Event is free but registration is required.
Register at mcgovern.science/MEGIN
This exciting new event, “McGovern-MEGIN symposium: MEGnificent brain discoveries,” aims to showcase innovative applications of MEG neuroimaging technology and bring into spotlight the MEG core facility at MIT’s McGovern Institute.
The event features two keynote lectures by leading MEG experts, Professor David Poeppel and Professor Sylvain Baillet, as well as a MEG software tutorial workshop.
Schedule:
1:00 – 2:00 PM Lecture 1 – Professor David Poeppel; There can be no neuroscience of language without MEG.
2:00 – 3:00 PM Lecture 2 – Professor Sylvain Baillet; Systems Neurophysiology of Predictive Mechanisms in Human Perception and Cognition.
3:00 – 3:30 PM Coffee Break
3:30 – 4:30 PM MEG Software Presentation (MNE Python, Brainstorm, FIND Neuro)
4:30 – 5:00 PM Reception
About the speakers:
Professor David Poeppel, PhD
Professor of Psychology and Neural Science, Department of Psychology, New York University
Co-Director, Center for Language, Music, and Emotion, New York University
Scientific Director and CEO, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Germany
Professor Sylvain Baillet, PhD
Professor, Montreal Neurological Institute Associate Dean (Research), Faculty of Medicine & Health Sciences McGill University
Organizers:
Dimitrios Pantazis, PhD, Principal Research Scientist, McGovern Institute for Brain Research, Director, MEG lab, Martinos Imaging Center
McGovern Institute for Brain Research, MIT
MEGIN. MEGIN is the global leader for Magnetoencephalography (MEG) Technology. We are experts in detecting and visualizing brain function, working together with clinicians, scientists, and healthcare organizations, as well as other partners to develop, deliver and support our MEG technology. Our technology and its applications transform neuroscience research and clinical decision making to improve people’s health.
Abstracts:
There can be no neuroscience of language without MEG
Systems Neurophysiology of Predictive Mechanisms in Human Perception and Cognition
Abstract: A challenging question in systems neuroscience is understanding the mechanisms of information integration in the brain: How do sensory inputs interact with ongoing neural activity? What is the nature of the convergence or tension between external inputs and the mental representations of our environment? How are these mechanisms altered in disease?
We have recently introduced a model for system dynamics in hierarchical brain networks, rooted in the concept of polyrhythmic oscillatory brain activity. This mechanistic framework implements a generic form of contextual predictive inference of the input signals of brain networks. Essentially, this model aligns with the principles of perceptual inference, suggesting that wakeful, spontaneous brain activity continuously shapes our self-representation of the environment and potential actions.
Leveraging this framework, I will review various neurophysiological MEG data in support of this hypothesis, spanning multiple brain functions and underscored by growing empirical evidence for individual-specific neurophysiological ‘brain fingerprints’. I will also discuss how artificial neural networks, trained using naturalistic stimuli, can be leveraged to identify the brain’s signaling pathways related to contextual uncertainty and prediction errors in perception, such as in natural speech processing. Finally, I will illustrate how these concepts and associated methodologies may pave the way for novel approaches in the study of Parkinson’s, Alzheimer’s, and other brain disorders.
