studying Conditional Permutation Importance (CPI) that is model agnostic and computationally lean, as well as reusable benchmarks of state-of-the-art variable importance estimators. We show theoretically and empirically that CPI overcomes the limitations of standard permutation importance by providing accurate type-I error control. When used with a deep neural network, CPI consistently showed top accuracy across benchmarks. An experiment on real-world data analysis in a largescale medical dataset showed that CPI provides a more parsimonious selection of statistically significant variables. Our results suggest that CPI can be readily used as drop-in replacement for permutation-based methods.

Figure 7
Figure7: Performance of Conditional permutation-based vs standard permutation-based variable importance: Performance at detecting important variables on simulated data with n=300 samples and p=100 features. (A): The type-I error quantifies to which extent the rate of low p-values (p<0.05) exceeds the nominal false positive rate. (B): The AUC score measures to which extent variables are ranked consistently with the ground truth. Dashed line: targeted type-I error rate. Solid line: chance level.

8.6 False Discovery Proportion control for aggregated Knockoffs

Participants: Main External Collaborators: Bertrand Thirion, Alexandre Blain, Pierre Neuvial [IMT, Univ. Toulouse], Olivier Grisel [Inria, Soda team].

Controlled variable selection is an important analytical step in various scientific fields, such as brain imaging or genomics. In these high-dimensional data settings, considering too many variables leads to poor models and high costs, hence the need for statistical guarantees on false positives. Knockoffs are a popular statistical tool for conditional variable selection in high dimension. However, they control for the expected proportion of false discoveries (FDR) and not their actual proportion (FDP). We present a new method, KOPI, that controls the proportion of false discoveries for Knockoff-based inference. The proposed method also relies on a new type of aggregation to address the undesirable randomness associated with classical Knockoff inference. We demonstrate FDP control and substantial power gains over existing Knockoff-based methods in various simulation settings and achieve good sensitivity/specificity tradeoffs on brain imaging and genomic data.

Figure 8
Figure8: Application of Kopi to cognitive brain imaging. We have employed KOPI on fMRI and genomics data. The aim of fMRI data analysis is to recover relevant brain regions for a given cognitive task as shown below. Here we display brain regions whose activity predicts that the participant is atending to stimuli with social motion.

9 Bilateral contracts and grants with industry

Participants: Philippe Ciuciu, Guillaume Daval-Frérot, Alexandre Gramfort, Charlotte Caucheteux, Thomas Moreau.

9.1 Bilateral contracts with industry

9.1.1 Siemens Healthineers & AI lab (Princeton, USA)

Since Fall 2019, Philippe Ciuciu has actively collaborated with the Siemens-Healthineers AI lab, led by Mariappan Nadar in the context of the joint supervision of Guillaume Daval-Frérot's CIFRE PhD thesis dedicated to Deep learning for off-resonance artifact correction in MR image reconstruction in the specific application of susceptibility weighted imaging at 3 Tesla. On top of the PhD funding, this contract has generated 45k€ for Mind and was managed by CEA/NeuroSpin. G. Daval-Frérot's PhD defense was held on December, 16 2022. As this first collaboration was successful, we engaged strategic discussions with the Siemens-Healthineers headquarters (Erlangen, Germany) during Spring 2022 to pursue this partnership and eventually set up a new one in 2023. Finally, a new CIFRE PhD thesis will start in January 2024 where Mrs. Asma Tanabene will be working on self-supervised 3D MR image reconstruction using deep learning models.

9.1.2 Facebook AI Research (FAIR)

There is currently a CIFRE PhD between FAIR and Mind (Alexandre Gramfort) to investigate the differences between deep learning models and the brain, especially considering NLP machine learning models. As the collaboration is led by Alexandre Gramfort in the team, it is therefore financially managed by Inria.

9.1.3 Saint Gobain Research (SGR)

There is currently a consulting contract between SGR and Mind (Thomas Moreau) to provide an expertise in machine learning to process temporal data, numerical optimization and scientific computing. The expertise is provided one half-day per month, in SGR offices, and it consists in scientific discussion sessions on the ML projects leaded by SGR data scientists.

9.2 Bilateral Grants with Industry

9.2.1 Google

Mind (Thomas Moreau) received a 30k€ donation from Google to support its open source activity around benchopt. In particular, the grant aims to support the organization of coding and benchmarking sprints around benchopt, the development of visualization tools, and the benchmarking of bilevel solvers, in particular the ones using jaxopt.

Participants: Demian Wassermann, Bertrand Thirion, Philippe Ciuciu, Thomas Moreau, Chaithya Giliyar Radhakrishna.


10 Partnerships and cooperations

10.1 International initiatives

10.1.1 Inria associate team not involved in an IIL or an international program

  • Title:
    Precision mapping of the Brain by Neuromod & Mind
  • Duration:
    2022 ->
  • Coordinator:
    Pierre BELLEC (pierre.bellec@criugm.qc.ca)
  • Partners:
    • Université de Montréal Montréal (Canada)
  • Inria contact:
    Bertrand Thirion
  • Summary:
    Among the main advances of the last decade, the development of powerful AI systems for vision, language processing, as well as reinforcement learning, have led to sophisticated cognitive systems that can be compared to the human brain, and sometimes surpass human performance. Brain/AI system comparison is a great opportunity for AI and for neuroscience. One of the most urgent tasks for cognitive neuroscience is thus to put together datasets that probe the brain system and are comprehensive enough to allow a reliable comparison of brain activity to the representations generated by AI systems. To address this endeavor, Parietal and NeuroMod have launched ambitious data acquisitions initiative (individual Brain CHarting and Courtois Neuromod), that consist in collecting huge amounts of brain data in few participants. These unprecedented data collection efforts bring novel challenges for data analysis: handling TB-scale data, automation, and better integration of analysis pipeline. Software such as Nilearn and MNE increasingly face the challenge of scaling up to larger datasets. Addressing this challenge in the context of IBC and Courtois Neuromod is thus a unique opportunity.

10.2 International research visitors

10.2.1 Visits of international scientists

Other international visits to the team
David Degras
  • Status
    Associate Professor in the Department of Mathematics
  • Institution of origin:
    University of Massachusetts Boston
  • Country:
  • Dates:
    from 01/09/2023 to 30/06/2024
  • Context of the visit:

    David is financially supported by the DataIA program and the Inria Saclay Ile-de-France center. His research projects in the team will revolve around machine learning and optimization for computational neuroimaging. For a smooth integration in the team, he is currently involved in dynamic functional connectivity analysis from fMRI data. Next he will be contributing to two ongoing research projects, namely (i) robustness in multifractal analysis of MEG/EEG signals and (ii) functional alignment of brain images. The first project will crucially engage with issues of robustness in learning. The second will largely revolve around optimization (including combinatorial optimization for the choice of barycenter and optimization of hyperparameters). Both projects will investigation the reproducibility of the obtained results as well as out-of-sample validation with independent data. Together, these aspects address the following points of the subject area:

    • Automatic learning and hyper-optimization
    • Optimization for learning, e.g., improvements in stochastic gradient methods, Bayesian optimization), combinatorial optimization
    • Reproducibility and robust learning
    • Statistical Inference and Validation.

    The proposed projects both contain: (i) an important methodological component of general interest to the statistical and machine learning community, and (ii) an applied neuroimaging component that serves the long-term goal of promoting health (for example, by better understanding the neural foundations of psychological/neurodegenerative pathologies and by better predicting therapeutic/surgical outcomes for people based on their brain function and anatomy).

  • Mobility program/type of mobility:
    sabbatical, i.e. invited Professor.

10.3 European initiatives

10.3.1 H2020 projects

  • Title:
  • Duration:
    2023 -> 2026
  • Coordinator:
    Petra Ritter (Charité, Berlin)
  • Partners:
    • TP21 GMBH
  • Inria contact:
    Bertrand Thirion
  • Summary:

    The project aims to develop a decentralized, data protection-compliant research platform capable of simulating some of the brain’s complex neurobiological phenomena. As part of the project, researchers will collate an array of different types of information, including data from PET and MRI scans, EEG tests, behavioral studies and lifestyle surveys, as well as clinical data from thousands of patients and healthy controls. These will be combined with biological information from knowledge databases and made available for research purposes. The resultant digital ‘brain twins’ will enable large numbers of researchers to conduct innovative research within a powerful digital infrastructure.

    Thanks to its transparent analytical pipelines, the new research infrastructure will also help to promote reproducible research.

    Furthermore, complex, personalized brain simulations which take into account large quantities of data may be able to provide a better understanding of the mechanisms underlying brain function and disorders. Virtual brain modeling may also improve diagnostics and disease prediction, in addition to enabling the optimization of treatment plans. The project consortium comprises 20 partners and operates in cooperation with EBRAINS AISBL, the coordinating entity of the EU-funded flagship ‘Human Brain Project’.

10.4 National initiatives

CEA BlueSky project
  • Title:
    Brain & Computers
  • Duration:
    2023 ->
  • Coordinator:
    Philippe Ciuciu (CEA/DRF/JOLIOT/NEUROSPIN/MIND), Saclay
  • Partners:
    • CEA/DRT/LIST/DSCIN, Grenoble
  • Inria contact:
    Philippe Ciuciu
  • Summary:

    Artificial Intelligence (AI) is now capable of approaching human performance in tasks such as visual recognition, classification (i.e., decision-making), and even textual or visual production (e.g., GPT-4). Understanding the human brain mechanisms of learning and decision-making in an uncertain environment remains a major scientific challenge in neuroscience. This understanding will enable the development of AI architectures that replicate brain circuits. Additionally, in clinical applications, it can lead to a generational leap in the design of neuroprostheses. These neuroprostheses hold great promise for improving the quality of life for individuals affected by spinal cord injuries (approximately 30% of cases).

    The two main objectives of this BlueSky project complement each other. On one hand, in healthy subjects, the goal is to design computational models based on AI that encode these cerebral functions to gain a more precise understanding of the associated brain activity. On the other hand, the objective is to enable a greater number of patients to control neuroprostheses autonomously through AI, making these medical devices more widely accepted as a therapeutic solution for motor rehabilitation. Tackling such a challenge is not without risks and requires expanding knowledge in neuroscience, surpassing current technological and clinical limits. It also calls for strong synergies among the various CEA institutes involved (Joliot, LETI, and LIST) and their academic (Inserm, Inria, Universities Paris-Saclay, and Grenoble Alpes) and clinical partners (CHU Grenoble-Alpes).

    This project has received a funding of 1.5M€ for the 2023-2025 period.

  • Title:
    DARLING: Distributed adaptation and learning over graph signals
  • Duration:
    2020 -> 2025 (extended)
  • Coordinator:
    Cédric Richard (cedric.richard@unice.fr),Professor 3IA Senior Chair in UCA
  • Partners:
    • Université Côte d'Azur Nice, France
    • CNRS, École Normale Supérieure, Lyon, France
    • Gipsa-lab, UMR 5216, CNRS, UGA, Grenoble, France
    • CentraleSupélec, University of Paris-Saclay, Gif-sur-yvette, France
  • Inria contact:
    Philippe Ciuciu
  • Summary:
    The DARLING project will aim to propose new adaptive learning methods, distributed and collaborative on large dynamic graphs in order to extract structured information of the data flows generated and/or transiting at the nodes of these graphs. In order to obtain performance guarantees, these methods will be systematically accompanied by an in-depth study of random matrix theory. This powerful tool, never exploited so far in this context although perfectly suited for inference on random graphs, will thereby provide even avenues for improvement. Finally, in addition to their evaluation on public data sets, the methods will be compared with each other using two advanced imaging techniques in which two of the partners are involved: radio astronomy with the giant SKA instrument (Obs. Côte d'Azur) and MEG brain imaging (Inria MIND at NeuroSpin, CEA Saclay). Sheng Wang as a postdoc in MIND and Merlin Dumeur as a MIND PhD student in co-tutelle with Matias Palva from Aalto University, Finland are actually involved in the processing of MEG and S/EEG time series on graphs, notably to analyze scale-free (i.e. critical and bistability) phenomena across these graphs and extract potentially new biomarkers for characterizing the pathophysiology of epileptogenic zone (EZ) in drug resistant epilepsy.
  • Title:
    VLFMRI: Very low field MRI for babies
  • Duration:
    2021 -> 2025
  • Coordinator:
    Claude Fermon (CEA Saclay, DRF/IRAMIS/SPECT)
  • Partners:
    • CEA/SHFJ/BIOMAPS, Orsay, France
    • CEA/NeuroSpin, Gif-sur-Yvette, France
    • APHP Robert Debré hospital, Paris, France
    • APHP Bicêtre hospital, Kremlin-Bicêtre, France
  • Inria contact:
    Philippe Ciuciu
  • Summary:
    VLFMRI aims at developing a very low-field Magnetic Resonance Imaging (MRI) system as an alternative to conventional high-field MRI for continuous imaging of premature newborns to detect hemorrhages or ischemia. This system is based on a combination of a new generation of magnetic sensors based on spin electronics, optimized MR acquisition sequences (based on the SPARKLING patent, Inria-CEA MIND team at NeuroSpin) and a open and compatible system with an incubator that will allow to achieve an image resolution of 1mm3 on a whole baby body in a short scan time. This project is a partnership of three academic partners and two hospital departments. The different stages of the project are the finalization of the hardware development and software system, preclinical validation on small animals and clinical validation. Kumari Pooja has been hired in January 2022 as research engineer in MIND to interact with the coordinator of this ANR project, Claude Fermon and design new accelerated acquisition methods for verly low field MRI. Preliminary encouraging results allow us to retrospectively accelerate MRI acquisition by a factor of 10 without degrading image quality at 2mm isotropic resolution.
  • Title:
    KARAIB: Knowledge And RepresentAtion Integration on the Brain
  • Duration:
    2020 -> 2024
  • Coordinator:
    Bertrand Thirion
  • Partners:
    • INRIA MIND, Gif-sur-Yvette, France
  • Inria contact:
    Bertrand Thirion
  • Summary:

    Cognitive science describes mental operations, and functional brain imaging provides a unique window into the brain systems that support these operations. A growing body of neuroimaging research has provided significant insight into the relations between psychological functions and brain activity. However, the aggregation of cognitive neuroscience results to obtain a systematic mapping between structure and function faces the roadblock that cognitive concepts are ill-defined and may not map cleanly onto the computational architecture of the brain.

    To tackle this challenge, we propose to leverage rapidly increasing data sources: text and brain locations described in neuroscientific publications, brain images and their annotations taken from public data repositories, and several reference datasets. Our aim here is to develop multi-modal machine learning techniques to bridge these data sources.

    • Aim 1 develops representation techniques for noisy data to couple brain data with descriptions of behavior or diseases, in order to extract semantic structure.
    • Aim 2 challenges these representations to provide explanations to the observed relationships, based on two frameworks: i) a statistical analysis framework; ii) integration into a domain-specific language.
    • Aim 3 outputs readily-usable products for neuroimaging: atlases and ontologies and focuses on implementation, with contributions to neuroimaging web-based data sharing tools.site.
  • Title:
    BrAIN: Bridging Artificial Intelligence and Neuroscience
  • Duration:
    2020 -> 2024
  • Coordinator:
    Alexandre Gramfort
  • Partners:
    • INRIA MIND, Gif-sur-Yvette, France
  • Inria contact:
    Alexandre Gramfort
  • Summary:

    The BrAIN project investigates learning tasks from multivariate EEG and MEG time series. In clinical or cognitive neuroscience, electromagnetic signals emitted by synchronously firing neurons are collected by electroencephalography (EEG) or magnetoencephalography (MEG). Such data, typically sampled at millisecond resolution, are routinely used for clinical applications such as anesthesia monitoring, sleep medicine, epilepsy or disorders of consciousness. Low cost EEG devices are also becoming commodities with hardware startups such as DREEM in France or InteraXon in Canada that have collected hundred of thousands of neural recordings. The field of neuroscience urgently needs algorithms that can learn from such large and poorly labeled datasets. The general objectives of BrAIN is to develop ML algorithms that can learn with weak or no supervision on neural time series. It requires contributions to self-supervised learning, domain adaptation and data augmentation techniques, exploiting the known underlying physical mechanisms that govern the data generating process of neurophysiological signals.

    The BrAIN project is organized around four objectives:

    • Learn with no-supervision on noisy and complex multivariate signals
    • Learn end-to-end predictive systems from limited data exploiting physical constraints
    • Learn from data coming from many different source domains
    • Develop high-quality software tools that can reach clinical research
ANR MICBrainPres
  • Title:
    MicBrainPres: Distributed adaptation and learning over graph signals
  • Duration:
    2023 -> 2026
  • Coordinator:
    Demian Wassermann
  • Partners:
    • Brain and Spine Institute, Paris, France
    • CNRS, Université de Paris, Lyon, France
  • Inria contact:
    Demian Wassermann
  • Summary:
    The main goal of this project is to harness the latest advances on machine learning-based neuroimage processing technologies to improve function-preserving brain tumour resection. Identifying eloquent brain regions is fundamental to performing tumour resection while preserving a maximum level of cognitive function. Despite the sustained advance in predicting subject-level cognitive abilities from neuroimaging data, current approaches lack sensitivity and specificity in identifying eloquent brain regions. This hinders neuroimaging’s usefulness for pre-surgical planning as a tool to predict the preservation of cognitive function after tumour resection. In this project, we propose that using subject-specific parcellations, derived from functional and diffusion MRI through deep-learning technologies, will achieve the needed sensitivity and specificity to locate eloquent areas pre-surgically and to predict cortical reshaping after tumour resection.
  • Title:
    EBUL: Event-based Unsupervised Learning for Physiological Signals
  • Duration:
    2023 -> 2027
  • Coordinator:
    Thomas Moreau
  • Partners:
    • INRIA MIND, Gif-sur-Yvette, France
  • Inria contact:
    Thomas Moreau
  • Summary:

    Sensor-based body monitoring is now routine clinical care. The resulting records are called physiological signals. While enormous quantities of signals are collected every day, the cost and time necessary to clean and annotate them is prohibitive to constitute large labeled databases. When working with physiological signals, extra sources of information are the events surrounding the recordings. Events are external phenomena that impact the signal and can correlate with the prediction task considered. EBUL propose to develop novel unsupervised learning techniques to process such records based on the notion of events, and to apply them to process general anesthesia records collected in Paris hospital Lariboisière. The methodology of the project relies on the development of novel point process models adapted to capture the distribution of physiological events, and their coupling with event detection algorithms. This will provide novel signal representations based on the distribution of events inside them, which are simpler to analyze and fine tune to derive predictive bio-markers.

    The EBUL project is organized around 3 objectives:

    • Develop novel point process models for physiological signals
    • Learn joint models for signals and events
    • Develop high-quality models for general anesthesia that can reach clinical research

11 Dissemination

Participants: Demian Wassermann, Bertrand Thirion, Philippe Ciuciu, Thomas Moreau, Chaithya Giliyar Radhakrishna.

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

  • T. Moreau
    2019-2023: Co-organizer of the Séminaire Palaisien, a monthly seminar in the Saclay ecosystem around statistics and machine learning (40 people).
Member of the organizing committees
Member of the conference program committees
  • P. Ciuciu
    October 2023: Member of the congress planning committee of ESMRMB 2023, Basel, Switzerland.
  • B. Thirion
    PCMember for IABM conference.
  • B. Thirion
    Area chair for NeurIPS 2023.
  • T. Moreau
    Reviewer for ICML and NeurIPS 2023.

11.1.2 Journal

Member of the editorial boards
Reviewer - reviewing activities
  • P. Ciuciu
    IEEE Trans on Medical Imaging, IEEE Signal Processing Magazine, Magnetic Resonance in Medicine, iScience, Medical Image Analysis, Computers in Biology and Medicine, Developmental Cognitive Neuroscience.
  • B. Thirion
    NeuroImage, MEdIA, IEEE Transactions on Medical Imaging, Brain Structure and Function, Human Brain Mapping, Nature Communications.
  • T. Moreau
    Transactions on Biomedical Engineering.
  • D. Wassermann
    reviewed for several conferences and journals including Nature Communications, Medical Image Analysis, NeurIPS.
  • C. Giliyar Radhakrishna
    reviewed for Magnetic Resonance in Medicine and IEEE Transactions on Medical Imaging.

11.1.3 Invited talks

  • P. Ciuciu


  • B.Thirion


    • March 2023: Data science Institute, Oxford: "How to remove or Control Confounds in predictive models, with application to brain biomarkers"
    • April 2023: Conference at CYCERON imaging center, CEA/CNRS, Caen, France
    • April 2023: Conference at the Brain Matters symposium "Synthesizing brain images from the neuroscientific literature"
    • June 2023: HBP WP2 final conference, 2 presentations; Barcelona, Spain
    • June 2023: One health day, Université Paris Saclay "Dealing with the data deluge: identifying important variables from multimodal health data".
  • T. Moreau


    • June 2023: Invited speaker at the SIAM OP 2023 conference, Seattle, USA.
    • August 2023: Invited speaker at the ICIAM 2023 conference, Tokyo, Japan.
  • D.Wassermann


    • October 2023: Neurolang Tutorial at Cogbases: Boosting Specificity in Cognitive Neuroimaging
    • October 2023: Keynote at Computational Diffusion MRI Workshop: How do we link diffusion MRI with brain structure and cognition? Breaching the gap between classical neuroanatomy and machine learning.
    • May 2023: Neural 2023: Specificity in Cognitive Neuroimaging: Pushing the Envelope in Meta-Analyses by Harnessing Rich Probabilistic Logical Models
    • March 2023: Neurosynth 2.0 group meeting: Specificity in Cognitive Neuroimaging: Pushing the Envelope in Meta-Analyses by Harnessing Rich Probabilistic Logical Models

11.1.4 Scientific expertise

  • P. Ciuciu

    • European expert reviewer for the European Innovation Council Accelerator actions (main track: AI and health).
    • External reviewer for the European Research Council (ERC) Synergy grant.

11.1.5 Research administration

  • P. Ciuciu


    • Member of the Board of Directors at NeuroSpin (CEA).
    • Member of the steering committee of the CEA cross-disciplinary research program on numerical simulation and AI.
  • B. Thirion


    • Délégué Scientifique of Inria Saclay Center
    • Member of ENS Paris-Saclay Scientific Council
    • Member of Telecom Sud Paris Scientific Council
    • Member of Inria Commission évaluation

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

  • P. Ciuciu


    • Tutorial presenter at the 2023 IEEE ISBI: Computational MRI in the Deep Learning Era.
    • Instructor at the at Harmonic and Multifractal analysis summer school.
    • October 2023: Invited speaker at the 2023 AMU Biomedical Imaging school in Cargese (Corsica, France).
    • Lecturer at the Institut d'Optique Graduate School (3rd year, Signal & Images major).
    • Lecturer at the M2 ATSI (CentraleSupelec, ENS Paris-Saclay): Medical imaging course.
  • D. Wassermann


    • Master MVA (École Polytechnique, École Normale Superiore, Centrale Supelec): Graphical Models
    • Master in Biomedical Engineering (Université Paris Descartes): Quantification in NeuroImaging.
  • T. Moreau


    • Master Data Science (IPP/UP Saclay): Datacamp.
    • Executive Master Big Data (Université Dauphine): Introduction to Python.
  • B.Thirion


    • MVA Master (École Polytechnique, École Normale Superiore, Centrale Supelec): Brain Function ; 12h
    • NeuroEngineering Master (UPSaclay): fMRI data analysi; 2h
    • Instructor at the HBP/Ebrains conference, March 2023: tutorial on fMRI data analysis on Ebrains ; 2h
    • Instructor at ESMRMB conference, Oct 2023, Basel. Two courses taught: "When does the use of statistics make sense ?" and "Help ! My sample is too small."

11.2.2 Supervision

  • P. Ciuciu


    • C. Giliyar Radhakrishna, PhD 2020-2023
    • M. Dumeur (with M. Palva, Aalto Univ), PhD in cotutelle (4y), 2020-2024
    • Z. Amor (with A. Vignaud, CEA) PhD 2020-2024
    • P.-A. Comby (with A. Vignaud, CEA), PhD 2021-2024
    • S. Brosset, (with Z. Saghi, CEA) PhD 2022-2025
    • D. Nuñez-Fernandez (with A. Vignaud, CEA), PhD 2023-2026
    • M. Marzulli, M1 BME Paris, (June - Sep 2023)
    • L. Oudjman, work-study professional master's degree at EpiTech (Oct 2023 - Jul 2025)
  • B. Thirion


    • Alexandre Pasquiou, PhD 2020-2023 (with C.Pallier)
    • Alexis Thual, PhD 2020-2024 (with S. Dehaene)
    • Ahmad Chamma, PhD 2021-2023 (with D.Engemann)
    • Raphael Meudec, PhD 2021-2024 (with D.Wassermann)
    • Thomas Chapalain, PhD 2021-2024 (with E.Eger)
    • Alexandre Balin, PhD 2021-2024 (with P.Neuvial)
    • Nicolas Salvy, PhD 2023-2026 (with H.Talbot)
  • D. Wassermann


    • Gaston Zanitti, PhD 2020-2023
    • Louis Rouillard, PhD 2021-2024
    • Raphael Meudec, PhD 2021-2024 (with B. Thirion)
    • Alexandre Le Bris, PhD 2022-2025
    • Gabriela Gomez Jimenez, PhD 2023-2026 (with J. Valette CEA)
  • T. Moreau


    • B. Malézieux (with M. Kowalski, UP Saclay), PhD 2020-2023
    • C. Allain (with A. Gramfort, Meta), PhD 2021-2024
    • M. Dagréou (with S. Vaiter, Université Cote d'Azur and P. Ablin, Apple), PhD 2021-2024
    • F. Michel (with M. Kowalski, UP Saclay), PhD 2022-2025
    • V. Loison (with J. Cartailler, APHP), PhD 2023-

11.2.3 Juries

  • P. Ciuciu
    : External reviewer of the PhD thesis defended by Clément Poiret (Univ Paris-Cité), Paris, France.
  • P. Ciuciu
    : PhD committee president of the PhD thesis defended by Pierre Barbot (Univ Paris-Saclay), L2S, Gif-sur-Yvette, France.
  • B. Thirion
    External reviewer of the PhD thesis defended by Lucrezia Carboni (Grenoble Université), October 2023
  • B. Thirion
    External reviewer of the PhD thesis defended by Etrit Haxholli  (Université Nice Côte d'Azur), October 2023
  • B. Thirion
    PhD committee president for the PhD thesis defended by Adrien Pavao (Université Paris-Saclay), in Dec 2023.
  • B. Thirion
    PhD committee president for the PhD thesis defended by Sydnez Krystal (Université Paris-Saclay), in Dec 2023.

11.3 Popularization

  • P.Ciuciu, B. Thirion
    Le 13-14 broadcasted on France Inter radio, April, 12. Round table around AI progress for health and neuroscience.
  • B.Thirion
    CPGE Saint Benoit, December 2023 Où va l'intelligence artificielle ?
  • B.Thirion
    Plenary conference at NeuroPlanet, Marc 2023 Cartographie Cérébrale Individualisée
  • D. Wassermann
    A Pint of Beer, talk on AI and Human Brain Function, June 2023

11.3.1 Internal or external Inria responsibilities

  • D. Wassermann
    COERLE Scientific representative for Inria Saclay Île-de-France; Representative at the Graduate School in Computer Science of Université Paris-Saclay for Inria Saclay Île-de-France.
  • T. Moreau
    President of the Inria Saclay CUMI; Representative for Inria Saclay in the commission for the development of the national computational resources.

11.3.2 Education

  • P.Ciuciu
    Talk given at the 3rd Doctoral Students day organized by DataIA for PhD students in AI/Data Science at the Paris-Saclay University, December 7.

12 Scientific production

12.1 Major publications

12.2 Publications of the year

International journals

International peer-reviewed conferences

  • 24 inproceedingsZ.Zaineb Amor, P.-A.Pierre-Antoine Comby, C.Caroline Le Ster, A.Alexandre Vignaud and P.Philippe Ciuciu. Non-Cartesian non-Fourier fmri imaging for high-resolution retinotopic mapping at 7 Tesla.CAMSAP 2023 - IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive ProcessingLos Suenos, Costa RicaDecember 2023HAL
  • 25 inproceedingsA.Alexandre Blain, B.Bertrand Thirion, O.Olivier Grisel and P.Pierre Neuvial. False Discovery Proportion control for aggregated Knockoffs.Proceedings of Advances in Neural Information Processing Systems 36 (NeurIPS 2023)NeurIPS 2023 – 37th Conference on Neural Information Processing SystemsNew Orleans, United States2023HALDOI
  • 26 inproceedingsC.Clément Bonet, B.Benoît Malézieux, A.Alain Rakotomamonjy, L.Lucas Drumetz, T.Thomas Moreau, M.Matthieu Kowalski and N.Nicolas Courty. Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals.ICML 2023 - Fortieth International Conference on Machine LearningHonololu, United States2023, 1-29HAL
  • 27 inproceedingsA.Ahmad Chamma, D.Denis Engemann and B.Bertrand Thirion. Statistically Valid Variable Importance Assessment through Conditional Permutations.NeurIPS 2023 - 37th Conference on Neural Information Processing SystemsNew Orleans (Louisiana), United StatesDecember 2023HAL
  • 28 inproceedingsA.Ahmad Chamma, B.Bertrand Thirion and D.Denis Engemann. Variable importance in high-dimensional settings requires grouping.AAAI 2024 - The 38th Annual AAAI Conference on Artificial IntelligenceVancouver, CanadaDecember 2023HAL
  • 29 inproceedingsA.Antoine Collas, T.Titouan Vayer, R.Rémi Flamary and A.Arnaud Breloy. Entropic Wasserstein component analysis.IEEE International Workshop on Machine Learning for Signal Processing (MLSP)Rome, ItalySeptember 2023HAL
  • 30 inproceedingsP.-A.Pierre-Antoine Comby, Z.Zaineb Amor, A.Alexandre Vignaud and P.Philippe Ciuciu. Denoising of FMRI volumes using local low rank methods.ISBI 2023 - International Symposium on Biomedical ImagingCarthagena de India, ColombiaIEEEApril 2023, 1-5HALDOI
  • 31 inproceedingsT.Theo Gnassounou, R.Rémi Flamary and A.Alexandre Gramfort. Convolutional Monge Mapping Normalization for learning on sleep data.NeurIPS 2023 - 37th Conference on Neural Information Processing SystemsNouvelle Orléans, United StatesNovember 2023HAL
  • 32 inproceedingsA.Ambroise Heurtebise, P.Pierre Ablin and A.Alexandre Gramfort. MultiView Independent Component Analysis with Delays.MLSP 2023 - 33rd IEEE International Workshop on Machine Learning for Signal ProcessingRome, ItalySeptember 2023HAL
  • 33 inproceedingsJ.Julia Linhart, A.Alexandre Gramfort and P. L.Pedro L C Rodrigues. L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference.NeurIPS 2023 - 37th Conference on Neural Information Processing SystemsNew Orleans (LA), United States2023, 1-27HALDOI
  • 34 inproceedingsG.Guillaume Staerman, C.Cédric Allain, A.Alexandre Gramfort and T.Thomas Moreau. FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels.PMLRInternational Conference on Machine Learning202Honololu, Hawaii, United StatesJuly 2023, 32575-32597HALback to text
  • 35 inproceedingsM. S.Maria Sayu Yamamoto, A.Apolline Mellot, S.Sylvain Chevallier and F.Fabien Lotte. Novel SPD matrix representations considering cross-frequency coupling for EEG classification using Riemannian geometry.EUSIPCO 2023 - The 31st European Signal Processing ConferenceHelsinki, FinlandSeptember 2023HAL

Conferences without proceedings

  • 36 inproceedingsZ.Zaineb Amor, C.Caroline Le Ster, C.Chaithya Giliyar Radhakrishna, G.Guillaume Daval-Frérot, B.Bertrand Thirion, N.Nicolas Boulant, F.Franck Mauconduit, M.Mirkes Christian, P.Philippe Ciuciu and A.Alexandre Vignaud. Impact of B 0 field imperfections correction on BOLD sensitivity in 3D-SPARKLING fMRI data.ISMRM & ISMRT 2023 - Annual Meeting & Exhibition - Annual Meeting of the International Society for Magnetic Resonance in MedicineToronto, CanadaJune 2023HAL
  • 37 inproceedingsA.Alexandre Pasquiou, Y.Yair Lakretz, B.Bertrand Thirion and C.Christophe Pallier. Probing brain context-sensitivity with masked-attention generation.CCN 2023 - Conference on Cognitive Computational NeuroscienceOxford, United Kingdom2023HALDOI

Scientific book chapters

Doctoral dissertations and habilitation theses

  • 39 thesisC.Charlotte Caucheteux. Language representations in deep learning algorithms and the brain.Université Paris-SaclayMay 2023HAL
  • 40 thesisC.Chengran Fang. Neuron modeling, Bloch-Torrey equation, and their application to brain microstructure estimation using diffusion MRI.Université Paris-SaclayFebruary 2023HAL
  • 41 thesisC.Chaithya Giliyar Radhakrishna. Designing and learning non-Cartesian k-space sampling trajectories for accelerated 3D MRI.Université Paris-SaclayApril 2023HAL
  • 42 thesisB.Benoît Malézieux. A study of unrolled algorithms for dictionary learning and inverse problems, and contributions to M/EEG signal processing.Université Paris-SaclaySeptember 2023HAL
  • 43 thesisA.Alexandre Pasquiou. Deciphering the neural bases of language comprehension using latent linguistic representations.Université Paris-SaclayJune 2023HAL
  • 44 thesisG. E.Gaston Ezequiel Zanitti. Development of a probabilistic domain-specific language for brain connectivity including heterogeneous knowledge representation.Université Paris-SaclayMarch 2023HAL

Reports & preprints

  • 45 miscZ.Zaineb Amor, P.Philippe Ciuciu, C.Chaithya Giliyar Radhakrishna, G.Guillaume Daval-Frérot, F.Franck Mauconduit, B.Bertrand Thirion and A.Alexandre Vignaud. Non-cartesian 3D-SPARKLING vs cartesian 3D-EPI encoding schemes for functional magnetic resonance Imaging at 7 Tesla.December 2023HAL
  • 46 reportF.Frédéric Blanqui, A.Anne Canteaut, H.Hidde de Jong, S.Sébastien Imperiale, N.Nathalie Mitton, G.Guillaume Pallez, X.Xavier Pennec, X.Xavier Rival and B.Bertrand Thirion. Recommandations sur les « éditeurs de la zone grise ».InriaJanuary 2023, 1-3HAL
  • 47 reportF.Frédéric Blanqui, A.Anne Canteaut, H. D.Hidde De Jong, S.Sébastien Imperiale, N.Nathalie Mitton, G.Guillaume Pallez, X.Xavier Pennec, X.Xavier Rival and B.Bertrand Thirion. Recommendations on "Grey-Zone Publishers": Recommendations from the Inria Evaluation Committee, translated from https://hal.inria.fr/hal-04001505.InriaJanuary 2023, 1-3HAL
  • 48 miscM.Mathieu Dagréou, T.Thomas Moreau, S.Samuel Vaiter and P.Pierre Ablin. A lower bound and a near-optimal algorithm for bilevel empirical risk minimization.2023HALDOI
  • 49 miscH.Ha Nguyen, J.Javier Albayay, R.Richard Höchenberger, S.Surabhi Bhutani, S.Sanne Boesveldt, N. A.Niko A. Busch, I.Ilja Croijmans, K. W.Keiland W. Cooper, J. H.Jasper H. B. de Groot, M. C.Michael C. Farruggia, A. W.Alexander W. Fjaeldstad, J. E.John E. Hayes, T.Thomas Hummel, P. V.Paule V. Joseph, T. K.Tatiana K. Laktionova, T.Thierry Thomas-Danguin, M. G.Maria G. Veldhuizen, V. V.Vera V. Voznessenskaya, V.Valentina Parma, M. Y.M. Yanina Pepino and K.Kathrin Ohla. Covid-19 affects taste independently of smell: results from a combined chemosensory home test and online survey from a global cohort (N=10,953).January 2023HALDOI
  • 50 miscA. L.Ana Luísa Pinho, H.Hugo Richard, M.Michael Eickenberg, A.Alexis Amadon, E.Elvis Dohmatob, I.Isabelle Denghien, J. J.Juan Jesús Torre, S.Swetha Shankar, H.Himanshu Aggarwal, A. F.Ana Fernanda Ponce, A.Alexis Thual, T.Thomas Chapalain, C.Chantal Ginisty, S.Séverine Becuwe-Desmidt, S.Séverine Roger, Y.Yann Lecomte, V.Valérie Berland, L.Laurence Laurier, V.Véronique Joly-Testault, G.Gaëlle Médiouni-Cloarec, C.Christine Doublé, B.Bernadette Martins, G.Gaël Varoquaux, S.Stanislas Dehaene, L.Lucie Hertz-Pannier and B.Bertrand Thirion. Individual Brain Charting third release, probing brain activity during Movie Watching and Retinotopic Mapping.November 2023HAL
  • 51 miscR. A.Russell A. Poldrack, C. J.Christopher J. Markiewicz, S.Stefan Appelhoff, Y. K.Yoni K. Ashar, T.Tibor Auer, S.Sylvain Baillet, S.Shashank Bansal, L.Leandro Beltrachini, C. G.Christian G. Benar, G.Giacomo Bertazzoli, S.Suyash Bhogawar, R. W.Ross W. Blair, M.Marta Bortoletto, M.Mathieu Boudreau, T. L.Teon L. Brooks, V. D.Vince D. Calhoun, F. M.Filippo Maria Castelli, P.Patricia Clement, A. L.Alexander L Cohen, J.Julien Cohen-Adad, S.Sasha d'Ambrosio, G.Gilles de Hollander, M.María de la Iglesia-Vayá, A.Alejandro de la Vega, A.Arnaud Delorme, O.Orrin Devinsky, D.Dejan Draschkow, E. P.Eugene Paul Duff, E.Elizabeth Dupre, E.Eric Earl, O.Oscar Esteban, F. W.Franklin W. Feingold, G.Guillaume Flandin, A.Anthony Galassi, G.Giuseppe Gallitto, M.Melanie Ganz, R.Rémi Gau, J.James Gholam, S. S.Satrajit S. Ghosh, A.Alessio Giacomel, A. G.Ashley G Gillman, P.Padraig Gleeson, S.Samuel Guay, T.Tal Yarkoni, K. J.Krzysztof J. Gorgolewski, A.Alexandre Gramfort, G.Giacomo Guidali, Y. O.Yaroslav O. Halchenko, D. A.Daniel A. Handwerker, N.Nell Hardcastle, P.Peer Herholz, D.Dora Hermes, C. J.Christopher J. Honey, R. B.Robert B. Innis, H.-I.Horea-Ioan Ioanas, A.Andrew Jahn, A.Agah Karakuzu, D. B.David B. Keator, G.Gregory Kiar, B.Balint Kincses, A. R.Angela R. Laird, J. C.Jonathan C. Lau, A.Alberto Lazari, J. H.Jon Haitz Legarreta, A.Adam Li, X.Xiangrui Li, B. C.Bradley C. Love, H.Hanzhang Lu, G.Giacomo Mazzamuto, S.Sajjad Torabian, B.Bradley Voytek, J.Julius Welzel, M.Martin Wilson, C.Camille Maumet, S. L.Steven L. Meisler, M.Mark Mikkelsen, H.Henk Mutsaerts, T. E.Thomas E. Nichols, A.Aki Nikolaidis, G.Gustav Nilsonne, G.Guiomar Niso, M.Martin Norgaard, T. W.Thomas W Okell, R.Robert Oostenveld, E.Eduard Ort, P. J.Patrick J. Park, M.Mateusz Pawlik, C. R.Cyril R. Pernet, F.Franco Pestilli, J.Jan Petr, C.Christophe Phillips, J.-B.Jean-Baptiste Poline, L.Luca Pollonini, P. R.Pradeep Reddy Raamana, P.Petra Ritter, G.Gaia Rizzo, K. A.Kay A. Robbins, A. P.Alexander P. Rockhill, C.Christine Rogers, A.Ariel Rokem, C.Chris Rorden, J. M.Jose Manuel Saborit-Torres, A. M.Alexandre M Routier, R. E.Robert E. Smith, T.Tamas Spisak, J.Julia Sprenger, N. C.Nicole C. Swann, M.Martin Szinte, S.Sylvain Takerkart, B.Bertrand Thirion, A. G.Adam G. Thomas, T.Taylor Salo, M.Michael Schirner and G.Gaël Varoquaux. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS).September 2023HAL
  • 52 miscR.Renata Porciuncula Baptista, M.Mathieu Naudin, C.Chaithya Giliyar Radhakrishna, G.Guillaume Daval-Frérot, F.Franck Mauconduit, A.Alexa Haeger, S.Sandro Romanzetti, M.Marc Lapert, P.Philippe Ciuciu, C.Cécile Rabrait-Lerman, R.Remy Guillevin, A.Alexandre Vignaud and F.Fawzi Boumezbeur. Accelerated sodium MRI using undersampled 3D SPARKLING at 7T.January 2023HAL
  • 53 miscH.Hugo Richard, E.Etienne Boursier and V.Vianney Perchet. Constant or logarithmic regret in asynchronous multiplayer bandits.May 2023HAL
  • 54 miscM.M Roascio, S.Sh Wang, V.V Myrov, F.F Siebenhühner, R.R. Tro, P.P. Mattioli, F.F. Famà, S.S. Morbelli, M.M. Pardini, J.Jm Palva, D.D Arnaldi and G.G. Arnulfo. Altered Brain Dynamics in idiopathic REM sleep behavior disorder: Implications for a continuum from prodromal to overt alpha-synucleinopathies.May 2023HALDOI
  • 55 miscM.Matthieu Terris and T.Thomas Moreau. Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers.November 2023HAL
  • 56 miscM.Matthieu Terris, T.Thomas Moreau, N.Nelly Pustelnik and J.Julian Tachella. Equivariant plug-and-play image reconstruction.December 2023HAL
  • 57 miscA.Alexis Thual, H.Huy Tran, T.Tatiana Zemskova, N.Nicolas Courty, R.Rémi Flamary, S.Stanislas Dehaene and B.Bertrand Thirion. Aligning individual brains with Fused Unbalanced Gromov-Wasserstein.2022HALDOI
  • 58 miscS.Sheng Wang, G.Gabriele Arnulfo, L.Lino Nobili, V.Vladislav Myrov, P.Paul Ferrari, P.Philippe Ciuciu, S.Satu Palva and J. M.J Matias Palva. Neuronal Synchrony and Critical Bistability: Mechanistic Biomarkers for Localizing the Epileptogenic Network.May 2023HALDOI
  • 59 miscH.-T.Hao-Ting Wang, S. L.Steven L Meiseler, H.Hanad Sharmarke, N.Natasha Clarke, N.Nicolas Gensollen, C. J.Christopher J Markiewicz, F.François Paugam, B.Bertrand Thirion and P.Pierre Bellec. Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn.April 2023HALDOI

Other scientific publications

  • 60 inproceedingsS. H.Sheng H. Wang, P.Paul Ferrari, A.Angel Hernandez, G.Gabriele Arnulfo, L.Lino Nobili, S.Satu Palva, P.Philippe Ciuciu and J. M.J Matias Palva. Improving epileptogenic zone (EZ) classification by combining brain criticality and connectivity features.International Society for the Advancement of Clinical MagnetoencephalographyOsaka (JP), JapanMay 2023HAL
  • 61 inproceedingsS. H.Sheng H Wang, M.Morgane Marzulli, P.Paul Ferrari, G.Gabriele Arnulfo, L.Lino Nobili, S.Satu Palva, P.Philippe Ciuciu and J. M.J Matias Palva. A Low-dimensional surface in Eigen Feature Space Characterizes an Interictal Continuum.American Epilepsy Society ConferenceOrlando (FL), United StatesDecember 2023HAL

12.3 Cited publications

  • 62 inproceedings M.Mart\i Abadi. back to text
  • 63 articleP.Pierre Ablin, J.-F.Jean-Francois Cardoso and A.Alexandre Gramfort. Faster independent component analysis by preconditioning with Hessian approximations.IEEE Trans. Signal Process.66152018, 4040-4049back to text
  • 64 inproceedingsP.Pierre Ablin, T.Thomas Moreau, M.Mathurin Massias and A.Alexandre Gramfort. Learning Step Sizes for Unfolded Sparse Coding.Advances in Neural Information Processing Systems (NeurIPS)Vancouver, BC, Canada2019, 13100--13110back to textback to text
  • 65 inproceedingsP.Pierre Ablin, G.Gabriel Peyré and T.Thomas Moreau. Super-Efficiency of Automatic Differentiation for Functions Defined as a Minimum.International Conference on Machine Learning (ICML)July 2020back to text
  • 66 inproceedingsA.Ahmed Alaa and M.Mihaela Van Der Schaar. Validating Causal Inference Models via Influence Functions.Proceedings of the 36th International Conference on Machine Learning97Proceedings of Machine Learning ResearchLong Beach, California, USAPMLR09--15 Jun 2019, 191--201URL: http://proceedings.mlr.press/v97/alaa19a.htmlback to text
  • 67 articleM.Marcelo Arenas, G.Georg Gottlob and a.as Pieris. Expressive Languages for Querying the Semantic Web.ACM Transactions on Database Systems433November 2018, 1--45DOIback to text
  • 68 articleS.Susan Athey and G.Guido Imbens. The State of Applied Econometrics - Causality and Policy Evaluation.ArXiv e-printsJuly 2016, arXiv:1607.00699back to text
  • 69 inproceedingsS.Sergul Aydore, B.Bertrand Thirion and G.Gael Varoquaux. Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data.ICML2019, 385--394back to text
  • 70 articleV.Vince BáRány, B. T.Balder Ten Cate, B.Benny Kimelfeld, D.Dan Olteanu and Z.Zografoula Vagena. Declarative Probabilistic Programming with Datalog.ACM Transactions on Database Systems424October 2017, 1-35DOIback to text
  • 71 articleT.T. Baltrušaitis, C.C. Ahuja and L.L. Morency. Multimodal Machine Learning: A Survey and Taxonomy.IEEE Transactions on Pattern Analysis and Machine Intelligence412Feb 2019, 423-443DOIback to textback to text
  • 72 articleH.Hubert Banville, O.Omar Chehab, A.Aapo Hyvarinen, D.Denis Engemann and A.Alexandre Gramfort. Uncovering the structure of clinical EEG signals with self-supervised learning.Journal of Neural Engineering2020back to textback to text
  • 73 articleL.Luigi Bellomarini, E.Emanuel Sallinger and G.Georg Gottlob. The Vadalog System: Datalog-Based Reasoning for Knowledge Graphs.Proceedings of the VLDB Endowment119May 2018, 975-987DOIback to textback to text
  • 74 articleY.Y. Bengio, A.A. Courville and P.P. Vincent. Representation learning: A review and new perspectives.Pattern Analysis and Machine Intelligence352013, 1798back to text
  • 75 articleY.Yoav Benjamini and Y.Yosef Hochberg. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.Journal of the Royal Statistical Society Series B (Methodological)5711995, 289-300URL: http://dx.doi.org/10.2307/2346101DOIback to text
  • 76 inproceedingsQ.Quentin Bertrand, Q.Quentin Klopfenstein, M.Mathieu Blondel, S.Samuel Vaiter, A.Alexandre Gramfort and J.Joseph Salmon. Implicit Differentiation of Lasso-Type Models for Hyperparameter Optimization.International Conference on Machine Learning (ICML)2002.08943onlineApril 2020, 3199--3210back to textback to text
  • 77 articleG.Gilles Blanchard, P.Pierre Neuvial and E.Etienne Roquain. Post hoc confidence bounds on false positives using reference families.Ann. Statist.48306 2020, 1281--1303URL: https://doi.org/10.1214/19-AOS1847DOIback to textback to textback to text
  • 78 phdthesisM.Martin Bompaire. Machine learning based on Hawkes processes and stochastic optimization.Université Paris Saclay (COmUE)CMAP, École Polytechnique2019back to text
  • 79 articleC.Claire Boyer, N.Nicolas Chauffert, P.Philippe Ciuciu, J.Jonas Kahn and P.Pierre Weiss. On the generation of sampling schemes for magnetic resonance imaging.SIAM Journal on Imaging Sciences942016, 2039--2072back to text
  • 80 articleE.Emmanuel Candès, Y.Yingying Fan, L.Lucas Janson and J.Jinchi Lv. Panning for gold: ‘model-X’ knockoffs for high dimensional controlled variable selection.Journal of the Royal Statistical Society: Series B (Statistical Methodology)8032018, 551-577URL: https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12265DOIback to text
  • 81 articleL.Lotfi Chaari, P.Philippe Ciuciu, S.Sébastien Mériaux and J.-C.Jean-Christophe Pesquet. Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI.Magnetic Resonance Materials in Physics, Biology and Medicine2762014, 509--529back to text
  • 82 articleG.GR Chaithya, P.Pierre Weiss, A.Aurélien Massire, A.Alexandre Vignaud and P.Philippe Ciuciu. Globally optimized 3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance Imaging.2020back to textback to text
  • 83 inproceedingsS.S. Chambon, V.V. Thorey, P. J.P. J. Arnal, E.E. Mignot and A.A. Gramfort. A Deep Learning Architecture to Detect Events in EEG Signals During Sleep.2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)Sept 2018, 1-6DOIback to text
  • 84 articleN.Nicolas Chauffert, P.Pierre Weiss, J.Jonas Kahn and P.Philippe Ciuciu. A projection algorithm for gradient waveforms design in Magnetic Resonance Imaging.IEEE Transactions on Medical Imaging3592016, 2026--2039back to text
  • 85 articleL.Lang Chen, D.Demian Wassermann, D. A.Daniel A. Abrams, J.John Kochalka, G.Guillermo Gallardo-Diez and V.Vinod Menon. The Visual Word Form Area (VWFA) Is Part of Both Language and Attention Circuitry.Nature Communications101December 2019, 5601DOIback to text
  • 86 inproceedingsH.Hamza Cherkaoui, L.Loubna El Gueddari, C.Carole Lazarus, A.Antoine Grigis, F.Fabrice Poupon, A.Alexandre Vignaud, S.Sammuel Farrens, J.-L.J-L Starck and P.Philippe Ciuciu. Analysis vs synthesis-based regularization for combined compressed sensing and parallel MRI reconstruction at 7 Tesla.2018 26th European Signal Processing Conference (EUSIPCO)IEEE2018, 36--40back to text
  • 87 articleH.H. Cherkaoui, T.T. Moreau, A.A. Halimi, C.C. Leroy and P.P. Ciuciu. Multivariate semi-blind deconvolution of fMRI time series.revised for publication to NeuroImageApril 2021back to textback to text
  • 88 inproceedingsH.Hamza Cherkaoui, J.Jeremias Sulam and T.Thomas Moreau. Learning to Solve TV Regularised Problems with Unrolled Algorithms.Advances in Neural Information Processing Systems (NeurIPS)online2020back to text
  • 89 inproceedingsJ.-A.Jérôme-Alexis Chevalier, J.Joseph Salmon and B.Bertrand Thirion. Statistical Inference with Ensemble of Clustered Desparsified Lasso.MICCAIGrenade, Spain2018HALback to text
  • 90 articleJ. H.James H Cole, R.Robert Leech, D. J.David J Sharp and A. D.Alzheimer's Disease Neuroimaging Initiative. Prediction of brain age suggests accelerated atrophy after traumatic brain injury.Annals of neurology7742015, 571--581back to text
  • 91 inproceedingsR.Ronan Collobert and J.Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning.ICML2008, 160back to textback to text
  • 92 inproceedingsE. D.Ekin D. Cubuk, B.Barret Zoph, J.Jonathon Shlens and Q. V.Quoc V. Le. Randaugment: Practical automated data augmentation with a reduced search space.IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Seattle, WA, USAIEEEJune 2020, 3008--3017URL: https://ieeexplore.ieee.org/document/9150790/DOIback to text
  • 93 unpublishedK.Kamalaker Dadi, G.Gael Varoquaux, J.Josselin Houenou, D.Danilo Bzdok, B.Bertrand Thirion and D.Denis Engemann. Beyond brain age: Empirically-derived proxy measures of mental health.October 2020, working paper or preprintHALDOIback to text
  • 94 bookJ. M.John M. Danskin. Theory of Max-Min and Its Application to Weapons Allocation Problems..OCLC: 953666019Berlin/HeidelbergSpringer Berlin Heidelberg1967back to text
  • 95 inproceedingsG.G. Daval-Frérot, A.A. Massire, M.M. Ripart, B.B. Mailhe, M.M. Nadar, A.A. Vignaud and P.P. Ciuciu. Off-resonance correction non-Cartesian SWI using internal field map estimation.29th Proc. of the ISMRM annual meetingvirtualMay 2021back to text
  • 96 incollectionJ.Jia Deng, A. C.Alexander C Berg, K.Kai Li and L.Li Fei-Fei. What does classifying more than 10,000 image categories tell us?ECCV2010, 71back to text
  • 97 articleJ.Jacob Devlin, M.-W.Ming-Wei Chang, K.Kenton Lee and K.Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.CoRRabs/1810.048052018, URL: http://arxiv.org/abs/1810.04805back to text
  • 98 articleE.Elvis Dohmatob, H.Hugo Richard, A. L.Ana Lu\'isa Pinho and B.Bertrand Thirion. Brain topography beyond parcellations: local gradients of functional maps.NeuroImageJanuary 2021, 117706HALDOIback to text
  • 99 articleN. U.Nico UF Dosenbach, B.Binyam Nardos, A. L.Alexander L Cohen, D. A.Damien A Fair, J. D.Jonathan D Power, J. A.Jessica A Church, S. M.Steven M Nelson, G. S.Gagan S Wig, A. C.Alecia C Vogel, C. N.Christina N Lessov-Schlaggar and others. Prediction of individual brain maturity using fMRI.Science32959972010, 1358--1361back to text
  • 100 inproceedingsT.Tom Dupré la Tour, T.Thomas Moreau, M.Mainak Jas and A.Alexandre Gramfort. Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals.Advances in Neural Information Processing Systems (NeurIPS)Montreal, Canada2018, 3296--3306back to text
  • 101 inproceedingsL.Loubna El Gueddari, E.Emilie Chouzenoux, A.Alexandre Vignaud, J.-C.Jean-Christophe Pesquet and P.Philippe Ciuciu. Online MR image reconstruction for compressed sensing acquisition in T2* imaging.Wavelets and Sparsity XVIII11138International Society for Optics and Photonics2019, 1113819back to text
  • 102 articleL.Loubna El Gueddari, C.Chaithya Giliyar Radhakrishna, E.Emilie Chouzenoux and P.Philippe Ciuciu. Calibration-Less Multi-Coil Compressed Sensing Magnetic Resonance Image Reconstruction Based on OSCAR Regularization.Journal of Imaging732021, 58back to text
  • 103 inproceedingsL.Loubna El Gueddari, C.Carole Lazarus, H.Hanaé Carrié, A.Alexandre Vignaud and P.Ph Ciuciu. Self-calibrating nonlinear reconstruction algorithms for variable density sampling and parallel reception MRI.2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM)IEEE2018, 415--419back to text
  • 104 articleD. A.Denis A Engemann, O.Oleh Kozynets, D.David Sabbagh, G.Guillaume Lemaître, G.Gael Varoquaux, F.Franziskus Liem and A.Alexandre Gramfort. Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers.eLife9may 2020, e54055URL: https://doi.org/10.7554/eLife.54055DOIback to textback to text
  • 105 articleD. A.Denis A Engemann, F.Federico Raimondo, J.-R.Jean-Rémi King, B.Benjamin Rohaut, G.Gilles Louppe, F.Frédéric Faugeras, J.Jitka Annen, H.Helena Cassol, O.Olivia Gosseries, D.Diego Fernandez-Slezak and others. Robust EEG-based cross-site and cross-protocol classification of states of consciousness.Brain141112018, 3179--3192back to text
  • 106 articleS.S Farrens, A.A Grigis, L.L El Gueddari, Z.Z Ramzi, G.GR Chaithya, S.S Starck, B.B Sarthou, H.H Cherkaoui, P.P Ciuciu and J.-L.J-L Starck. PySAP: Python Sparse Data Analysis Package for Multidisciplinary Image Processing.Astronomy and Computing322020, 100402back to textback to text
  • 107 articleC.Chaithya Giliyar Radhakrishna, P.Pierre Weiss, G.Guillaume Daval-Frérot, A.Aurélien Massire, A.Alexandre Vignaud and P.Philippe Ciuciu. Optimizing full 3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance Imaging.IEEE Transactions on Medical ImagingAugust 2022HALback to text
  • 108 articleD.Davis Gilton, G.Gregory Ongie and R.Rebecca Willett. Deep Equilibrium Architectures for Inverse Problems in Imaging.arXiv preprint arXiv:2102.079442021back to text
  • 109 articleJ.Julie Gonneaud, A. T.Alex T Baria, A. P.Alexa Pichet Binette, B. A.Brian A Gordon, J. P.Jasmeer P Chhatwal, C.Carlos Cruchaga, M.Mathias Jucker, J.Johannes Levin, S.Stephen Salloway, M.Martin Farlow and others. Functional brain age prediction suggests accelerated aging in preclinical familial Alzheimer's disease, irrespective of fibrillar amyloid-beta pathology.bioRxiv2020back to text
  • 110 inproceedingsK.Karol Gregor and Y.Yann Lecun. Learning Fast Approximations of Sparse Coding.Proceedings of the 27th International Conference on Machine Learning2010back to text
  • 111 articleA. G.Alan G Hawkes. Point spectra of some mutually exciting point processes.Journal of the Royal Statistical Society: Series B (Methodological)3331971, 438--443back to text
  • 112 articleA. A.Andrés A Hoyos-Idrobo, G.Gaël Varoquaux, Y.Yannick Schwartz and B.Bertrand Thirion. FReM -- scalable and stable decoding with fast regularized ensemble of models.NeuroImage2017, 1-16HALDOIback to textback to text
  • 113 inproceedingsV.Valentin Iovene, G.Gaston Zanitti and D.Demian Wassermann. Complex Coordinate-Based Meta-Analysis with Probabilistic Programming.Association for the Advancement of Artificial IntelligenceOnline, FranceFebruary 2021HALback to text
  • 114 inproceedingsM.Maëliss Jallais, P. L.Pedro Luiz Coelho Rodrigues, A.Alexandre Gramfort and D.Demian Wassermann. Cytoarchitecture Measurements in Brain Gray Matter using Likelihood-Free Inference.June 2021HALback to textback to text
  • 115 inproceedingsH.H. Janati, T.T. Bazeille, B.B. Thirion, M.M. Cuturi and A.A. Gramfort. Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates.Information Processing in Medical ImagingChamSpringer International Publishing2019, 743--754back to text
  • 116 inproceedingsH.Hicham Janati, M.Marco Cuturi and A.Alexandre Gramfort. Wasserstein regularization for sparse multi-task regression.AISTATS89Proceedings of Machine Learning ResearchPMLRApril 2019, 1407--1416URL: http://proceedings.mlr.press/v89/janati19a.htmlback to text
  • 117 inproceedingsM.Mainak Jas, T.Tom Dupré la Tour, U.Umut Şimşekli and A.Alexandre Gramfort. Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding.Advances in Neural Information Processing Systems (NeurIPS)Long Beach, CA, USA2017, 1099--1108back to text
  • 118 articleA.Adel Javanmard and A.Andrea Montanari. Confidence Intervals and Hypothesis Testing for High-Dimensional Regression.151January 2014, 2869–2909back to text
  • 119 unpublishedJ.Julie Josse, N.Nicolas Prost, E.Erwan Scornet and G.Gaël Varoquaux. On the consistency of supervised learning with missing values.March 2019, working paper or preprintHALback to text
  • 120 articleS.S. Kapur, A. G.A. G. Phillips and T. R.T. R. Insel. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?Molecular Psychiatry17122012, 1174--1179back to text
  • 121 articleJ.-R.J-R King, L.L Gwilliams, C.C Holdgraf, J.J Sassenhagen, A.A Barachant, D.D Engemann, E.E Larson and A.A Gramfort. Encoding and Decoding Framework to Uncover the Algorithms of Cognition.The Cognitive Neurosciences VI, MIT Pressin press2020back to text
  • 122 articleE.Elnaz Lashgari, D.Dehua Liang and U.Uri Maoz. Data augmentation for deep-learning-based electroencephalography.Journal of Neuroscience Methods3462020, 108885URL: https://www.sciencedirect.com/science/article/pii/S0165027020303083DOIback to text
  • 123 articleC.Carole Lazarus, P.Pierre Weiss, N.Nicolas Chauffert, F.Franck Mauconduit, L.Loubna El Gueddari, C.Christophe Destrieux, I.Ilyess Zemmoura, A.Alexandre Vignaud and P.Philippe Ciuciu. SPARKLING: variable-density k-space filling curves for accelerated T2*-weighted MRI.Magnetic Resonance in Medicine8162019, 3643--3661back to textback to textback to text
  • 124 articleC.Carole Lazarus, P.Pierre Weiss, L.Loubna Gueddari, F.Franck Mauconduit, A.Aurélien Massire, M.Mathile Ripart, A.Alexandre Vignaud and P.Philippe Ciuciu. 3D variable-density SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance imaging.NMR in Biomedicine33e43492020, 1--12back to textback to textback to text
  • 125 inproceedingsH. K.H. K. Lee, J.-H.J.-H. Lee, J.-O.J.-O. Park and Y.-S.Y.-S. Choi. Data-driven Data Augmentation for Motor Imagery Brain-Computer Interface.2021 International Conference on Information Networking (ICOIN)ISSN: 1976-7684January 2021, 683--686DOIback to text
  • 126 articleF.Franziskus Liem, G.Gaël Varoquaux, J.Jana Kynast, F.Frauke Beyer, S. K.Shahrzad Kharabian Masouleh, J. M.Julia M Huntenburg, L.Leonie Lampe, M.Mehdi Rahim, A.Alexandre Abraham, R. C.R Cameron Craddock and others. Predicting brain-age from multimodal imaging data captures cognitive impairment.NeuroImage1482017, 179--188back to text
  • 127 miscM.Molei Liu, E.Eugene Katsevich, L.Lucas Janson and A.Aaditya Ramdas. Fast and Powerful Conditional Randomization Testing via Distillation.2020back to textback to text
  • 128 articleF.F. Lotte. Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain-Computer Interfaces.Proc. of the IEEE10362015, 871-890back to text
  • 129 articleC.Christos Louizos, U.Uri Shalit, J.Joris Mooij, D.David Sontag, R.Richard Zemel and M.Max Welling. Causal Effect Inference with Deep Latent-Variable Models.arXiv e-printsMay 2017, arXiv:1705.08821back to text
  • 130 articleJ.Julien Mairal, F. R.Francis R. Bach, J.Jean Ponce and G.Guillermo Sapiro. Online Learning for Matrix Factorization and Sparse Coding.Journal of Machine Learning Research (JMLR)1112010, 19--60back to text
  • 131 inproceedingsM.M. Massias, A.A. Gramfort and J.J. Salmon. Celer: a Fast Solver for the Lasso with Dual Extrapolation.Proceedings of the 35th International Conference on Machine Learning802018, 3321--3330URL: https://arxiv.org/abs/1802.07481back to text
  • 132 articleM.Mathurin Massias, S.Samuel Vaiter, A.Alexandre Gramfort and J.Joseph Salmon. Dual Extrapolation for Sparse GLMs.Journal of Machine Learning Research212342020, 1-33URL: http://jmlr.org/papers/v21/19-587.htmlback to text
  • 133 articleV.Vinod Menon, G.Guillermo Gallardo, M. A.Mark A Pinsk, V.-D.Van-Dang Nguyen, J.-R.Jing-Rebecca Li, W.Weidong Cai and D.Demian Wassermann. Microstructural Organization of Human Insula Is Linked to Its Macrofunctional Circuitry and Predicts Cognitive Control.eLife9June 2020, e53470DOIback to text
  • 134 inproceedingsA.Arthur Mensch, J.Julien Mairal, D.Danilo Bzdok, B.Bertrand Thirion and G.Gaël Varoquaux. Learning Neural Representations of Human Cognition across Many fMRI Studies.NIPSLong Beach, United StatesDecember 2017HALback to text
  • 135 articleA.Arthur Mensch, J.Julien Mairal, B.Bertrand Thirion and G.Gael Varoquaux. Stochastic Subsampling for Factorizing Huge Matrices.IEEE Transactions on Signal Processing661January 2018, 113--128back to text
  • 136 inproceedingsI.Ishan Misra, C. L.C Lawrence Zitnick and M.Martial Hebert. Shuffle and learn: unsupervised learning using temporal order verification.ECCVSpringer2016, 527--544back to text
  • 137 articleT.Thomas Moreau and A.Alexandre Gramfort. DiCoDiLe: Distributed Convolutional Dictionary Learning.Transaction on Pattern Analysis and Machine Intelligence (TPAMI)in press2020back to textback to text
  • 138 inproceedingsT.Thomas Moreau, L.Laurent Oudre and N.Nicolas Vayatis. DICOD: Distributed Convolutional Sparse Coding.International Conference on Machine Learning (ICML)Stockohlm, SwedenPMLR (80)2018, 3626--3634back to textback to text
  • 139 articleM. J.Matthew J Muckley, B.Bruno Riemenschneider, A.Alireza Radmanesh, S.Sunwoo Kim, G.Geunu Jeong, J.Jingyu Ko, Y.Yohan Jun, H.Hyungseob Shin, D.Dosik Hwang, M.Mahmoud Mostapha and others. Results of the 2020 fastMRI challenge for machine learning mr image reconstruction.IEEE transactions on medical imaging4092021, 2306--2317back to text
  • 140 articleS. A.Samuel A Neymotin, D. S.Dylan S Daniels, B.Blake Caldwell, R. A.Robert A McDougal, N. T.Nicholas T Carnevale, M.Mainak Jas, C. I.Christopher I Moore, M. L.Michael L Hines, M.Matti Hämäläinen and S. R.Stephanie R Jones. Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data.eLife9jan 2020, e51214URL: https://doi.org/10.7554/eLife.51214DOIback to text
  • 141 inproceedingsT.-B.Tuan-Binh Nguyen, J.-A.Jérôme-Alexis Chevalier, B.Bertrand Thirion and S.Sylvain Arlot. Aggregation of Multiple Knockoffs.ICML 2020 - 37th International Conference on Machine LearningProceedings of the ICML 37th International Conference on Machine Learning,119Vienne / Virtual, AustriaJuly 2020HALback to text
  • 142 inproceedingsT.-B.Tuan-Binh Nguyen, J.-A.Jérôme-Alexis Chevalier and B.Bertrand Thirion. ECKO: Ensemble of Clustered Knockoffs for robust multivariate inference on MRI data.IPMIJune 2019HALback to text
  • 143 articleX.Xinkun Nie and S.Stefan Wager. Quasi-Oracle Estimation of Heterogeneous Treatment Effects.ArXiv e-printsDecember 2017, arXiv:1712.04912back to text
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  • 149 inproceedingsA.Adam Paszke, S.Sam Gross, F.Francisco Massa, A.Adam Lerer, J.James Bradbury, G.Gregory Chanan, T.Trevor Killeen, Z.Zeming Lin, N.Natalia Gimelshein, L.Luca Antiga, A.Alban Desmaison, A.Andreas Kopf, E.Edward Yang, Z.Zachary DeVito, M.Martin Raison, A.Alykhan Tejani, S.Sasank Chilamkurthy, B.Benoit Steiner, L.Lu Fang, J.Junjie Bai and S.Soumith Chintala. PyTorch: An Imperative Style, High-Performance Deep Learning Library.Advances in Neural Information Processing Systems (NeurIPS)Vancouver, BC, Canada2019, 12back to text
  • 150 articleL.Luis Pineda and others. Active MR k-space Sampling with Reinforcement Learning.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)12262 LNCS2020, 23--33back to text
  • 151 articleA. L.Ana Lu\'isa Pinho, A.Alexis Amadon, M.Murielle Fabre, E.Elvis Dohmatob, I.Isabelle Denghien, J. J.Juan Jesús Torres, C.Chantal Ginisty, S.Séverine Becuwe-Desmidt, S.Séverine Roger, L.Laurence Laurier, V.Véronique Joly-Testault, G.Gaëlle Médiouni-Cloarec, C.Christine Doublé, B.Bernadette Martins, P.Philippe Pinel, E.Evelyn Eger, G.Gaël Varoquaux, C. C.Christophe C Pallier, S.Stanislas Dehaene, L.Lucie Hertz-Pannier and B.Bertrand Thirion. Subject-specific segregation of functional territories based on deep phenotyping.Human Brain MappingDecember 2020HALDOIback to text
  • 152 articleA. L.Ana Lu\'isa Pinho, A.Alexis Amadon, B.Baptiste Gauthier, N.Nicolas Clairis, A.André Knops, S.Sarah Genon, E.Elvis Dohmatob, J.Juan Jesús Torre, C.Chantal Ginisty, S.Séverine Becuwe-Desmidt, S.Séverine Roger, Y.Yann Lecomte, V.Valérie Berland, L.Laurence Laurier, V.Véronique Joly-Testault, G.Gaëlle Médiouni-Cloarec, C.Christine Doublé, B.Bernadette Martins, E.Eric Salmon, M.Manuela Piazza, D.David Melcher, M.Mathias Pessiglione, V.Virginie Van Wassenhove, E.Evelyn Eger, G.Gaël Varoquaux, S.Stanislas Dehaene, L.Lucie Hertz-Pannier and B.Bertrand Thirion. Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping.Scientific Data71October 2020HALDOIback to text
  • 153 inproceedingsZ.Zaccharie Ramzi, P.Philippe Ciuciu and J.-L.Jean-Luc Starck. Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction.ISBI 2021 - International Symposium on Biomedical Imaging2021, URL: http://arxiv.org/abs/2101.01570back to textback to text
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