Total variation regularization for fMRI-based prediction of behaviour.

International audienceWhile medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional MRI (fMRI) data, that provide an indirect measure of taskrelated or spontaneous neuronal activity, are classically analyzed in a mass-univariate procedure yielding statistical parametric maps. This analysis framework disregards some important principles of brain organization: population coding, distributed and overlapping representations. Multivariate pattern analysis, i.e., the prediction of behavioural variables from brain activation patterns better captures this structure. To cope with the high dimensionality of the data, the learning method has to be regularized. However, the spatial structure of the image is not taken into account in standard regularization methods, so that the extracted features are often hard to interpret. More informative and interpretable results can be obtained with the '1 norm of the image gradient, a.k.a. its Total Variation (TV), as regularization. We apply for the first time this method to fMRI data, and show that TV regularization is well suited to the purpose of brain mapping while being a powerful tool for brain decoding. Moreover, this article presents the first use of TV regularization for classification

Learning to rank from medical imaging data

Medical images can be used to predict a clinical score coding for the severity of a disease, a pain level or the complexity of a cognitive task. In all these cases, the predicted variable has a natural order. While a standard classifier discards this information, we would like to take it into account in order to improve prediction performance. A standard linear regression does model such information, however the linearity assumption is likely not be satisfied when predicting from pixel intensities in an image. In this paper we address these modeling challenges with a supervised learning procedure where the model aims to order or rank images. We use a linear model for its robustness in high dimension and its possible interpretation. We show on simulations and two fMRI datasets that this approach is able to predict the correct ordering on pairs of images, yielding higher prediction accuracy than standard regression and multiclass classification techniques

A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding

International audienceFunctional Magnetic Resonance Imaging (fMRI) provides a unique opportunity to study brain functional architecture, while being minimally invasive. Reverse inference, a.k.a. decoding, is a recent statistical analysis approach that has been used with success for deciphering activity patterns that are thought to fit the neuroscientific concept of population coding. Decoding relies on the selection of brain regions in which the observed activity is predictive of certain cognitive tasks. The accuracy of such a procedure is quantified by the prediction of the behavioral variable of interest - the target. In this paper, we discuss the optimality of decoding methods in two different settings, namely intra- and inter-subject kind of decoding. While inter-subject prediction aims at finding predictive regions that are stable across subjects, it is plagued by the additional inter-subject variability (lack of voxel-to-voxel correspondence), so that the best suited prediction algorithms used in reverse inference may not be the same in both cases. We benchmark different prediction algorithms in both intra- and inter-subjects analysis, and we show that using spatial regularization improves reverse inference in the challenging context of inter-subject prediction. Moreover, we also study the different maps of weights, and show that methods with similar accuracy may yield maps with very different spatial layout of the predictive regions

Total Variation meets Sparsity: statistical learning with segmenting penalties

International audiencePrediction from medical images is a valuable aid to diagnosis. For instance, anatomical MR images can reveal certain disease conditions, while their functional counterparts can predict neuropsychi-atric phenotypes. However, a physician will not rely on predictions by black-box models: understanding the anatomical or functional features that underpin decision is critical. Generally, the weight vectors of clas-sifiers are not easily amenable to such an examination: Often there is no apparent structure. Indeed, this is not only a prediction task, but also an inverse problem that calls for adequate regularization. We address this challenge by introducing a convex region-selecting penalty. Our penalty combines total-variation regularization, enforcing spatial conti-guity, and 1 regularization, enforcing sparsity, into one group: Voxels are either active with non-zero spatial derivative or zero with inactive spatial derivative. This leads to segmenting contiguous spatial regions (inside which the signal can vary freely) against a background of zeros. Such segmentation of medical images in a target-informed manner is an important analysis tool. On several prediction problems from brain MRI, the penalty shows good segmentation. Given the size of medical images, computational efficiency is key. Keeping this in mind, we contribute an efficient optimization scheme that brings significant computational gains

Identifying predictive regions from fMRI with TV-L1 prior

International audienceDecoding, i.e. predicting stimulus related quantities from functional brain images, is a powerful tool to demonstrate differences between brain activity across conditions. However, unlike standard brain mapping, it offers no guaranties on the localization of this information. Here, we consider decoding as a statistical estimation problem and show that injecting a spatial segmentation prior leads to unmatched performance in recovering predictive regions. Specifically, we use L1 penalization to set voxels to zero and Total-Variation (TV) penalization to segment regions. Our contribution is two-fold. On the one hand, we show via extensive experiments that, amongst a large selection of decoding and brain-mapping strategies, TV+L1 leads to best region recovery. On the other hand, we consider implementation issues related to this estimator. To tackle efficiently this joint prediction-segmentation problem we introduce a fast optimization algorithm based on a primal-dual approach. We also tackle automatic setting of hyper-parameters and fast computation of image operation on the irregular masks that arise in brain imaging

Improved brain pattern recovery through ranking approaches

International audienceInferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture multivariate statistical effects distributed across voxels and brain regions. Up to now, much effort has been made to improve decoding by incorporating prior knowledge in the form of a particular regularization term. In this paper we demonstrate that further improvement can be made by accounting for non-linearities using a ranking approach rather than the commonly used least-square regression. Through simulation, we compare the recovery properties of our approach to linear models commonly used in fMRI based decoding. We demonstrate the superiority of ranking with a real fMRI dataset

Task-load-dependent activation of dopaminergic midbrain areas in the absence of reward

Dopamine release in cortical and subcortical structures plays a central role in reward-related neural processes. Within this context, dopaminergic inputs are commonly assumed to play an activating role, facilitating behavioral and cognitive operations necessary to obtain a prospective reward. Here, we provide evidence from human fMRI that this activating role can also be mediated by task-demand-related processes and thus extendsbeyondsituationsthatonlyentailextrinsicmotivatingfactors. Using a visual discrimination task in which varying levels of task demands were precued, we found enhanced hemodynamic activity in the substantia nigra (SN) for high task demands in the absence of reward or similar extrinsic motivating factors. This observation thus indicates that the SN can also be activated in an endogenous fashion. In parallel to its role in reward-related processes, reward-independent activation likely serves to recruit the processing resources needed to meet enhanced task demands. Simultaneously, activity in a wide network of cortical and subcortical control regions was enhanced in response to high task demands, whereas areas of the default-mode network were deactivated more strongly. The present observations suggest that the SN represents a core node within a broader neural network that adjusts the amount of available neural and behavioral resources to changing situational opportunities and task requirements, which is often driven by extrinsic factors but can also be controlled endogenously

Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity

International audienceInverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some cognitive variables related to brain activation maps, this approach aims at decoding brain activity. Inverse inference takes into account the multivariate information between voxels and is currently the only way to assess how precisely some cognitive information is encoded by the activity of neural populations within the whole brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, since there are far more features than samples, i.e., more voxels than fMRI volumes. To address this problem, different methods have been proposed, such as, among others, univariate feature selection, feature agglomeration and regularization techniques. In this paper, we consider a sparse hierarchical structured regularization. Specifically, the penalization we use is constructed from a tree that is obtained by spatially-constrained agglomerative clustering. This approach encodes the spatial structure of the data at different scales into the regularization, which makes the overall prediction procedure more robust to inter-subject variability. The regularization used induces the selection of spatially coherent predictive brain regions simultaneously at different scales. We test our algorithm on real data acquired to study the mental representation of objects, and we show that the proposed algorithm not only delineates meaningful brain regions but yields as well better prediction accuracy than reference methods