Multiple Subject Learning for Inter-Subject Prediction

International audienceMulti-voxel pattern analysis has become an important tool for neuroimaging data analysis by allowing to predict a behavioral variable from the imaging patterns. However, standard models do not take into account the differences that can exist between subjects, so that they perform poorly in the inter-subject prediction task. We here introduce a model called Multiple Subject Learning (MSL) that is designed to effectively combine the information provided by fMRI data from several subjects; in a first stage, a weighting of single-subject kernels is learnt using multiple kernel learning to produce a classifier; then, a data shuffling procedure allows to build ensembles of such classifiers, which are then combined by a majority vote. We show that MSL outperforms other models in the inter-subject prediction task and we discuss the empirical behavior of this new model

MKPM: A multiclass extension to the kernel projection machine

International audienceWe introduce Multiclass Kernel Projection Machines (MKPM), a new formalism that extends the Kernel Projection Machine framework to the multiclass case. Our formulation is based on the use of output codes and it implements a co-regularization scheme by simultaneously constraining the projection dimensions associated with the individual predictors that constitute the global classifier. In order to solve the optimization problem posed by our formulation, we propose an efficient dynamic programming approach. Numerical simulations conducted on a few pattern recognition problems illustrate the soundness of our approach

Inter-subject pattern analysis A straightforward and powerful scheme for group-level MVPA

International audienceMultivariate pattern analysis (MVPA) has become vastly popular for analyzing functional neuroimaging data. At the group level, two main strategies are used in the literature. The standard one is hierarchical, combining the outcomes of within-subject decoding results in a second-level analysis. The alternative one, inter-subject pattern analysis, directly works at the group-level by using, e.g. a leave-one-subject-out cross-validation. This study provides a thorough comparison of these two group-level decoding schemes, using both a large number of artificial datasets where the size of the multivariate effect and the amount of inter-individual variability are parametrically controlled, as well as two real fMRI datasets comprising 15 and 39 subjects, respectively. We show that these two strategies uncover distinct significant regions with partial overlap, and that inter-subject pattern analysis is able to detect smaller effects and to facilitate the interpretation. The core source code and data are openly available, allowing to fully reproduce most of these results

Faster Sound Stream Segmentation In Musicians Than In Nonmusicians

The musician's brain is considered as a good model of brain plasticity as musical training is known to modify auditory perception and related cortical organization. Here, we show that music-related modifications can also extend beyond motor and auditory processing and generalize (transfer) to speech processing. Previous studies have shown that adults and newborns can segment a continuous stream of linguistic and non-linguistic stimuli based only on probabilities of occurrence between adjacent syllables, tones or timbres. The paradigm classically used in these studies consists of a passive exposure phase followed by a testing phase. By using both behavioural and electrophysiological measures, we recently showed that adult musicians and musically trained children outperform nonmusicians in the test following brief exposure to an artificial sung language. However, the behavioural test does not allow for studying the learning process per se but rather the result of the learning. In the present study, we analyze the electrophysiological learning curves that are the ongoing brain dynamics recorded as the learning is taking place. While musicians show an inverted U shaped learning curve, nonmusicians show a linear learning curve. Analyses of Event-Related Potentials (ERPs) allow for a greater understanding of how and when musical training can improve speech segmentation. These results bring evidence of enhanced neural sensitivity to statistical regularities in musicians and support the hypothesis of positive transfer of training effect from music to sound stream segmentation in general

Functional connectivity within the voice perception network and its behavioural relevance

International audienceRecognizing who is speaking is a cognitive ability characterized by considerable individual differences, which could relate to the inter-individual variability observed in voice-elicited BOLD activity. Since voice perception is sustained by a complex brain network involving temporal voice areas (TVAs) and, even if less consistently, extra-temporal regions such as frontal cortices, functional connectivity (FC) during an fMRI voice localizer (passive listening of voices vs non-voices) has been computed within twelve temporal and frontal voice-sensitive regions ("voice patches") individually defined for each subject (N ¼ 90) to account for inter-individual variability. Results revealed that voice patches were positively co-activated during voice listening and that they were characterized by different FC pattern depending on the location (anterior/posterior) and the hemisphere. Importantly, FC between right frontal and temporal voice patches was behaviorally relevant: FC significantly increased with voice recognition abilities as measured in a voice recognition test performed outside the scanner. Hence, this study highlights the importance of frontal regions in voice perception and it supports the idea that looking at FC between stimulus-specific and higher-order frontal regions can help understanding individual differences in processing social stimuli such as voices

Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI

The past, present, and future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

A multi-source perspective on inter-subject learning : Contributions to neuroimaging

L’apprentissage inter-sujet consiste à fournir des prédictions sur des données d'un sujet humain non présent dans la base d’apprentissage, comme dans l’aide au diagnostic où un ordinateur doit prédire si un sujet inconnu est sain ou malade. Dans cette thèse, nous défendons le point de vue que ce problème doit être formalisé dans le cadre multi-source, où chaque sujet d’apprentissage fournit une source de données. Nous présentons ensuite trois contributions destinées à des applications en neuroimagerie.La première est une méthode de prédiction inter-sujet pour données d'IRM fonctionnelle. La variabilité inter-sujet fait que les espaces d’entrée sont tous différents. Nous construisons un espace commun à l'aide de graphes et d'un noyau de graphe, qui projette ces données dans un espace de hilbert à noyau reproduisant. Nous démontrons l’efficacité de cette approche sur des données de tonotopie enregistrées dans le cortex auditif.La deuxième est une méthode de morphométrie corticale. Nous construisons des graphes à partir des extrema de profondeur du cortex, que nous projetons dans un espace commun grâce à un noyau de graphe. Une méthode d’inférence spatiale permet l’identification des zones du cortex qui présentent des différences entre populations. Nous étudions avec cette méthode les asymétries corticales et les différences inter-sexe.La troisième est une méthode d’adaptation de domaine multi-source. Nous décrivons une extension du kernel mean matching au cas où l’ensemble d’apprentissage se compose de plusieurs sources de données et des résultats préliminaires sur une tâche de classification inter-sujet dans une expérience de magnéto-encéphalographie.Inter-subject learning consists in giving predictions on data from a subject not present in the training database, as with computer-aided diagnosis where the computer has to guess wether an unknown individual is healthy or sick. In this thesis, we argue that inter-subject learning should be handled in the multi-source framework where each subject is a different source of data. We then introduce three original contributions for neuroimaging applications.The first one is a method for inter-subject predictions of fMRI data. Because of the inter-subject variability, the original feature spaces are all different. Using graphs and a graph kernel, the input patterns are implicitly projected into a common reproducing kernel hilbert space. We show the effectiveness of this method on tonotopy data recorded in the auditory cortex.The second one is a cortical morphometry method. We design graphs from the deepest points of cortical sulci, and we project them into a common space using a graph kernel. A spatial inference method is then proposed to perform the detection of cortical zones where populations are different. Using this method, we study cortical asymmetries and gender differences.The third contribution of this thesis is a multi-source domain adaptation technique. Our method is an extension of the kernel mean matching for the multi-source case. We present preliminary results on a inter-subject prediction task used to analyse data from a magneto-encephalography experiment

Demo dataset (VSD functional imaging) for the Vobi One software suite

See published paper:<br><br><div><div> Front. Neurosci., 24 January 2014 | <a href="https://doi.org/10.3389/fnins.2014.00002">https://doi.org/10.3389/fnins.2014.00002</a> </div> </div> <div> <a></a>Vobi One: a data processing software package for functional optical imaging <div> <a href="http://www.frontiersin.org/people/u/87568">Sylvain Takerkart</a>¹^*, Philippe Katz^1,2, Flavien Garcia¹, <a href="http://www.frontiersin.org/people/u/131152">Sébastien Roux</a>¹, <a href="http://www.frontiersin.org/people/u/123500">Alexandre Reynaud</a>³ and <a href="http://www.frontiersin.org/people/u/25234">Frédéric Chavane</a>¹</div> <ul><li>¹Institut de Neurosciences de la Timone UMR 7289, CNRS - Aix Marseille Université, Marseille, France</li><li>²LabISEN, Vision Department, Institut Supérieur de lElectronique et du Numérique, Brest, France</li><li>³McGill Vision Research, Department of Ophtalmology, McGill University, Montréal, QC, Canada</li></ul></div><br

Un point de vue multi-source pour l'apprentissage inter-sujet. Contributions pour la neuroimagerie.

Inter-subject learning is a family of learning problems encountered in the analysis of data recorded in human subjects where we need to perform predictions on data recorded from a subject that was not available at training time. The most usual problem that uses inter-subject learning is to ask whether an unknown individual is healthy or sick, i.e to design a computer-aided diagnosis tool. In this thesis, we argue that such inter-subject learning questions should be addressed within the multi-source learning framework, and we formalize it as such in the context of neuroimaging studies. Indeed, each subject is a different source of data, with data samples that potentially live in different feature spaces and that are drawn from different probability distributions. The multi-source setting therefore constitutes an extenstion of the domain adaptation problem where a single source of training data is available. We then introduce three original contributions motivated by inter-subject learning questions in neuroimaging.The result of our first contribution is a method that is able to perform reliable inter-subject predictions from fMRI data using fine-scale spatial patterns defined within a region of interest. Because of the strong inter-subject variability present at such fine scale, the original feature spaces are different across subjects. Our contribution consists in designing a common space for the patterns of all subjects using graphical representations of the patterns together with a graph kernel that implicitly projects the samples into a reproducing kernel hilbert space. We show that this approach is effective through the increased accuracy achieved on an inter-subject prediction task designed to study the functional organization of thehuman auditory cortex.Our second contribution is a new method that enables to detect local differences in cortical shape across groups of anatomical MRI scans. The objects used to detect such differences are, yet again, graphical reprentations, this time designed from the spatial organization of the sulcal pits – the deepest points of cortical sulci. Using a graph kernel designed for these objects allows to project them into a reproducing kernel hilbert space and to quantify the differences between groups through the performances of a classifier trained to recognize these groups. A non-parametric spatial inference method is then proposed to perform the detection of cortical zones where the differences are statistically significant. We validate this method by showing that it detects cortical asymmetries and gender differences using a large database of healthy subjects.The third contribution of this thesis is a multi-source domain adaptation technique. Our method builds upon the kernel mean matching, a distribution matching procedure that estimates importance weights for the training samples so that the weighted source distribution matches more closely the target distribution than the unweighted one. We introduce an extension of the kernel mean matching for the multi-source case, i.e when the training samples are drawn from several sources of data. We present preliminary results of this framework on a inter-subject prediction task used to analyse data from a magneto-encephalography experiment.L’apprentissage inter-sujet intervient dans l’analyse des données enregistrées chez des sujets humains, lorsque le sujet chez lequel on doit faire une prédiction ne faisait pas partie de la base d’apprentissage. Le plus typique de ces problèmes est l’aide au diagnostic, lorsque on demande à un outil informatique si un sujet, inconnu jusque là, est sain ou malade. Dans cette thèse, nous défendons le point de vue que le problème d’apprentissage inter-sujet doit être formalisé comme un problème multi-source dans lequel chaque sujet de la base d’apprentissage fournit une source de données enregistrées dans un espace d’entrée potentiellement différent et qui sont des réalisations de distributions différentes. Le cadre multi-source est ainsi une généralisation du problème d’adaptation de domaine, dans lequel une seule source de données est disponible. Nous présentons ensuite trois contributions motivées par des problèmes d’apprentissage inter-sujet en neuroimagerie.Le résultat de notre première contribution est une méthode qui permet de produire des prédictions inter-sujet sur des données d’IRM fonctionnelle en utilisant les patrons d’activation disponibles à des échelles spatiales relativement fines disponibles dans une région d’intérêt du cortex. Du à la forte variabilité fonctionnelle inter-sujet, les espaces d’entrée dans lesquels vivent ces patrons sont différents au travers des sujets. Notre contribution consiste à construire un espace commun pour tous les sujets en utilisant une représentation graphique des patrons d’activation ainsi qu’un noyau de graphe qui projette implicitement ces représentations dans un espace de hilbert à noyau reproduisant. Nous avons démontré l’efficacité de cette approche grâce à l’amélioration de la performance de classification dans un tâche de prédiction inter-sujet construite pour étudier l’organisation fonctionnelle du cortex auditif.La deuxième contribution présentée dans cette thèse est une nouvelle méthode qui permet l’identification de différences de formes locales du cortex entre plusieurs groupes d’observations. Les objets utilisés sont, une fois de plus, des représentations graphiques, cette fois construites à partir des points correspondant à des extrema de profondeur des sillons corticaux. L’utilisation d’un noyau de graphe adapté à ces objets permet, dans l’ espace de hilbert à noyau reproduisant correspondant, de quantifier les différences entre groupes d’observations par la performance d’un classifieur entraîné à reconnaître ces groupes. Une méthode d’inférence spatial non paramétrique permet ensuite la détection, c’est à dire l’identification des zones du cortex qui présentent des différences significatives. Nous validons cette méthode en démontrant qu’elle permet d’identifier, sur une large population de sujets sains, des asymétries corticales ainsi que des différences inter-sexe.La troisième contribution est une méthode d’adaptation de domaine pour le cas multi-source. Notre méthode se base sur le kernel mean matching, une procédure d’appariement de distributions qui adapte la distribution de l’ensemble d’entrainement à celle de l’ensemble de test par une pondération des exemples d’apprentissage. Nous décrivons une extension du kernel mean matching au cas où l’ensemble d’apprentissage se compose de plusieurs sources de données. Nous présentons des résultats préliminaires sur une tâche de classification inter-sujet dans une expérience de magnéto-encéphalographie