Sylvain Baillet's lab contributes advances to understand human brain activity: from milliseconds to the life span.
Our broad objective is to comprehend the mechanisms of large-scale brain rhythms (a.k.a. oscillatory neural dynamics): how they enable functions by shaping communication in brain networks, and how the earliest detection of their alterations in disease can contribute to improved healthcare prevention and interventions. To achieve this goal, our group has a strong backbone of expertise in imaging methods and experimental neuroscience, complemented with collaborations in computational and disease models, neuromodulation techniques, and translational arms to the clinic and industry.
Our rationale is that so far, the ubiquitous polyrhythmic activity of the brain has been approached empirically, with underlying mechanisms that remain not understood. This hinders our comprehension of how 1) perception and behaviour emerge from brain network activity, and 2) the pathophysiological developments of brain and mental-health disorders increasingly studied as network diseases, affect large-scale neural communication.
Our vision is that these difficult questions require a bottom-up approach: We need to understand how basic physiological factors of neural integrity and function shape the dynamical structure of oscillatory brain rhythms, such as their interdependence across multiple frequencies through cross-frequency coupling. These phenomena represent a deep source of uncharted markers of neural integrity, excitability, activity and connectivity.
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Read more about our latest research just published in Nature Neuroscience and Neuron.
To address our science questions, we develop innovative approaches for structural and time-resolved functional neuroimaging. Our method of choice is electromagnetic imaging with MEG, complemented with other modalities such as Magnetic Resonance Imaging (MRI), basic electrophysiology and neurostimulation techniques.
Our network of collaborators has hubs in: computational models (Skinner, U of Toronto), rodent e-phys (Avoli, de Villers-Sidani, MNI; Williams, Douglas), congenital amusia (Peretz, U of Montreal), epilepsy model (Dudley, Montreal Children's Hospital), Alzheimer disease (Breitner, Gauthier, Rosa-Neto, Williams, McGill/Douglas Institute; Dang-Vu, Concordia University), TMS/tACS (Zatorre, MNI), data repositories (Evans, MNI; Rajah, Douglas Institute; Farivar, RI-MUHC), autism & schizophrenia (Jacquemont, U of Montreal; Forgeot d'Arc, Rivière des Prairies), ADHD (Brisebois, Szabo; College Montmorency), motor & decision systems (Kalaska, U of Montreal), nanoprobes (Kinsella, McGill/Bioengineering), multimodal imaging (Boudrias, Hoge, Mitsis, McGill), software developments (Leahy, USC; Mosher, Cleveland Clinic; Hämälainen, Harvard Medical School).
Our lab also provides continuous support and expertise to investigators interested in using MEG as a tool for their cognitive and clinical neuroscience research. The MEG core unit at McGill’s Montreal Neurological Institute is part of the McConnell Brain Imaging Centre, and is an open platform for academic researchers and industrial partners.
We are funded by (inter)national public – including CIHR, NSERC, SSHRC, NIH, Brain Canada, Compute Canada, CFI – and private sources (gross total obtained: $26.2M; 21 presently active grants).
We are advocates of open science, as a vector of research productivity and reproducibility: in 2014-15, our group led a Quebec Bioimaging Network strategic initiative for open data (the Open MEG Archive), and we host with RO1 NIH support since 2009, the international developments of Brainstorm, a widely used open-source software (>15,000 registered downloads) application for multimodal functional brain imaging.