The Parietal team is localized in two places: in the Alan Turing building of Inria Saclay, and the Neurospin building of CEA Saclay.
The Parietal team focuses on mathematical methods for modeling and statistical inference based on neuroimaging data, with a particular interest in machine learning techniques and applications of human functional imaging. This general theme splits into four research axes:
Modeling for neuroimaging population studies,
Encoding and decoding models for cognitive imaging,
Statistical and machine learning methods for large-scale data,
Compressed-sensing for MRI.
Parietal is also strongly involved in open-source software development in scientific Python (machine learning) and for neuroimaging applications.
Many problems in neuroimaging can be framed as forward and inverse problems. For instance, brain population imaging is concerned with the inverse problem that consists in predicting individual information (behavior, phenotype) from neuroimaging data, while the corresponding forward problem boils down to explaining neuroimaging data with the behavioral variables. Solving these problems entails the definition of two terms: a loss that quantifies the goodness of fit of the solution (does the model explain the data well enough ?), and a regularization scheme that represents a prior on the expected solution of the problem. These priors can be used to enforce some properties on the solutions, such as sparsity, smoothness or being piece-wise constant.
Let us detail the model used in typical inverse problem: Let
where the vector contains
where
with
When
Total Variation regularization (see Fig. ) is obtained for
(
Smooth lasso is obtained with (
Note that, while the qualitative aspect of the solutions are very different, the predictive power of these models is often very close.
The performance of the predictive model can simply be evaluated as the
amount of variance in
This framework is easily extended by considering
Grouped penalization, where the penalization explicitly includes a prior clustering of the features, i.e. voxel-related signals, into given groups. This amounts to enforcing structured priors on the problem solution.
Combined penalizations, i.e. a mixture of simple and group-wise penalizations, that allow some variability to fit the data in different populations of subjects, while keeping some common constraints.
Logistic and hinge regression, where a non-linearity is applied to the linear model so that it yields a probability of classification in a binary classification problem.
Robustness to between-subject variability to avoid the
learned model overly reflecting a few outlying particular observations
of the training set. Note that noise and deviating assumptions can be
present in both
Multi-task learning: if several target variables
are thought to be related, it might be useful to constrain the
estimated parameter vector
For instance, when one of the variables
then
Multivariate decompositions provide a way to model complex data such as brain activation images: for instance, one might be interested in extracting an atlas of brain regions from a given dataset, such as regions exhibiting similar activity during a protocol, across multiple protocols, or even in the absence of protocol (during resting-state). These data can often be factorized into spatial-temporal components, and thus can be estimated through regularized Principal Components Analysis (PCA) algorithms, which share some common steps with regularized regression.
Let
where
where
The problem is not jointly convex in all the variables but each
penalization given in Eq () yields a convex problem on
Utlimately, the main limitations to these algorithms is the cost due to the memory requirements: holding datasets with large dimension and large number of samples (as in recent neuroimaging cohorts) leads to inefficient computation. To solve this issue, online method are particularly attractive.
Another important estimation problem stems from the general issue of learning the relationship between sets of variables, in particular their covariance. Covariance learning is essential to model the dependence of these variables when they are used in a multivariate model, for instance to study potential interactions between variables. Covariance learning is necessary to model latent interactions in high-dimensional observation spaces, e.g. when considering multiple contrasts or functional connectivity data.
The difficulties are two-fold: on the one hand, there is a shortage of data to learn a good covariance model from an individual subject, and on the other hand, subject-to-subject variability poses a serious challenge to the use of multi-subject data. While the covariance structure may vary from population to population, or depending on the input data (activation versus spontaneous activity), assuming some shared structure across problems, such as their sparsity pattern, is important in order to obtain correct estimates from noisy data. Some of the most important models are:
Sparse Gaussian graphical models, as they express meaningful conditional independence relationships between regions, and do improve conditioning/avoid overfit.
Decomposable models, as they enjoy good computational properties and enable intuitive interpretations of the network structure. Whether they can faithfully or not represent brain networks is still an open question.
PCA-based regularization of covariance which is powerful when modes of variation are more important than conditional independence relationships.
Adequate model selection procedures are necessary to achieve the right level of sparsity or regularization in covariance estimation; the natural evaluation metric here is the out-of-samples likelihood of the associated Gaussian model. Another essential remaining issue is to develop an adequate statistical framework to test differences between covariance models in different populations. To do so, we consider different means of parametrizing covariance distributions and how these parametrizations impact the test of statistical differences across individuals.
The brain as a highly structured organ, with both functional specialization and a complex newtork organization. While most of the knowledge historically comes from lesion studies and animal electophysiological recordings, the development of non-invasive imaging modalities, such as fMRI, has made it possible to study routinely high-level cognition in humans since the early 90's. This has opened major questions on the interplay between mind and brain , such as: How is the function of cortical territories constrained by anatomy (connectivity) ? How to assess the specificity of brain regions ? How can one characterize reliably inter-subject differences ?
Functional connectivity is defined as the interaction structure that is underlies brain function. Since the beginning of fMRI, it has been observed that remote regions sustain high correlation in their spontaneous activity, i.e. in the absence of a driving task. This means that the signals observed during resting-state define a signature of the connectivity of brain regions. The main interest of retsing-state fMRI is that it provides easy-to-acquire functional markers that have recently been proved to be very powerful for population studies.
While fMRI has been very useful in defining the function of regions at the mm scale, Magneto-encephalography (MEG) provides the other piece of the puzzle, namely temporal dynamics of brain activity, at the ms scale. MEG is also non-invasive. It makes it possible to keep track of precise schedule of mental operations and their interactions. It also opens the way toward a study of the rythmic activity of the brain. On the other hand, the localization of brain activity with MEG entails the solution of a hard inverse problem.
Functional Description
Mayavi is the most used scientific 3D visualization Python software. Mayavi can be used as a visualization tool, through interactive command line or as a library. It is distributed under Linux through Ubuntu, Debian, Fedora and Mandriva, as well as in PythonXY and EPD Python scientific distributions. Mayavi is used by several software platforms, such as PDE solvers (fipy, sfepy), molecule visualization tools and brain connectivity analysis tools (connectomeViewer).
Contact: Gaël Varoquaux
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.
Participants: Gaël Varoquaux, Bertrand Thirion, Loïc Estève, Alexandre Abraham, Michael Eickenberg, Alexandre Gramfort, Fabian Pedregosa Izquierdo, Elvis Dohmatob and Virgile Fritsch
Contact: Bertrand Thirion
Keywords: FMRI - Statistic analysis - Neurosciences - IRM - Brain - Health - Medical imaging
Functional Description
As part of fMRI data analysis, PyHRF provides a set of tools for addressing the two main issues involved in intra-subject fMRI data analysis : (i) the localization of cerebral regions that elicit evoked activity and (ii) the estimation of the activation dynamics also referenced to as the recovery of the Hemodynamic Response Function (HRF). To tackle these two problems, PyHRF implements the Joint Detection-Estimation framework (JDE) which recovers parcel-level HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps.
Participants: Thomas Vincent, Solveig Badillo, Lotfi Chaari, Christine Bakhous, Florence Forbes, Philippe Ciuciu, Laurent Risser, Thomas Perret and Aina Frau Pascual
Partners: CEA - NeuroSpin
Contact: Florence Forbes
URL: http://
Keywords: Classification - Learning - Clustering - Regession - Medical imaging
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.
Participants: Olivier Grisel, Gaël Varoquaux, Bertrand Thirion, Michael Eickenberg, Loïc Estève, Alexandre Gramfort, Arthur Mensch
Partners: CEA - Logilab - Nuxeo - Saint Gobain - Telecom Paris - Tinyclues
Contact: Olivier Grisel
Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising. Its applicability to large datasets has been addressed with online and randomized methods, that reduce the complexity in one of the matrix dimension, but not in both of them. In this paper, we tackle very large matrices in both dimensions. We propose a new factoriza-tion method that scales gracefully to terabyte-scale datasets, that could not be processed by previous algorithms in a reasonable amount of time. We demonstrate the efficiency of our approach on massive functional Magnetic Resonance Imaging (fMRI) data, and on matrix completion problems for recommender systems, where we obtain significant speed-ups compared to state-of-the art coordinate descent methods.
We propose a multivariate online dictionary-learning method for obtaining de-compositions of brain images with structured and sparse components (aka atoms). Sparsity is to be understood in the usual sense: the dictionary atoms are constrained to contain mostly zeros. This is imposed via an 1-norm constraint. By "struc-tured", we mean that the atoms are piece-wise smooth and compact, thus making up blobs, as opposed to scattered patterns of activation. We propose to use a Sobolev (Laplacian) penalty to impose this type of structure. Combining the two penalties, we obtain decompositions that properly delineate brain structures from functional images. This non-trivially extends the online dictionary-learning work of Mairal et al. (2010), at the price of only a factor of 2 or 3 on the overall running time. Just like the Mairal et al. (2010) reference method, the online nature of our proposed algorithm allows it to scale to arbitrarily sized datasets. Experiments on brain data show that our proposed method extracts structured and denoised dictionaries that are more intepretable and better capture inter-subject variability in small medium, and large-scale regimes alike, compared to state-of-the-art models.
Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. However, the state of the art, based on total variation or graph-net, is computationally costly. Here we introduce sparsity in the local neighborhood of each voxel with social-sparsity, a structured shrinkage operator. We find that, on brain imaging classification problems, social-sparsity performs almost as well as total-variation models and better than graph-net, for a fraction of the computational cost. It also very clearly outlines predictive regions. We give details of the model and the algorithm.
Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets increase sample sizes to compensate for this complexity, at the cost of uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to those face in realistic diagnostic applications. Here, we demonstrate the feasibility of inter-site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi-site autism dataset. For this purpose, we investigate pipelines that extract the most predictive biomarkers from the data. These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas. Connectomes are then compared across participants to learn patterns of connectivity that differentiate typical controls from individuals with autism. We predict this neuropsychiatric status for participants from the same acquisition sites or different, unseen, ones. Good choices of methods for the various steps of the pipeline lead to 67% prediction accuracy on the full ABIDE data, which is significantly better than previously reported results. We perform extensive validation on multiple subsets of the data defined by different inclusion criteria. These enables detailed analysis of the factors contributing to successful connectome-based prediction. First, prediction accuracy improves as we include more subjects, up to the maximum amount of subjects available. Second, the definition of functional brain areas is of paramount importance for biomarker discovery: brain areas extracted from large R-fMRI datasets outperform reference atlases in the classification tasks.
Convolutional networks used for computer vision represent candidate models for the computations performed in mammalian visual systems. We use them as a detailed model of human brain activity during the viewing of natural images by constructing predictive models based on their different layers and BOLD fMRI activations. Analyzing the predictive performance across layers yields characteristic fingerprints for each visual brain region: early visual areas are better described by lower level convolutional net layers and later visual areas by higher level net layers, exhibiting a progression across ventral and dorsal streams. Our predictive model generalizes beyond brain responses to natural images. We illustrate this on two experiments, namely retinotopy and face-place oppositions, by synthesizing brain activity and performing classical brain mapping upon it. The synthesis recovers the activations observed in the corresponding fMRI studies, showing that this deep encoding model captures representations of brain function that are universal across experimental paradigms.
Systems neuroscience has identified a set of canonical large-scale networks in humans. These have predominantly been characterized by resting-state analyses of the task-uncon-strained, mind-wandering brain. Their explicit relationship to defined task performance is largely unknown and remains challenging. The present work contributes a multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks. The method is validated in two extensive datasets (n = 500 and n = 81) by model-based generation of synthetic activity maps from recombination of shared network topographies. To study a use case, we formally revisited the poorly understood difference between neural activity underlying idling versus goal-directed behavior. We demonstrate that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks. The possibility of decomposing a mental task into the relative contributions of major brain networks, the "network co-occurrence architecture" of a given task, opens an alternative access to the neural substrates of human cognition.
Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomarker of neurodegenerative diseases, such as Alzheimer's disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference functional marker of AD is the fluorodeoxyglucose positron emission tomography (FDG-PET). It gives a reliable quantification of metabolic activity, but it is costly and invasive. Here, our goal is to analyze AD populations solely based on rs-fMRI, as functional connectivity is correlated to metabolism. We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is learned from an independent FDG-PET dataset to improve functional connectivity-based prediction of AD. The prior acts as a regularization of connectivity learning and improves the estimation of discriminative patterns from distinct rs-fMRI datasets. Our approach is a two-stage classification strategy that combines several seed-based connectivity maps to cover a large number of functional networks that identify AD physiopathology. Experimental results show that our transmodal approach increases classification accuracy compared to pure rs-fMRI approaches, without resorting to additional invasive acquisitions. The method successfully recovers brain regions known to be impacted by the disease.
Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didactic overview of the relevant theoretical considerations. Practical aspects are highlighted with an extensive empirical study of the common decoders in within-and across-subject predictions, on multiple datasets –anatomical and functional MRI and MEG– and simulations. Theory and experiments outline that the popular " leave-one-out " strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred. Experiments outline the large error bars of cross-validation in neuroimaging settings: typical confidence intervals of 10%. Nested cross-validation can tune decoders' parameters while avoiding circularity bias. However we find that it can be more favorable to use sane defaults, in particular for non-sparse decoders.
Collecting the maximal amount of information in a given scanning time is a major concern in Magnetic Resonance Imaging (MRI) to speed up image acquisition. The hardware constraints (gradient magnitude, slew rate, ...), physical distortions (e.g., off-resonance effects) and sampling theorems (Shannon, compressed sensing) must be taken into account simultaneously, which makes this problem extremely challenging. To date, the main approach to design gradient waveform has consisted of selecting an initial shape (e.g. spiral, radial lines, ...) and then traversing it as fast as possible using optimal control. In this paper, we propose an alternative solution which first consists of defining a desired parameterization of the trajectory and then of optimizing for minimal deviation of the sampling points within gradient constraints. This method has various advantages. First, it better preserves the density of the input curve which is critical in sampling theory. Second, it allows to smooth high curvature areas making the acquisition time shorter in some cases. Third, it can be used both in the Shannon and CS sampling theories. Last, the optimized trajectory is computed as the solution of an efficient iterative algorithm based on convex programming. For piecewise linear trajectories, as compared to optimal control reparameterization, our approach generates a gain in scanning time of 10% in echo planar imaging while improving image quality in terms of signal-to-noise ratio (SNR) by more than 6 dB. We also investigate original trajectories relying on traveling salesman problem solutions. In this context, the sampling patterns obtained using the proposed projection algorithm are shown to provide significantly better reconstructions (more than 6 dB) while lasting the same scanning time.
Perceptual learning sculpts ongoing brain activity. This finding has been observed by statistically comparing the functional connectivity (FC) patterns computed from resting-state functional MRI (rs-fMRI) data recorded before and after intensive training to a visual attention task. Hence, functional connectivity serves a dynamic role in brain function, supporting the consolidation of previous experience. Following this line of research, we trained three groups of individuals to a visual discrimination task during a magneto-encephalography (MEG) experiment. The same individuals were then scanned in rs-fMRI. Here, in a supervised classification framework, we demonstrate that FC metrics computed on rs-fMRI data are able to predict the type of training the participants received. On top of that, we show that the prediction accuracies based on tangent embedding FC measure outperform those based on our recently developed multivariate wavelet-based Hurst exponent estimator, which captures low frequency fluctuations in ongoing brain activity too.
The Wendelin project has been granted on December 3rd, 2014. It has been selected at the Programme d’Investissements d’Avenir (PIA) that supports "cloud computing et Big Data". It gives visibility and fosters the French technological big data sector, and in particular the scikit-learn library, the NoSQL “NEO” et the decentralized “SlapOS” cloud, three open-source software supported by the Systematic pôle de compétitivité.
Scikit-learn is a worldwide reference library for machine learning. Gaël Varoquaux, Olivier Grisel and Alexandre Gramfort have been major players in the design of the library and Scikit-learn has then been supported by the growing scientific Python community. It is currently used by major internet companies as well as dynamic start-ups, including Google, Airbnb, Spotify, Evernote, AWeber, TinyClues; it wins more than half of the data science "Kaggle" competitions. Scikit-learn makes it possible to predict future outcomes given a training data, and thus to optimize company decisions. Almost 1 million euros will be invested to improve the algorithmic core of scikit-learn through the Wendelin project thanks to the Inria, ENS and Institut Mines Télécom teams. In particular, scikit-learn will be extended in order to ease online prediction and to include recent stochastic gradient algorithms.
NEO is the native NoSQL base of the Python language. It was initially designed by Nexedi and is currently used and embedded in the main software of company information systems. More than one million euros will be invested into NEO, so that scikit-learn can process within 10 years (out-of-core) data of 1 exabyte size.
Paris13 university and the Mines Télécom institute will extend the SlapOS distributed mesh cloud to deploy Wendelin in Big Data as a Service (BDaaS) mode, to achieve the interoperability between the Grid5000 and Teralab infrastructures and to extend the cloud toward smart sensor systems.
The combination of scikit-learn, NEO and SlapOS will improve the
predictive maintenance of industrial plants with two major use cases:
connected windmills (GDF SUEZ, Woelfel) and customer satisfaction in
car sale systems (MMC Rus). In both cases it is about non-personal,
yet profitable big data.
The Wendelin project actually demonstrates that Big data can improve
infrastructure and everyday-life equipment without intrusive data
collection. For more information, please see http://
The project partners are:
Nexedi (leader)
GDF SUEZ
Abilian
2ndQuadrant
Institut Mines Télécom
Inria
Université Paris 13
This is a collaborative project with Jean-Luc Stark, (CEA) funded by the CEA program drf-impulsion.
Compressed Sensing is a recent theory in maths that allows the perfect recovery of signals or images from compressive acquisition scenarios. This approach has been popularized in MRI over the last decade as well as in astrophysics (noticeably in radio-astronomy). So far, both of these fields have developed skills in CS separately. The aim of the COSMIC project is to foster collaborations between CEA experts in MRI (Inria-CEA Parietal team within NeuroSpin) and in astrophysics (CosmoStat lab within the Astrophysics Department). These interactions will allow us to share different expertise in order to improve image quality, either in MRI or in radio-astronomy (thanks to the interferometry principle). In this field, given the data delivered by radio-telescopet he goal will consist of extracting high temporal resolution information in order to study fast transient events.
This is a collaborative project with Lenka Zdeborová, Theoretical Physics Institute (CEA) funded by the CEA program drf-impulsion.
In many scientific fields, the data acquisition devices have benefited
of hardware improvement to increase the resolution of the observed
phenomena, leading to ever larger datasets. While the dimensionality
has increased, the number of samples available is often limited, due
to physical or financial limits. This is a problem when these data are
processed with estimators that have a large sample complexity, such as
multivariate statistical models. In that case it is very useful to
rely on structured priors, so that the results reflect the state of
knowledge on the phenomena of interest. The study of the human brain
activity through high-field MRI belongs among these problems, with up
to
We are missing fast estimators for multivariate models with structured priors, that furthermore provide statistical control on the solution. Approximate message passing methods are designed to work optimally with low-sample-complexity, they accommodate rather generic class of priors and come with an estimation of statistical significance. They are therefore well suited for our purposes.
We want to join forces to design a new generation of inverse problem solvers that can take into account the complex structure of brain images and provide guarantees in the low-sample-complexity regime. To this end, we will first adapt AMP to the brain mapping setting, using first standard sparsity priors (e.g. Gauss-Bernoulli) on the model. We will then consider more complex structured priors that control the variation of the learned image patterns in space. Crucial gains are expected from the use of the EM algorithm for parameter setting, that comes naturally with AMP. We will also examine the estimators provided by AMP for statistical significance. BrainAMP will design a reference inference toolbox released as a generic open source library. We expect a 3- to 10-fold improvement in CPU time, that will benefit to large-scale brain mapping investigations.
This is a Digiteo project (2014-2017).
Mapping brain functional connectivity from functional Magnetic Resonance Imaging (MRI) data has become a very active field of research. However, analysis tools are limited and many important tasks, such as the empirical definition of brain networks, remain difficult due to the lack of a good framework for the statistical modeling of these networks. We propose to develop population models of anatomical and functional connectivity data to improve the alignment of subjects brain structures of interest while inferring an average template of these structures. Based on this essential contribution, we will design new statistical inference procedures to compare the functional connections between conditions or populations and improve the sensitivity of connectivity analysis performed on noisy data. Finally, we will test and validate the methods on multiple datasets and distribute them to the brain imaging community.
This is a Digicosme project (2016-2019) and a collaboration with Fabian Suchanek (Telecom Paritech).
Understanding how cognition emerges from the billions of neurons that constitute the human brain is a major open problem in science that could bridge natural science –biology– to humanities –psychology. Psychology studies performed on humans with functional Magnetic Resonance Imaging (fMRI) can be used to probe the full repertoire of high-level cognitive functions. While analyzing the resulting image data for a given experiment is a relatively well-mastered process, the challenges in comparing data across multiple datasets poses serious limitation to the field. Indeed, such comparisons require to pool together brain images acquired under different settings and assess the effect of different experimental conditions that correspond to psychological effects studied by neuroscientists.
Such meta-analyses are now becoming possible thanks to the development of
public data resources –OpenfMRI http://
The purpose of this project is to learn a semantic structure in cognitive terms from their occurrence in brain activations. This structure will simplify massive multi-label statistical-learning problems that arise in brain mapping by providing compact representations of cognitive concepts while capturing the imprecision on the definition these concepts.
CDS2 is an "Strategic research initiatice” of the Paris Saclay
University Idex http://
The scale-free concept formalizes the intuition that, in many systems, the analysis of temporal dynamics cannot be grounded on specific and characteristic time scales. The scale-free paradigm has permitted the relevant analysis of numerous applications, very different in nature, ranging from natural phenomena (hydrodynamic turbulence, geophysics, body rhythms, brain activity,...) to human activities (Internet traffic, population, finance, art,...).
Yet, most successes of scale-free analysis were obtained in contexts where data are univariate, homogeneous along time (a single stationary time series), and well-characterized by simple-shape local singularities. For such situations, scale-free dynamics translate into global or local power laws, which significantly eases practical analyses. Numerous recent real-world applications (macroscopic spontaneous brain dynamics, the central application in this project, being one paradigm example), however, naturally entail large multivariate data (many signals), whose properties vary along time (non-stationarity) and across components (non-homogeneity), with potentially complex temporal dynamics, thus intricate local singular behaviors.
These three issues call into question the intuitive and founding identification of scale-free to power laws, and thus make uneasy multivariate scale-free and multifractal analyses, precluding the use of univariate methodologies. This explains why the concept of scale-free dynamics is barely used and with limited successes in such settings and highlights the overriding need for a systematic methodological study of multivariate scale-free and multifractal dynamics. The Core Theme of MULTIFRACS consists in laying the theoretical foundations of a practical robust statistical signal processing framework for multivariate non homogeneous scale-free and multifractal analyses, suited to varied types of rich singularities, as well as in performing accurate analyses of scale-free dynamics in spontaneous and task-related macroscopic brain activity, to assess their natures, functional roles and relevance, and their relations to behavioral performance in a timing estimation task using multimodal functional imaging techniques.
This overarching objective is organized into 4 Challenges:
Multivariate scale-free and multifractal analysis,
Second generation of local singularity indices,
Scale-free dynamics, non-stationarity and non-homogeneity,
Multivariate scale-free temporal dynamics analysis in macroscopic brain activity.
BrainPedia is an ANR JCJC (2011-2015) which addresses the following question: Neuroimaging produces huge amounts of complex data that are used to better understand the relations between brain structure and function. While the acquisition and analysis of this data is getting standardized in some aspects, the neuroimaging community is still largely missing appropriate tools to store and organize the knowledge related to the data. Taking advantage of common coordinate systems to represent the results of group studies, coordinate-based meta-analysis approaches associated with repositories of neuroimaging publications provide a crude solution to this problem, that does not yield reliable outputs and looses most of the data-related information. In this project, we propose to tackle the problem in a statistically rigorous framework, thus providing usable information to drive neuroscientific knowledge and questions.
Context: The NiConnect project (2012-2016) arises from an increasing need of medical imaging tools to diagnose efficiently brain pathologies, such as neuro-degenerative and psychiatric diseases or lesions related to stroke. Brain imaging provides a non-invasive and widespread probe of various features of brain organization, that are then used to make an accurate diagnosis, assess brain rehabilitation, or make a prognostic on the chance of recovery of a patient. Among different measures extracted from brain imaging, functional connectivity is particularly attractive, as it readily probes the integrity of brain networks, considered as providing the most complete view on brain functional organization.
Challenges: To turn methods research into popular tool widely usable by non specialists, the NiConnect project puts specific emphasis on producing high-quality open-source software. NiConnect addresses the many data analysis tasks that extract relevant information from resting-state fMRI datasets. Specifically, the scientific difficulties are i) conducting proper validation of the models and tools, and ii) providing statistically controlled information to neuroscientists or medical doctors. More importantly, these procedures should be robust enough to perform analysis on limited quality data, as acquiring data on diseased populations is challenging and artifacts can hardly be controlled in clinical settings.
Outcome of the project: In the scope of computer science and statistics, NiConnect pushes forward algorithms and statistical models for brain functional connectivity. In particular, we are investigating structured and multi-task graphical models to learn high-dimensional multi-subject brain connectivity models, as well as spatially-informed sparse decompositions for segmenting structures from brain imaging. With regards to neuroimaging methods development, NiConnect provides systematic comparisons and evaluations of connectivity biomarkers and a software library embedding best-performing state-of-the-art approaches. Finally, with regards to medical applications, the NiConnect project also plays a support role in on going medical studies and clinical trials on neurodegenerative diseases.
Consortium
Parietal Inria research team: applied mathematics and computer science to model the brain from MRI
LIF INSERM research team: medical image data analysis and modeling for clinical applications
CATI center: medical image processing center for large scale brain imaging studies
Henri-Mondor hospital neurosurgery and neuroradiology: clinical teams conducting research on treatments for neurodegenerative diseases, in particular Huntington and Parkinson diseases
Logilab: consulting in scientific computing
Title: The Human Brain Project
Programm: FP7
Duration: October 2013 - September 2016
Coordinator: EPFL
Partners: 100 across Europe
Inria contact: Olivier Faugeras
Understanding the human brain is one of the greatest challenges facing 21st century science. If we can rise to the challenge, we can gain profound insights into what makes us human, develop new treatments for brain diseases and build revolutionary new computing technologies. Today, for the first time, modern ICT has brought these goals within sight. The goal of the Human Brain Project, part of the FET Flagship Programme, is to translate this vision into reality, using ICT as a catalyst for a global collaborative effort to understand the human brain and its diseases and ultimately to emulate its computational capabilities. The Human Brain Project will last ten years and will consist of a ramp-up phase (from month 1 to month 36) and subsequent operational phases. This Grant Agreement covers the ramp-up phase. During this phase the strategic goals of the project will be to design, develop and deploy the first versions of six ICT platforms dedicated to Neuroinformatics, Brain Simulation, High Performance Computing, Medical Informatics, Neuromorphic Computing and Neurorobotics, and create a user community of research groups from within and outside the HBP, set up a European Institute for Theoretical Neuroscience, complete a set of pilot projects providing a first demonstration of the scientific value of the platforms and the Institute, develop the scientific and technological capabilities required by future versions of the platforms, implement a policy of Responsible Innovation, and a programme of transdisciplinary education, and develop a framework for collaboration that links the partners under strong scientific leadership and professional project management, providing a coherent European approach and ensuring effective alignment of regional, national and European research and programmes. The project work plan is organized in the form of thirteen subprojects, each dedicated to a specific area of activity. A significant part of the budget will be used for competitive calls to complement the collective skills of the Consortium with additional expertise.
Program: Marie Curie
Project acronym: Neuroimaging Power
Project title: Effect size and power for neuroimaging.
Duration: mois année début - mois année fin
Coordinator: Inria
Other partners: Univ. Stanford, USA
Abstract: There is an increasing concern about statistical power in neuroscience research. Critically, an underpowered study has poor predictive power. Findings from a low-power study are unlikely to be reproducible, and thus a power analysis is a critical component of any paper. This project aims to promote and facilitate the use of power analyses.A key component of a power analysis is the specification of an effect size. However, in neuroimaging, there is no standardised way to communicate effect sizes, which makes the choice of an appropriate effect size a formidable task. The best way today to perform a power analysis is by collecting a pilot data set, a very expensive practice. To eliminate the need for pilot data, we will develop a standardised measure of effect size taking into account the spatial variance and the uncertainty of the measurements. Communicating effect sizes in new publications will facilitate the use of power analyses.To further alleviate the need for pilot data, we will provide a library of effect sizes for different tasks and contrasts, using open data projects in neuroimaging. We will integrate our effect size estimator in open repositories NeuroVault and OpenfMRI. Consequently, these effect sizes can then serve as a proxy for a pilot study, and as such, a huge cost in the design of an experiment is eliminated.A new experiment will not be identical to the open data and as such the hypothesised parameters might not be fully accurate. To address this issue, we present a flexible framework to analyse data mid-way without harming the control of the type I error rate. Such a procedure will allow re-evaluating halfway an experiment whether it is useful to continue a study, and how many more subjects are needed for statistically sound inferences.To make our methods maximally available, we will write a software suite including all these methods in different programming platforms and we will provide a GUI to further increase the use of power analyses.
Title: Machine learning for meta-analysis of functional neuroimaging data
International Partner (Institution - Laboratory - Researcher):
Stanford (United States) - Department of Psychology - Russ Poldrack
Start year: 2015
See also: https://
Neuroimaging produces huge amounts of complex data that are used to better understand the relations between brain structure and function. Observing that the neuroimaging community is still largely missing appropriate tools to store and organize the knowledge related to the data, Parietal team and Poldrack's lab, have decided to join forces to set up a framework for functional brain image meta-analysis, i.e. a framework in which several datasets can be jointly analyzed in order to accumulate information on the functional specialization of brain regions. MetaMRI will build upon Poldrack's lab expertise in handling, sharing and analyzing multi-protocol data and Parietal's recent developments of machine learning libraries to develop a new generation of meta-analytic tools.
Bertrand Thirion: Organization for Human Brain Mapping.
Philippe Ciuciu: IEEE ISBI (15 papers), IEEE ICASSP (10 papers), IEEE ICIP (5 papers), NIPS (4 papers), EUSIPCO (5 papers).
Bertrand Thirion: IPMI, MICCAI, NIPS, ISBI, PRNI, AISTATS
Gaël Varoquaux: IEEE ICASSP, MICCAI, NIPS, IPMI, ICML
Bertrand Thirion: Medical Image Analysis, Frontiers in brain imaging
Gaël Varoquaux: Frontiers in NeuroInformatics, Frontiers in brain imaging methods, NeuroImage
Philippe Ciuciu: Reviewer for Neuroimage, IEEE Signal Processing Letters, Signal Processing, IEEE Trans. Medical Imaging, Plos One, Plos Comput. Biology, Frontiers in Neuroscience.
Bertrand Thirion: Human Brain Mapping, IEEE TMI, MedIA, NeuroImage, PNAS
Gaël Varoquaux: NeuroImage, JSTSP, PNAS, HBM, PLOS Comp Bio, Gigascience
Olivier Grisel: Journal of Machine Learning Research (software track).
February: invited talk at the Imagerie du Vivant National congress, entitled Large-scale analyses in functional brain Imaging.
February: presentation at the Pasadena working group of the Digicosme Labex.
April: invited presentation at European Neuroscience institute, Paris, entitled Seeing it all: Convolutional network layers map the function of the human visual system.
April: presentation Functional connectomicts, at DTU Copenhagen, entitled from large-scale estimators to empirical validation.
May: Talk at Atlas workshop, Grenoble, entitled Learning representations from functional brain imaging.
June: organizer of a table ronde at the Futur en Seine event entitled Computational methods for neurosciences & medical imaging.
October: talk at MPI Psychiatry, Munich, entitled Machine learning for neuroimaging: current challenges and solutions.
June: Talk at Neurostic workshop, Grenoble, entitled Learning representations from functional brain imaging.
October: Invited talk by the ITMO Neuroscience, Bordeaux, entitled Working with large data samples: the case of human brain imaging.
12/16: IEEE Lecture at University of British Columbia (Vancouver, Canada): Sparkling: Novel non-Cartesian sampling schemes for accelerated 2D anatomical imaging at 7 Tesla.
12/16: Pacific Parkinson's research center (Vancouver, Canada): Impact of perceptual learning on resting-state brain dynamics in fMRI: A supervised classification study.
09/16: GdR d'Analyse Multifractale (Avignon, France): Convergence of neural activity to multifractal attractors in MEG predicts learning.
08/16: invitation to the Special session entitled “Unraveling brain networks from functional neuroimaging data” at EUSIPCO'16 (Budapest, Hungary): Impact of perceptual learning on resting-state fMRI connectivity: A supervised classification study.
06/16: Journées scientifiques d'Inria (Rennes, France): Compressive Sampling in MRI.
06/16: Inria Sophia-Antipolis, équipe Athena. New physically plausible compressive sampling schemes for MRI: First results at 7 Tesla
05/16: University of Geneva (Campus BioTech, Geneva, Switzlerand): Convergence to asymptotic Multifractal dynamics in the brain predicts learning.
02/16: Grenoble Institut of Neurosciences (Grenoble, France): Physically plausible trajectories for Compressed Sensing in MRI.
02/16: Workshop on 7 Tesla scanner at NeuroSpin (Gif-sur-Yvette, France) Compressed sensing for high resolution MRI at 7 Tesla.
01/16: Cosmostat lab, IRFU/CEA. On the generation of compressed sampling schemes in MRI.
EuroScipy 2016: scikit-learn tutorial
Budapest BI 2016 : scikit-learn tutorial and talk "Recent developments in scikit-learn and joblib"
PyData Berlin and PyData Paris 2016: "Predictive modeling with Python, trends and tools
invited talk on Some recent developments in Deep Learning researc at Strata London 2016.
Paris Open Source summit 2016: scikit-learn, the vision and the community
EuroScipy 2016 (Erlangen): keynote: "On writing code the science"
Open Data Science Conference 2017 (London): keynote: "The code of data science"
EuroPython 2016 (Bilbao): keynote "Scientists meet web dev: how Python became the language of data"
PiterPy 2016 (St Petersbourg): keynote: "Python for data"
Facebook AI Research: some statistical learning problems in brain imaging
GDR ISIS Imagerie medicale: prediction de pathologies psychiatriques à partir d'imagerie fonctionnelle de repos
Brain network analysis workshop, MICCAI 2016 (Athenes): keynote
Journée Graphes et neuroscience à Marseilles: Machine learning on brain graphes
Séminaire débat sur le Big data en Neuroscience, Lyon
Seminar Max Planck Institute Leipzig: data mining for neuroimaging
Seminar Telecom ParisTech: randomized methods for high-dimensional statistical learning
Séminaire d'équipe Asclepios: Quelques problèmes d'apprentissage sur des images cérébrales
Gaël Varoquaux: Chair of the steering committee, IEEE PRNI
Bertrand Thirion: member of the Committee on Best Practices in Data Analysis and Sharing for the OHBM community.
Philippe Ciuciu: ANR JC, NSERC au Canada, FWO
Bertrand Thirion: ANR, NWO, NSF
Gaël Varoquaux: Membre de la Commission Expertises Scientifiques, (CE23) ANR
Olivier Grisel did 3 days of consulting with the CTO of the Therapixel startup to share expertise on the use of Deep Learning for the predictive analysis of 3D imaging data.
03/16: Involvement in the CEA visiting committee on High Performance Computing.
05/16: Member of a Comité de sélection for hiring an Assistant Professor in Paris-Saclay University (Section 61 of CNU).
06/16: Member of the Inria scientific commission in charge of ranking PhD and post-doctoral applicants as well as delegations of Assistant Professors to Inria.
Leader of the Datasense axis of the Digicosme Labex
Member of the STIC department committee Paris-Saclay University and of the bureau thereof.
DSA Saclay.
Member of "Commité de suivi doctoral", Inria Saclay
Member of "Commité cluster", Inria Saclay
Member of "Commission de Développement Technologique", Inria Saclay
Member of the directorate of the Paris-Saclay CDS (Center for Data Science)
Master : Brain Computer interface and Functional Neuroimaging, 12 heures équivalent TD, niveau M2, ENS Cachan
Master 2 : “Functional MRI: From data acquisition to analysis”, 3h, Univ. Paris V René Descartes & Télécom-Paristech, Master of Biomedical Engineering
Master 2 : “FMRI data analysis”, 3h, Univ. Paris-Saclay, Master of medical Physics
Master 2 : “Brain functional connectivity analysis”, 7h, Univ. Paris V René Descartes & Télécom-Paristech, Master of Biomedical Engineering
Master 2 : “Machine learning with scikit-learn”, 2h, ENSAE
Master 2 : “Advanced Machine learning with scikit-learn”, 3h, Centrale Paris, MSc in data sciences & business analytics
Ecole d'été multidisciplinaire analyse de données, Rennes, 1h
OHBM 2016: course on machine learning for cognitive neuroimaging 30mn
PRNI 2016: nilearn for machine learning on brain images, 8h
Max Planck Institute Leipzig: nilearn for machine learning on brain images, 8h
PhD in progress: Elvis Dohmatob,
PhD in progress: Arthur Mensch,
PhD in progress: Andrés Hoyos Idrobo
PhD defended: Aina Frau-Pascual, “Statistical models for the analysis of BOLD and ASL Magnetic Resonance modalities to study brain function and disease”, University of Grenoble-Alpes (doctoral school: Mathématiques, Sciences et Technologies de l’Information, Informatique), defense: 19/12/2016, Advisors: Florence Forbes (Dir), Philippe Ciuciu (Co-Dir)
PhD in progress: Carole Lazarus, “Physically plausible compressed sensing for high resolution MRI at 7 Tesla in Humans” starting date: October 2015 (Univ. Paris-Saclay, doctoral school: EOBE). Advisors: Philippe Ciuciu (Dir), Alexandre Vignaud (Co-Dir)
PhD in progress: Loubna El Gueddari, “Parallel proximal algorithms for compressed sensing MRI reconstruction. Applications in ultra-high magnetic field imaging”, starting date: October 2016 (Univ. Paris-Saclay, doctoral school: EOBE). Advisors: Philippe Ciuciu (Dir) and Jean-Christophe Pesquet (Co-Dir, Prof. at Centrale-Supélec)
PhD defended: Alexandre Abraham
PhD in progress: Elvis Dohmatob,
PhD in progress: Arthur Mensch,
PhD in progress: Andrés Hoyos Idrobo
04/29: Reviewer of Niklas Kasenburg PhD Thesis , Univ. Copenhagen, Denmark.
01/12: Examiner of Simona Schiavi PhD Thesis, Univ. Paris Saclay.
14/12: Reviewer of Olivier Marre habilitation, Paris.
15/12: Reviewer of Maite Termenon PhD thesis, Univ. Grenoble.
04/16: Reviewer of Aiping Liu's PhD thesis (ECCS Dpt, Univ. British Columbia, Vancouvern Canada) entitled “Brain Connectivity Network Modeling using fMRI signals”
05/16: Reviewer of Andrea Laruelo-Fernandez's PhD thesis (INP Toulouse-IRIT- ENSEEIHT) entitled “Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning”
09/16: Examinor of Mohanad Albughdadi's PhD thesis (INP Toulouse-IRIT- ENSEEIHT) entitled “ Bayesian joint detection-estimation in functional MRI with automatic parcellation and functional constraints”
10/16: Reviewer of Sébastien Combrexelle's PhD thesis (INP Toulouse-IRIT- ENSEEIHT) entitled “Multifractal analysis for multivariate data with application to remote sensing”.
10/16: Co-director of Aina Frau-Pascual's PhD thesis (see above).
06/16: Examiner of Alberto García Durán, PhD Thesis, UTC Compiegne.
Unithé ou Café, Inria Saclay Ile de France
Atelier IHEST Les mots du numérique - 17 novembre
Software Carpentry workshops:
git course at UNIC in Gif-sur-Yvette March 29-30
helper at "Scientific Programming with Python and Software Engineering Best Practices" workshop, April 28-29 at Télécom Paris
git course at Proto 204, May 24-25
Mentor at Startup Weekend Artificial Intelligence, November 4-6.
"La tête au carré" radio show on France Inter in January 2016 to share
his expertise and opinion on the use and impacts of Big Data and
predictive algorithms