Event will also be live streamed. Link is available below.
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 symposium features three keynote lecutres by leading experts, Professor David Poeppel, Assistant Professor Leyla Isik, and Professor Sylvain Baillet, as well as a MEG Software Presentation (MNE Python, Brainstorm, FIND Neuro).
1:00 – 1:45 PM Lecture 1 - David Poeppel; There can be no neuroscience of language without MEG.
1:45 – 2:30 PM Lecture 2 - Leyla Isik; From neural dynamics to neural computations: recognizing objects, actions, and social interactions
2:30 – 3:15 PM Lecture 3 - Sylvain Baillet; Systems Neurophysiology of Predictive Mechanisms in Human Perception and Cognition.
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
Clare Boothe Luce Assistant Professor, Department of Cognitive Science, Johns Hopkins Krieger School of Arts and Sciences
Professor Sylvain Baillet, PhD
Professor, Montreal Neurological Institute Associate Dean (Research), Faculty of Medicine & Health Sciences McGill University
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.
There can be no neuroscience of language without MEG
From neural dynamics to neural computations: recognizing objects, actions, and social interactions
The human brain can quickly make sense of the visual world to recognize objects, people, and even what those people are doing. How do our brains extract all of this information with such speed and ease? In this talk, I will first argue that high temporal resolution information, like that from MEG data, is critical to understanding computations in the brain. I will discuss studies that combine multivariate MEG decoding with computational modeling to understand the neural basis of different aspects of high-level vision, including object, action and social interaction recognition. In the second part of my talk, I will discuss recent work using EEG-fMRI fusion methods to differentiate between computational processes used during social scene understanding.
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.