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.Development of the BIDS Standard has been supported by the International Neuroinformatics Coordinating Facility, Laura and John Arnold Foundation, National Institutes of Health (R24MH114705, R24MH117179, R01MH126699, R24MH117295, P41EB019936, ZIAMH002977, R01MH109682, RF1MH126700, R01EB020740), National Science Foundation (OAC-1760950, BCS-1734853, CRCNS-1429999, CRCNS-1912266), Novo Nordisk Fonden (NNF20OC0063277), French National Research Agency (ANR-19-DATA-0023, ANR 19-DATA-0021), Digital Europe TEF-Health (101100700), EU H2020 Virtual Brain Cloud (826421), Human Brain Project (SGA2 785907, SGA3 945539), European Research Council (Consolidator 683049), German Research Foundation (SFB 1436/425899996), SFB 1315/327654276, SFB 936/178316478, SFB-TRR 295/424778381), SPP Computational Connectomics (RI 2073/6-1, RI 2073/10-2, RI 2073/9-1), European Innovation Council PHRASE Horizon (101058240), Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative, ERAPerMed Pattern-Cog, and the Virtual Research Environment at the Charité Berlin – a node of EBRAINS Health Data Cloud.N

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