A T M S Advanced Technologies For Medicine and Signals

A T M S مخبر البحث في التكنولوجيات المتقدمة في الإشارة و الطب



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KEYNOTE SPEAKERS





Julien DOYON

Julien DOYON


Director of the McConnell Brain Imaging Centre, The Neuro, McGill University
Canada

Title:

Brain and Cervical Spinal Cord Contributions to Motor Learning Examined Using Functional magnetic resonance imaging

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Abstract:

For more than 30 years, my laboratory has used psychophysical, electrophysiological and multimodal neuroimaging techniques in healthy individuals and clinical populations to investigate the behavioural determinants, neural substrates and neurophysiological mechanisms mediating the different learning phases (fast, slow, consolidation, automatization and retention) of motor skills. During this presentation, I will first review some of our work focusing on motor sequence learning (MSL) and will discuss our studies showing that the consolidation of this form of memory trace depends upon greater functional integration of the cortico-striatal system and non-rapid eye movement (N-REM) sleep spindle activity measured during the night following the initial training session. Yet despite such advances, models of motor skill learning have up until recently been incomplete because they do not account for the contribution of another important part of the central nervous system (CNS) : i.e., the cervical spinal cord (CSC). To address this knowledge gap, my group has pioneered a methodological technique that employs simultaneous brain/CSC functional magnetic resonance imaging (fMRI) during MSL in healthy individuals. In 2015 we used this new imaging method to provide the first ever evidence for local, intrinsic plasticity within the CSC, as well as connectivity changes with the sensorimotor cortex and cerebellum over the course of MSL using neuroimaging. Later, we then reported that further local functional plasticity within the CSC can be observed at different phases (fast vs. slow) of MSL using both univariate and data-driven multivariate approaches, depending on the group of muscles involved in the motor task. Finally, I will then discuss these results and highlight a few possible applications of this brain/CSC imaging technique.

Biography:

Julien DOYON, PhD, is the Director of the McConnell Brain Imaging Centre. He has held several leadership roles related to neuroimaging, including most recently as Scientific Director of the Unité de Neuroimagerie Fonctionnelle at the Centre de recherche, Institut universitaire de gériatrie de Montréal, Director of the Quebec Bio-Imaging Network, and codirector of the Laboratoire international de neuroimagerie et modélisation, INSERM‐ Université de Montréal. .





Christophe GROVA

Christophe GROVA


Associate Professor, Physics / PERFORM Centre, Concordia University, Montreal, Canada
Adjunct Professor, Biomedical Engineering Dpt, McGill University, Montreal Canada

Title:

EEG/MEG source imaging of transient and oscillatory epileptic brain activity using the maximum entropy on the mean framework

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Abstract:

Accurate delineation of the epileptogenic zone (EZ) during presurgical workup of focal drug-resistant epilepsy patients can be challenging. Stereo-electroencephalography (SEEG) recordings, considered as the gold-standard for the localization of the EZ, might be the step towards mapping the seizure-onset zone (SOZ) and determining surgical candidacy. However, a successful investigation requires a strong pre-implantation hypothesis on the localization of the EZ, which can be derived from non-invasive investigations such as EEG or Magnetoencephalography (MEG) source imaging. The purpose of this talk is to introduce the Maximum Entropy on the Mean (MEM) source imaging framework, as a Bayesian approach to solve the ill-posed inverse problem of localizing the generators of EEG and MEG signals along the cortical surface. We will first review the time-domain version of MEM, which is sensitive to the spatial extent of the underlying generators, notably when localizing transient epileptic discharges. The localization accuracy of the MEM method and its ability to recover the spatial extent of the generators was quantitatively validated using SEEG (Abdallah et al Neurology 2022) or surgical cavity and postsurgical outcome as ground truth. In the second part of the talk, we will introduce the time-frequency wavelet-based extension of MEM (wavelet MEM) as a source image method of interest to localize transient oscillations, such as ictal oscillations localizing the seizure onset zone, transient high frequency oscillations and also resting state ongoing oscillations. The accuracy of wavelet-based MEM to recover oscillatory power spectra from resting state MEG data was validated using the MNI SEEG atlas of normal brain activity as ground truth (Afnan et al Neuroimage 2023).

Biography:

Christophe GROVA, After completing in 2002 a PhD in biomedical engineering from University of Rennes 1 (France, 1998-2002), validating multimodal image registration techniques, Dr. GROVA did a postdoctoral fellowship at the Montreal Neurological Institute (McGill University, Montreal, Canada) under the supervision of Dr. J. Gotman, studying simultaneous Electro-EncephaloGraphy (EEG)/functional Magnetic Resonance Imaging (fMRI) investigations of epileptic activity and developing expertise in EEG and MagnetoEncephaloGraphy source imaging. Recruited as assistant Professor in Biomedical Engineering and Neurology and Neurosurgery departments at McGill University in July 2008, he created the Multimodal Functional Imaging Lab, aiming at characterizing normal and pathological brain activity, especially epilepsy, combining bioelectrical neuronal activity using electrophysiology. In July 2014, he joined Concordia University as “tenure track” assistant Professor in the department of Physics of Concordia University, in the context of a strategic recruitment with PERFORM centre. PERFORM is new multimodal imaging center developed at Concordia University, dedicated to the promotion of research projects involving prevention in health and lifestyle experiences (sleep, physical exercise, nutrition). Promoted associate Professor with tenure since July 2017, his research involves combining different neuroimaging modalities to characterize brain activity, from a bio-electrical point of view (Electro-and MagnetoEncephalography) as well as from an hemodynamic perspective (functional Magnetic Resonance Imaging, Near Infra-red Spectroscopy), to study neurovascular coupling processes and ongoing resting state activity. .





Habib BEN ALI

Habib BEN ALI


Scientific Director, PERFORM Centre and Professor, Department of Electrical and Computer Engineering Faculty of Engineering and Computer Science Concordia University Montreal, QC,
Canada

Title:

Digital brain activity and pathology in Alzheimer disease: Toward a predictive physiopathology

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Abstract:

The objective of this research program is to better understand brain activity in healthy aging and to shed light on factors predicting conversion to neurodegenerative disease. Indeed, understanding neuronal activity, brain metabolism and patho-physiological process will enable the development of innovative computational models by combining biological and biomedical images from the basic modelling of the brain's anatomo-functional networks to models of tau protein accumulation. In such “virtual brain activity and pathology” environment, the outcome of brain disease development for individual subjects can be foreseen by simulations. Numerical simulation tools would allow prediction of the progress of the disease as well as an understanding of its causes, which remain uncertain. This computational approach is a new paradigm of the study of the patho-physiological process in healthy aging of subjects at-risk for neurodegenerative disease. This approach, referred to as “predictive physiopathology”, offer better health through prevention.

Biography:

Habib BEN ALI completed my PhD in Applied Mathematics and Statistics at Rennes University in 1985. He joined the French National Institute of Health and Medical Research (INSERM) in 1989. He served as Head of the Laboratory of Functional Imaging (INSERM U678 unit with over 65 members) from 2008 to 2013 and Deputy Director of the Biomedical Imaging Laboratory (with over 100 members), INSERM - The National Center for Scientific Research (CNRS) and Sorbonne University until 2015. Together with Prof. Julien DOYON, from the Université de Montréal (UdeM), he founded and became co-director of the International Laboratory of Neuroimaging and Modelisation of the INSERM-Sorbonne University and UdeM administrative bodies in 2007. He is a regular research member of the Centre de Recherche Mathématiques of UdeM since 2002 and a researcher at Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, UdeM, since 2005. He is currently the Scientific Director for the PERFORM Centre, NSERC Canada Research Chair Tier 1 in Biomedical Imaging and Healthy Aging and Professor at the Faculty of Engineering and Computer Science at Concordia University. In the last 30 years, he has gone from starting a new neuroimaging research activity to directing a productive laboratory in France and Canada with activities that span from the development of new mathematical modeling and image analysis techniques to cognitive neuroscience and clinical applications. During this productive time in his scientific career, he published more than 300 peer reviewed papers in the best scientific journals. He is a member of national and international per review committees, executive committees and member of Advisory Board & committees.




Mehrez ZRIBI

Mehrez ZRIBI


CESBIO
France

Title:

Abstract:

Biography:

M. ZRIBI is a Director of Research in CNRS (French National Scientific Research Center) and the Head of CESBIO (Centre d’Etudes Spatiales de la BIOsphère). In 1995, he joined the Centre d’Etude des Environnements Terrestre et Planétaires Laboratory [Institut Pierre Simon Laplace/CNRS], Vélizy, France. In 2001, he joined CNRS organism to develop microwave remote sensing research applied to land surface. Since October 2008, he has been with the Centre d’Etudes Spatiales de la Biosphère, Toulouse. During the period (2008-2012), he was with the French Institute of Research for Developement to develop researches in water resources based on remote sensing in semi-arid regions. He is expert on microwave remote sensing applied to land surfaces. Mehrez ZRIBI has published actively in refereed journals with high impact factor (150 papers). He coordinated publication of 20 books about remote sensing for land surfaces. His H index (Web of Science) is equal to 48. He coordinated and participated to several research projects funded by different research programs (CNES, ANR, ESA, FP5, FP6, FP7 etc). He is associated editor for three international journals (Geophysical instrumentation, Methods and data systems (EGU), Nature/Scientific Reports and Remote Sensing/MDPI). He is senior member of IEEE Geoscience and Remote Sensing.




bhiksha_raj

Bhiksha RAMAKRISHNAN


Carnegie Mellon University, Pittsburgh,
PA, United States

Title:

Privacy and Security Issues in Speech Processing

Abstract:

Biography:

Dr. Bhiksha RAMAKRISHNAN is a tenured (full) professor of Computer Science at Carnegie Mellon University. Dr. RAMAKRISHNAN completed his Ph.D in Electrical engineering and Computer Science from Carnegie Mellon University, USA, in 2000. He was at Compaq (Cambridge) Research Lab until 2001. From 2001 to 2008 he led Speech Research at Mitsubishi Electric Research Labs. Since 2008 he has been a full-time faculty at Carnegie Mellon. Over his career Dr. RAMAKRISHNAN has made pioneering contributions to three broad areas of research: Speech and Audio Processing, Privacy and Security in Multimedia Processing, and lately, Deep Learning and AI. He holds over 30 patents in these areas, is co-editor of three technical books and has published over over 360 research papers in peer-reviewed journals and conferences. His current research spans topics of high contemporary importance, such as exploiting data and structure redundancy for deep learning and AI systems, preserving user privacy in speech and audio processing systems, learning and evaluating classifiers under real-world labeling assumptions, and robustness of AI systems to adversarial attacks. He is a fellow of the IEEE. .




Gérard CHOLLET

Gérard CHOLLET


Télécom-SudParis (SAMOVAR),
France

Title:

Abstract:

Biography:

Pr. Gérard CHOLLET studied Linguistics, Electrical Engineering and Computer Science at the University of California, Santa Barbara where he was granted a PhD in Computer Science and Linguistics. He taught at Memphis State University and University of Florida before joining CNRS. In 1981, he was asked to take in charge the speech research group of Alcatel. In 1983, he joined a newly created CNRS research unit at ENST (Telecom-ParisTech within Institut Mines-Telecom). In 1992, he was asked to participate to the development of IDIAP, a new research laboratory of the `Fondation Dalle Molle' in Martigny, Switzerland. From 1996 to 2012, he was back full time at ENST, managing research projects and supervising doctoral work. He supervised more than forty doctoral thesis. CNRS decided in July 2012 to grant him an emeritus status. He visited the Boise State University in 2013 and the University of Eastern Finland in 2014. He is now VP of Research at Intelligent Voice. His main research interests are in phonetics, automatic audio-visual speech processing, speech dialog systems, multimedia, pattern recognition, biometrics, privacy-preserving digital signal processing, speech pathology and speech training aids. His main publications are available from his Google Scholar Citations profile.