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Search Results: 1 - 10 of 1877 matches for " Bertrand Thirion "
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Small-sample Brain Mapping: Sparse Recovery on Spatially Correlated Designs with Randomization and Clustering
Gael Varoquaux,Alexandre Gramfort,Bertrand Thirion
Computer Science , 2012,
Abstract: Functional neuroimaging can measure the brain?s response to an external stimulus. It is used to perform brain mapping: identifying from these observations the brain regions involved. This problem can be cast into a linear supervised learning task where the neuroimaging data are used as predictors for the stimulus. Brain mapping is then seen as a support recovery problem. On functional MRI (fMRI) data, this problem is particularly challenging as i) the number of samples is small due to limited acquisition time and ii) the variables are strongly correlated. We propose to overcome these difficulties using sparse regression models over new variables obtained by clustering of the original variables. The use of randomization techniques, e.g. bootstrap samples, and clustering of the variables improves the recovery properties of sparse methods. We demonstrate the benefit of our approach on an extensive simulation study as well as two fMRI datasets.
Mapping cognitive ontologies to and from the brain
Yannick Schwartz,Bertrand Thirion,Ga?l Varoquaux
Computer Science , 2013,
Abstract: 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.
On spatial selectivity and prediction across conditions with fMRI
Yannick Schwartz,Ga?l Varoquaux,Bertrand Thirion
Computer Science , 2012,
Abstract: 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.
Multiclass Sparse Bayesian Regression for fMRI-Based Prediction
Vincent Michel,Evelyn Eger,Christine Keribin,Bertrand Thirion
International Journal of Biomedical Imaging , 2011, DOI: 10.1155/2011/350838
Abstract: Inverse inference has recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. We introduce in this paper a new model, called Multiclass Sparse Bayesian Regression (MCBR), that, unlike classical alternatives, automatically adapts the amount of regularization to the available data. MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization. We detail these framework and validate our algorithm on simulated and real neuroimaging data sets, showing that it performs better than reference methods while yielding interpretable clusters of features. 1. Introduction In the context of neuroimaging, machine learning approaches have been used so far to address diagnostic problems, where patients were classified into different groups based on anatomical or functional data. By contrast, in cognitive studies, the standard framework for functional or anatomical brain mapping was based on mass univariate inference procedures [1]. Recently, a new way of analyzing functional neuroimaging data has emerged [2, 3], and it consists in assessing how well behavioral information or cognitive states can be predicted from brain activation images such as those obtained with functional magnetic resonance imaging (fMRI). This approach opens new ways for understanding the mental representation of various perceptual and cognitive parameters, which can be regarded as the study of the corresponding neural code, albeit at a relatively low spatial resolution. The accuracy of the prediction of the behavioral or cognitive target variable, as well as the spatial layout of predictive regions, can provide valuable information about functional brain organization; in short, it helps to decode the brain system [4]. Many different pattern recognition and machine leaning methods have been used to extract information from brain images and compare it to the corresponding target. Among them, Linear Discriminant Analysis (LDA) [3, 5], Support Vector Machine (SVM) [6–9], or regularized
Which fMRI clustering gives good brain parcellations?
Bertrand Thirion,Gael Varoquaux,Jean-Baptiste Poline
Frontiers in Neuroscience , 2014, DOI: 10.3389/fnins.2014.00167
Abstract: Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subjects images, parcellation approaches use brain activity (e.g. found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and K-means clustering algorithms. We show that in general Ward’s clustering performs better than alternative methods with regards to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.
CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series
Ga?l Varoquaux,Sepideh Sadaghiani,Jean Baptiste Poline,Bertrand Thirion
Computer Science , 2009,
Abstract: Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract meaningful patterns without prior information. However, ICA is not robust to mild data variation and remains a parameter-sensitive algorithm. The validity of the extracted patterns is hard to establish, as well as the significance of differences between patterns extracted from different groups of subjects. We start from a generative model of the fMRI group data to introduce a probabilistic ICA pattern-extraction algorithm, called CanICA (Canonical ICA). Thanks to an explicit noise model and canonical correlation analysis, our method is auto-calibrated and identifies the group-reproducible data subspace before performing ICA. We compare our method to state-of-the-art multi-subject fMRI ICA methods and show that the features extracted are more reproducible.
HRF estimation improves sensitivity of fMRI encoding and decoding models
Fabian Pedregosa,Michael Eickenberg,Bertrand Thirion,Alexandre Gramfort
Computer Science , 2013,
Abstract: Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal. The general linear model (GLM) allows to estimate the activation from a design matrix and a fixed hemodynamic response function (HRF). However, the HRF is known to vary substantially between subjects and brain regions. In this paper, we propose a model for jointly estimating the hemodynamic response function (HRF) and the activation patterns via a low-rank representation of task effects.This model is based on the linearity assumption behind the GLM and can be computed using standard gradient-based solvers. We use the activation patterns computed by our model as input data for encoding and decoding studies and report performance improvement in both settings.
Second order scattering descriptors predict fMRI activity due to visual textures
Michael Eickenberg,Fabian Pedregosa,Senoussi Mehdi,Alexandre Gramfort,Bertrand Thirion
Computer Science , 2013,
Abstract: Second layer scattering descriptors are known to provide good classification performance on natural quasi-stationary processes such as visual textures due to their sensitivity to higher order moments and continuity with respect to small deformations. In a functional Magnetic Resonance Imaging (fMRI) experiment we present visual textures to subjects and evaluate the predictive power of these descriptors with respect to the predictive power of simple contour energy - the first scattering layer. We are able to conclude not only that invariant second layer scattering coefficients better encode voxel activity, but also that well predicted voxels need not necessarily lie in known retinotopic regions.
Data-driven HRF estimation for encoding and decoding models
Fabian Pedregosa,Michael Eickenberg,Philippe Ciuciu,Bertrand Thirion,Alexandre Gramfort
Computer Science , 2014, DOI: 10.1016/j.neuroimage.2014.09.060
Abstract: Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding score in two different datasets. Our results show that the R1-GLM model significantly outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency.
Region segmentation for sparse decompositions: better brain parcellations from rest fMRI
Alexandre Abraham,Elvis Dohmatob,Bertrand Thirion,Dimitris Samaras,Gael Varoquaux
Computer Science , 2014,
Abstract: Functional Magnetic Resonance Images acquired during resting-state provide information about the functional organization of the brain through measuring correlations between brain areas. Independent components analysis is the reference approach to estimate spatial components from weakly structured data such as brain signal time courses; each of these components may be referred to as a brain network and the whole set of components can be conceptualized as a brain functional atlas. Recently, new methods using a sparsity prior have emerged to deal with low signal-to-noise ratio data. However, even when using sophisticated priors, the results may not be very sparse and most often do not separate the spatial components into brain regions. This work presents post-processing techniques that automatically sparsify brain maps and separate regions properly using geometric operations, and compares these techniques according to faithfulness to data and stability metrics. In particular, among threshold-based approaches, hysteresis thresholding and random walker segmentation, the latter improves significantly the stability of both dense and sparse models.
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