Tous les articles par Valérie Chanoine

REVCOR F0 – CREx REPORT

 

Investing the cerebral representation of intonation using reverse-correlation fMRI
Pascal Belin (INT), Etienne Thoret (INT), Regis Trapeau (INT), Valérie Chanoine (ILCB). CREx: Training of students in pre-processing and analysis of fMRI data (MEEG) using mainly fmriprep and SMP12. New pipelines in python for animations of cerebral activation on cortical surfaces. 


Abstract. The project investigates whether the auditory cortex has a topographic organization for pitch contours, similar to tonotopy for frequency. It hypothesizes that secondary auditory areas represent different pitch contours (e.g., rising, falling, V-shape) in a structured manner. Using reverse-correlation fMRI, stimuli with random f0 contour variations are presented, and brain activity is analyzed to derive f0 contour “kernels” for each voxel, identifying optimal pitch contours. This study builds on previous research (Ponsot et al., 2018) that linked pitch contours to social judgments like Trustworthiness and Dominance. Pilot data from two participants show promising results, and further scans are ongoing. Two M2 students are contributing as part of their internships. Collaboration with Crex will enhance fMRI analysis methods and provide training. Reverse-correlation fMRI is a novel approach, and its success remains to be validated. The project builds on prior research with support from ILCB and BLRI. Led by Pascal Belin and Etienne Thoret, experts in auditory neuroimaging and reverse-correlation techniques, it benefits from complementary expertise. Adding advanced fMRI expertise strengthens the research. Findings will be relevant for understanding both verbal (prosody) and nonverbal (emotion) communication. Insights could inform how pitch perception mechanisms evolved in humans. The study aligns with ILCB’s research focus and has potential for high-impact publications.

PREDYS – CREx Report

Johannes Ziegler (CRPN), Elisa Gavard (CRPN), Yufei Tan (CRPN), Eddy Cavalli (EMC, Lyon), Jean-Luc Anton (INT), Valérie Chanoine (CREx) & Franziska Geringswald (CREx)


Abstract. The aim of this project is to improve the understanding of the role and neuro-functional bases of prediction and statistical learning in typical and atypical reading. Indeed, the idea of the ‘predictive brain’ has become a key concept in neuroscience (Friston and Kiebel, 2009) and cognitive science (Hohwy, 2013; Lupyan and Clark, 2015). According to this idea, the brain is a ‘machine’ that makes predictions about future events and seeks to minimise errors in these predictions, which is the basis of implicit and adaptive learning (Grossberg, 2012). A number of researchers have argued that prediction plays a key role in language comprehension. In the field of dyslexia, it has been shown that adult dyslexics make greater use of high-level linguistic information (semantics, morphology, syntax) to ‘compensate’ for their deficits in orthographic and phonological processing (Cavalli et al., 2017), which would amount to a form of prediction. It has also been suggested that a deficit in statistical learning could be at the root of dyslexia. This project will contribute new theoretical knowledge on this subject by evaluating the effects of prediction in adults with dyslexia, but also by specifying the nature of this information, the precise location of the neural networks involved, and the dissociation between semantic and syntactic processes, which is still controversial in the literature (Kuperberg et al., 2000; Tyler et al., 2001).


Experimental Support

Supervision of one (PhD, neuropsychology) student in :

  • conception of paradigm (fMRI and eye-tracking)
  • preprocessing (fMRI and eye-tracking)
  • univariate analyses for fMRI data
  • visualisation of research data
  • writing of methods and results

Conception of an experimental design adapted to fMRI and eye-tracking
Optimising of the stimuli randomisation using Neurodesign tool

fMRI Data preprocessing with fmriprep

We preprocessed the fMRI data using fmriprep (version 20.2.2; Esteban, Markiewicz, et al., 2018; Esteban, Blair, et al. ,2018), a robust and standardized pipeline, which applies distortion correction, realignment, and normalization to MNI space.

Univariate Whole Brain Analysis

The univariate whole brain analysis was performed with Statistical Parametric Mapping software (SPM12, https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) on Matlab R2022b (MathWorks Inc., Natick, MA).

Regions of interest Analysis

using python nilearn (https://nilearn.github.io/stable/index.html) and R (https://www.r-project.org/) for statistical analyses (mixed models)

Gavard, E., Chanoine, V., Geringswald, F., Anton, J-L., Cavalli, E., & Ziegler, J. C. (2025). Neural Networks for Semantic and Syntactic Prediction and Visuo-Motor Statistical Learning in Adult Readers with and without Dyslexia. Neurobiology of Language. doi: 10.1162/nol.a.8


Gloups MEG – CREX REPORT

Dataset for Evaluating the Production of Phonotactically Legal and Illegal Pseudowords Snežana Todorović (Jagiellonian University), Valérie Chanoine (ILCB), Bruno Nazarian (INT), Jean-Michel Badier (INS), Khoubeib Kanzari (INS), Andrea Brovelli (INT), Sonja A. Kotz (Maastricht University) & Elin Runnqvist (LPL). CREx: methodological supervision for a publication (Scientific data).


Abstract. Despite its central role for speaking in human interactions, the neural mechanisms sustaining speech motor sequence learning are still not fully understood. While several studies have explored this topic using fMRI (Segawa et al., 2015; Whitfield et al., 2017; Masapollo et al., 2020; Todorović et al., 2023), a precise understanding of the temporal dynamics related to the learning process necessitates a fine-grained temporal resolution as provided by magnetoencephalography (MEG). Consequently, we conducted a MEG experiment (“MEG-GLOUPS”) to highlight the spatio-temporal dynamics of such learning of new speech motor sequences.
The “MEG-GLOUPS” dataset offers a curated collection of raw magnetoencephalography recordings from seventeen French participants engaged in a syllable learning task involving the overt production of phonotactically legal and illegal pseudo-words. We also collected resting state data before and after the task.


Experimental Support


MRI and MEG preprocessing

To avoid participant identification, the T1-weighted MRI images were defaced using PyDeface (https://github.com/poldracklab/pydeface, Gulban et al., 2019) and organized according to the Brain Imaging Data Structure (Gorgolewski et al., 2019), MNE-BIDS (Niso et al., 2018). For the learning task, the trigger values in the raw MEG files were modified based on the participants’ response accuracy.

Dataset Structure (MNE BIDS)

The dataset is organized according to MNE Brain Imaging Data Structure (MNE BIDS) version 1.7.0 and publicly available on the OpenNeuro Dataset under a Creative Common Licence 0

Dataset description

This data collection was prepared in line with the guidelines of the review (Scientific Data). It includes comprehensive descriptions of the theoretical background, methods, data recordings, and technical validation. We also provide Python code utilizing MNE-Python to read the MEG raw files, convert BTi to FIF files, and organize the data in BIDS format.


 

 

 

EMOREC ML – CREx Report

Neural representation of intergenerational reading of facial expressions. Valérie Chanoine (ILCB), Marie-Hélène Grosbras (CRPN). CREx:  Pipeline in machine learning for fMRI analysis.


Abstract. The communication and reading of emotions via facial expression is ubiquitous in our daily exchange with our friends, partners, colleagues and kids. While a wealth of studies investigates reading of emotions in adults or children/adolescents, this project provides a first account of intergenerational emotional communication. In this project, participants watched adolescents and adults faces expressing different emotions during functional magnetic resonance imaging (fMRI). We expect higher similarity in patterns of activity produced for same-age faces, in face-specific occipitotemporal cortice, as well as in brain circuits involved in communication. This project will contribute to elucidating the effects of partner age on neural patterns engaged during (non-verbal) interactions.


Experimental Support

MRI Data preprocessing with fmriprep

We preprocessed the fMRI data using fmriprep (version 20.2.2; Esteban, Markiewicz, et al., 2018; Esteban, Blair, et al. ,2018), a robust and standardized pipeline, which applies distortion correction, realignment, and normalization to MNI space.

Univariate Whole Brain Analysis

The univariate whole brain analysis was performed with Statistical Parametric Mapping software (SPM12, https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) on Matlab R2022b (MathWorks Inc., Natick, MA).

Figure 1. Results of Whole Brain Analysis. Statistical T-maps for 30 participants were projected on an MNI cortical surface (left, right and posterior view). Activations correspond to significant
differences in three conditions: (A) Age: Adult versus Adolescent stimuli’; (B) Emotion: ‘Happy minus Angry and (C) Interaction between Age and Emotion [cluster threshold of p < .05, FWE corrected; MNI=Montreal Neurological Institute].

Multivariate Analysis

In order to take advantage of high spatial-frequency pattern information within each participants’ data, we estimated condition-specific responses using a general linear model (GLM) based on functional native-space images unnormalized and unsmoothed and anatomical native-space tissues to define the explicit mask [in working progress]

SING SING – CREx Report

Pre-attentive and attentive syllabic perception in professional singers and non-musicians.
Mireille Besson (CRPN), Jean-Michel Badier (INS), Valérie Chanoine (ILCB). CREx: Training of students in pre-processing and analysis of simultaneous EEG and MEG data (MEEG).  Pipelines using mainly MNE-python.


Abstract. Within the theoretical framework of transfer of training, this study aims to determine whether professional singers, trained in music and singing, perceive unfamiliar syllables (from an unknown foreign language) better than control non-musicians at both the pre-attentive andattentive levels. On top of studying music, singers often need to produce and to perceive words and syllables in a foreign language. We will present syllables from familiar and unfamiliar phonemic repertoires (French and Thai) and we will record changes in brain electrical (EEG) and magnetic activity (MEG) simultaneously to be able to directly compare the results gained from these two complementary methods. We hypothesize that professional singers will be more sensitive than non-musicians to unfamiliar syllables that vary in pitch and aspiration, two segmental features that do not belong to the French phonemic repertoire. This should translate into larger amplitude and shorter latency of the Mismatch Negativity (MMN) at the pre-attentive level and of the P3b component at the attentive level. Moreover, a second objective is to localize the brain generators of the MMN and P3b recorded at the scalp, with the hypothesis that source localization will be more precisefor MEG than for EEG. Finally, a third objective is to reconstruct the time course of the activity of the cortical structures involved in the elictation of MMN/P3b. Results will foster a deeper understanding of the influence of singing practice on the perception of syllables, the basic units of language perception, with potential implications for second language learning and more generally for education since early singing practice may help perceive and pronounce speech sounds. They will also help us pinpoint the respective advantages of the MEG and EEG methods for a better understanding of brain plasticity associated with transfer of training.


Experimental support


Pre-processing of EEG and MEG Data

EEG and MEG signals were recorded simultaneously, synchronized based on stimulus triggers and combined into one file using AnyWave (Colombet, Woodman, Badier & Bénar, 2015). EEG and MEG data were pre-processed using MNE-Python (Gramfort et al., 2013).

Sensor-Level Analysis

Figure 1. Regions of interest (ROIs) used for EEG and MEG sensor-level analyses. For EEG analysis (A), ROIs were selected from a 64-electrode cap template based on the 10-20 system. For MEG analysis (B), ROIs were selected from a 248 magnometer system template. They were chosen to match the positions of EEG ROIs as closely as possible. For both EEG and MEG, the following 9 ROIs were used for analyses: Frontal Left, Frontal Midline and Frontal Right in pink color; Central Left, Central Midline and Central Right in purple color; Parietal Left, Parietal Midline and Parietal Right in green color.


Figure 2. At fronto-central ROIs, large deviant stimuli elicited larger MMN than intermediate and small deviant stimuli. MMNs were analysed within two-time windows, highlighted in light orange, to capture the early MMN (100-190 ms) and the late MMN (260-420 ms). When the deviant x ROI interaction was significant, one-way repeated measures ANOVAs were conducted with deviance as a within-subject factor, for each time window and for each ROI. Significant p-values are indicated with a star (*: p< .05, **: p< .01, ***: p< .001).


Source-Level Analysis

Figure 3. Regions of interest (ROIs) used for EEG and MEG source-level analyses and defined from the Schaefer human brain atlas (Schaefer et al., 2018)

Morphosem


Bases Neurales des traitements morphologique et sémantique chez le lecteur adulte expert et dyslexique
Johannes Ziegler (CRPN), Eddy Cavalli (EMC, Lyon), Valérie Chanoine (ILCB, LPL), Pascale Colé (CRPN) &  Pascal Belin (INT)

 

Abstract


La dyslexie développementale est un des troubles spécifiques des apprentissages qui est actuellement le plus étudié. En effet, de nombreuses études cherchent à décrire et comprendre les facteurs risque de la dyslexie en identifiant les causes possibles et l’ensemble des déficits observés. Toutefois, très peu d’études sont actuellement conduites pour comprendre les facteurs de protection de la dyslexie susceptibles de limiter l’impact des déficits cognitifs que présentent ces individus en lecture et de leur permettre de réussir leur parcours académique. Dans ce contexte, l’objectif général de ce projet consiste à étudier et décrire les bases neurales de certains traitements impliqués dans la lecture et identifiés comme des facteurs de protection de la dyslexie : les traitements morphologiques et sémantiques. En comparant un groupe d’adultes lecteurs experts et d’adultes dyslexiques (tous étudiants à l’université), ce projet vise à décrire l’organisation (ou la réorganisation) fonctionnelle des représentations morphologiques et sémantiques pour éclairer les mécanismes et stratégies compensatoires dans la dyslexie chez l’adulte. L’originalité de ce projet est triple : D’abord, nous utilisons L’IRM fonctionnelle (IRMf) associée à la technique d’analyse multivariée de type RSA (Representational Similarity Analysis) pour étudier précisément la nature des représentations morphologiques, orthographiques et sémantiques dans des régions d’intérêt du cerveau et sur l’ensemble du cerveau ; puis nous utilisons une vraie tâche de lecture (lecture à voix haute de mots isolés) en non pas une tâche de décision lexicale avec ou sans amorce ; enfin nous utilisons des techniques de diffusions (DTI) récentes pour étudier la connectivité structurale chez l’adulte dyslexique. Ce projet a le potentiel de renseigner la théorie et les modèles neurolinguistiques du traitement morphologique et permettra de préciser un modèle neurocognitif de la compensation dans la dyslexie chez l’adulte.


Publications


Cavalli, E., Chanoine, V., Tan, Y., Anton, J-L., Giordano, B. L., Pegado, F., & Ziegler, J. C. (2024). Atypical Hemispheric Re-Organization of the Reading Network in High-Functioning Adults with Dyslexia: Evidence from Representational Similarity Analysis. Imaging Neuroscience,2: 1-23 doi: ⟨10.1162/imag_a_00070⟩. ⟨hal-04391146⟩


Tan, Y., Chanoine, V., Cavalli, E.,  Anton, J.-L. & Ziegler, J.C. (2022). Is there evidence for a noisy computation deficit in developmental dyslexia? Front. Hum. Neurosci. 16:919465. 10.3389/fnhum.2022.919465


Cavalli, E., Chanoine, V., Tan, Y., Anton, J-L., Giordano, B. L., Pegado, F., & Ziegler, J. C. Atypical Hemispheric Re-Organization of the Reading Network in High-Functioning Adults with Dyslexia: Evidence from Representational Similarity Analysis. Interactive paper (2023, October), Society for the Neurobiology of Language. Marseille France. 

 

Contribution CREx


  • Analyses univariée et multivariée (RSA) en IRMf
  • Formation de 2 étudiantes (master et doctorat)
  • Contribution aux publications (méthodologie)