On spatial selectivity and prediction across conditions with fMRI

Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs). This paper presents two machine learning approaches, transfer learning and selection transfer, that are compared upon their ability to identify the common patterns between brain activation maps related to two functional tasks. We provide some preliminary quantification of these similarities, and show that selection transfer makes it possible to set a spatial scale yielding ROIs that are more specific to the context of interest than with transfer learning. In particular, selection transfer outlines well known regions such as the Visual Word Form Area when discriminating between different visual tasks.Comment: PRNI 2012 : 2nd International Workshop on Pattern Recognition in NeuroImaging, London : United Kingdom (2012

Mapping cognitive ontologies to and from the brain

Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies. Due to the nature of the individual experiments, based on eliciting neural response from a small number of stimuli, this link is incomplete, and unidirectional from the causal point of view. To come to conclusions on the function implied by the activation of brain regions, it is necessary to combine a wide exploration of the various brain functions and some inversion of the statistical inference. Here we introduce a methodology for accumulating knowledge towards a bidirectional link between observed brain activity and the corresponding function. We rely on a large corpus of imaging studies and a predictive engine. Technically, the challenges are to find commonality between the studies without denaturing the richness of the corpus. The key elements that we contribute are labeling the tasks performed with a cognitive ontology, and modeling the long tail of rare paradigms in the corpus. To our knowledge, our approach is the first demonstration of predicting the cognitive content of completely new brain images. To that end, we propose a method that predicts the experimental paradigms across different studies.Comment: NIPS (Neural Information Processing Systems), United States (2013

Analyse à grande échelle d'IRM fonctionnelle pour accumuler la connaissance sur les fonctions cérébrales

How can we accumulate knowledge on brain functions? How can we leverage years of research in functional MRI to analyse finer-grained psychological constructs, and build a comprehensive model of the brain? Researchers usually rely on single studies to delineate brain regions recruited by mental processes. They relate their findings to previous works in an informal way by defining regions of interest from the literature. Meta-analysis approaches provide a more principled way to build upon the literature. This thesis investigates three ways to assemble knowledge using activation maps from a large amount of studies. First, we present an approach that uses jointly two similar fMRI experiments, to better condition an analysis from a statistical standpoint. We show that it is a valuable data-driven alternative to traditional regions of interest analyses, but fails to provide a systematic way to relate studies, and thus does not permit to integrate knowledge on a large scale. Because of the difficulty to associate multiple studies, we resort to using a single dataset sampling a large number of stimuli for our second contribution. This method estimates functional networks associated with functional profiles, where the functional networks are interacting brain regions and the functional profiles are a weighted set of cognitive descriptors. This work successfully yields known brain networks and automatically associates meaningful descriptions. Its limitations lie in the unsupervised nature of this method, which is more difficult to validate, and the use of a single dataset. It however brings the notion of cognitive labels, which is central to our last contribution. Our last contribution presents a method that learns functional atlases by combining several datasets. [Henson 2006] shows that forward inference, i.e. the probability of an activation given a cognitive process, is often not sufficient to conclude on the engagement of brain regions for a cognitive process. Conversely, [Poldrack 2006] describes reverse inference as the probability of a cognitive process given an activation, but warns of a logical fallacy in concluding on such inference from evoked activity. Avoiding this issue requires to perform reverse inference with a large coverage of the cognitive space. We present a framework that uses a "meta-design" to describe many different tasks with a common vocabulary, and use forward and reverse inference in conjunction to outline functional networks that are consistently represented across the studies. We use a predictive model for reverse inference, and perform prediction on unseen studies to guarantee that we do not learn studies' idiosyncrasies. This final contribution permits to learn functional atlases, i.e. functional networks associated with a cognitive concept. We explored different possibilities to jointly analyse multiple fMRI experiments. We have found that one of the main challenges is to be able to relate the experiments with one another. As a solution, we propose a common vocabulary to describe the tasks. [Henson 2006] advocates the use of forward and reverse inference in conjunction to associate cognitive functions to brain regions, which is only possible in the context of a large scale analysis to overcome the limitations of reverse inference. This framing of the problem therefore makes it possible to establish a large statistical model of the brain, and accumulate knowledge across functional neuroimaging studies.Comment peut-on accumuler de la connaissance sur les fonctions cérébrales ? Comment peut-on bénéficier d'années de recherche en IRM fonctionnelle (IRMf) pour analyser des processus cognitifs plus fins et construire un modèle exhaustif du cerveau ? Les chercheurs se basent habituellement sur des études individuelles pour identifier des régions cérébrales recrutées par les processus cognitifs. La comparaison avec l'historique du domaine se fait généralement manuellement pas le biais de la littérature, qui permet de définir des régions d'intérêt dans le cerveau. Les méta-analyses permettent de définir des méthodes plus formelles et automatisables pour analyser la littérature. Cette thèse examine trois manières d'accumuler et d'organiser les connaissances sur le fonctionnement du cerveau en utilisant des cartes d'activation cérébrales d'un grand nombre d'études. Premièrement, nous présentons une approche qui utilise conjointement deux expériences d'IRMf similaires pour mieux conditionner une analyse statistique. Nous montrons que cette méthode est une alternative intéressante par rapport aux analyses qui utilisent des régions d'intérêts, mais demande cependant un travail manuel dans la sélection des études qui l'empêche de monter à l'échelle. A cause de la difficulté à sélectionner automatiquement les études, notre deuxième contribution se focalise sur l'analyse d'une unique étude présentant un grand nombre de conditions expérimentales. Cette méthode estime des réseaux fonctionnels (ensemble de régions cérébrales) et les associe à des profils fonctionnels (ensemble pondéré de descripteurs cognitifs). Les limitations de cette approche viennent du fait que nous n'utilisons qu'une seule étude, et qu'elle se base sur un modèle non supervisé qui est par conséquent plus difficile à valider. Ce travail nous a cependant apporté la notion de labels cognitifs, qui est centrale pour notre dernière contribution. Cette dernière contribution présente une méthode qui a pour objectif d'apprendre des atlas fonctionnels en combinant plusieurs jeux de données. [Henson2006] montre qu'une inférence directe, c.a.d. la probabilité d'une activation étant donné un processus cognitif, n'est souvent pas suffisante pour conclure sur l'engagement de régions cérébrales pour le processus cognitif en question. Réciproquement, [Poldrack 2006] présente l'inférence inverse qui est la probabilité qu'un processus cognitif soit impliqué étant donné qu'une région cérébrale est activée, et décrit le risque de raisonnements fallacieux qui peuvent en découler. Pour éviter ces problèmes, il ne faut utiliser l'inférence inverse que dans un contexte où l'on suffisamment bien échantillonné l'espace cognitif pour pouvoir faire une inférence pertinente. Nous présentons une méthode qui utilise un «  meta-design » pour décrire des tâches cognitives avec un vocabulaire commun, et qui combine les inférences directe et inverse pour mettre en évidence des réseaux fonctionnels qui sont cohérents à travers les études. Nous utilisons un modèle prédictif pour l'inférence inverse, et effectuons les prédictions sur de nouvelles études pour s'assurer que la méthode n'apprend pas certaines idiosyncrasies des données d'entrées. Cette dernière contribution nous a permis d'apprendre des réseaux fonctionnels, et de les associer avec des concepts cognitifs. Nous avons exploré différentes approches pour analyser conjointement des études d'IRMf. L'une des difficultés principales était de trouver un cadre commun qui permette d'analyser ensemble ces études malgré leur diversité. Ce cadre s'est instancié sous la forme d'un vocabulaire commun pour décrire les tâches d'IRMf. et a permis d'établir un modèle statistique du cerveau à grande échelle et d'accumuler des connaissances à travers des études d'IRM fonctionnelle

Improving accuracy and power with transfer learning using a meta-analytic database

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e. to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.Comment: MICCAI, Nice : France (2012

Improving sparse recovery on structured images with bagged clustering

International audience—The identification of image regions associated with external variables through discriminative approaches yields ill-posed estimation problems. This estimation challenge can be tackled by imposing sparse solutions. However, the sensitivity of sparse estimators to correlated variables leads to non-reproducible results, and only a subset of the important variables are selected. In this paper, we explore an approach based on bagging clustering-based data compression in order to alleviate the instability of sparse models. Specifically, we design a new framework in which the estimator is built by averaging multiple models estimated after feature clustering, to improve the conditioning of the model. We show that this combination of model averaging with spatially consistent compression can have the virtuous effect of increasing the stability of the weight maps, allowing a better interpretation of the results. Finally, we demonstrate the benefit of our approach on several predictive modeling problems

Assessing and tuning brain decoders: cross-validation, caveats, and guidelines

International audienceDecoding, 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

On spatial selectivity and prediction across conditions with fMRI

International audienceResearchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs). This paper presents two machine learning approaches, transfer learning and selection transfer, that are compared upon their ability to identify the common patterns between brain activation maps related to two functional tasks. We provide some preliminary quantification of these similarities, and show that selection transfer makes it possible to set a spatial scale yielding ROIs that are more specific to the context of interest than with transfer learning. In particular, selection transfer outlines well known regions such as the Visual Word Form Area when discriminating between different visual tasks

The Brainomics/Localizer database

International audienceThe Brainomics/Localizer database exposes part of the data collected by the in house Localizer project, which planned to acquire four types of data from volunteer research subjects: anatomical MRI scans, functional MRI data, behavioral and demographic data, and DNA sampling. Over the years, this local project has been collecting such data from hundreds of subjects. We had selected 94 of these subjects for their complete datasets, including all four types of data, as the basis for a prior publication; the Brainomics/Localizer database publishes the data associated with these 94 subjects. Since regulatory rules prevent us from making genetic data available for download, the database serves only anatomical MRI scans, functional MRI data, behavioral and demographic data. To publish this set of heterogeneous data, we use dedicated software based on the open-source CubicWeb semantic web framework. Through genericity in the data model and flexibility in the display of data (web pages, CSV, JSON, XML), CubicWeb helps us expose these complex datasets in original and efficient ways

Brainomics: A management system for exploring and merging heterogeneous brain mapping data

International audienceWe propose an open source solution to manage brain imaging datasets and associated meta data. This framework is a powerful querying and reporting tool, customized for the needs of the emerging imaging-genetics field. A demonstration website and more details are available at http:/brainomics.cea.fr

Influence of tip shroud cavities on low-pressure turbine main flowat design and off-design conditions

A lot of studies on turbomachinery main flow optimisation have been performed in order to reach actual efficiency level of modern gas turbines. To go further in the study of aerodynamic losses sources, a better understanding on technological effects is required. Tip shroud cavities in low pressure turbine is an example. Indeed, the by-pass flow causes additional pressure losses. In addition, interactions between main flow and cavity flows, as well as the re-entering flow, cause mixing losses and modifications of flow angle. This paper investigates the contribution of tip shroud cavities in a low pressure turbine stage on flow structures using (Unsteady) Reynolds Averaged Navier-Stokes simulations. The ability of a steady simulation to predict the overall performance and flow physics of this kind of flow is well documented in the literature but time-resolved simulations are needed to deepen the analysis. This is an objective of this paper. Following the presentation of the configuration under investigation, an analysis of flow structures is made in the upstream region of the rotor, close to the shroud. After that, simulations at off-design conditions are studied in order to evaluate this impact on the previous mechanisms