Welcome to the MEG lab at MIT!
The Magnetoencephalography (MEG) lab operates as a core facility at MIT and is part of MIT's Martinos Imaging Center at the McGovern Institute for Brain Research. It is accessible to all members of the local research community.
The McGovern Institute for Brain Research building on the MIT campus.
The director of the MEG lab is Dimitrios Pantazis. His group focuses on the following main research lines: i) Development and validation of novel MEG and multimodal neuroimaging methods; ii) Novel approaches to diagnose and study brain disorders, with emphasis on Alzheimer's disease; iii) Study of cognitive function with focus on human visual recognition.
MEG Lab presence at the MEG North America 2023 Workshop
Two of our group members received awards for their outstanding contributions! Congratulations to our group members, Amita Giri and Hugo Ramirez, for both being recipients of Outstanding Trainee Speaker Awards! Amita was honored for her work titled "Temporal dynamics of age, gender, and identity representations invariant to head views for familiar faces", while Hugo was received recognition for his work titled "Fully hyperbolic neural Networks: A novel approach to discover aging trajectories from MEG brain networks". Overall, the group made a significant impact at the event, delivering a total of four oral presentations.
Research Highlight: Graph Convolutional Networks in Alzheimer's Disease
Alzheimer's disease is a network-based disease affecting large-scale brain systems involving the medial temporal and heteromodal cortices, making these networks highly promising, quantitative, disease biomarkers. We have been developing deep learning algorithms tuned to brain network analyses (graph convolutional networks). These algorithms, called graph convolutional networks are the generalization of convolutional neural networks to graph-structured (network) datasets, akin to MEG connectivity networks in Alzheimer's disease.
Research Highlight: How face perception unfolds over time
In a Nature Communications article, we resolved the time course of face processing in the human brain with MEG. We found that facial gender and age information emerged before identity information, suggesting a coarse-to-fine processing of face dimensions. We also found that identity and gender representations of familiar faces were enhanced very early on, suggesting that the behavioral benefit for familiar faces results from tuning of early feed-forward processing mechanisms.
Research Highlight: Refined spatiotemporal maps of brain activation
Our novel MEG-fMRI fusion technique, published in Nature Neuroscience, enables a unique view of human brain function with millisecond-millimeter resolution. Using this method, we produced a first-of-its-kind movie revealing the activation cascade of the human ventral visual pathway. See MIT press release, and a related article discussing the method.
Research Highlight: Brainstorm software
Brainstorm software is an open-source environment dedicated to the analysis of brain recordings (MEG, EEG, NIRS, ECoG, depth electrodes, animal electrophysiology) with 17,000+ registered users and 2000+ related publications (as of 2022). Dimitrios Pantazis is a key collaborator in the development team, with major contributions in time-frequency analysis tools, labeling of cortical surfaces, statistical analysis of cortical activation maps, multivariate pattern analysis, and machine learning.
Research Highlight: Daredevil-like ability allows us to size up rooms
Our MEG eNeuro article investigating auditory space perception has caught the attention of Science News, UK Daily Mail, and APS (Association for Psychological Science)! The study provides the first neuromagnetic evidence for a robust auditory space size representation in the human brain.
Research Highlight: How small can MEG see?
In a series of empirical experiments in our NeuroImage article, we showed that neural signals at the level of cortical orientation columns (~800 μm) are accessible by MEG measurements in humans! Our work has been highlighted in a spotlight article in Trends in Cognitive Sciences, which even describes as a 'game changer' the notion that our research suggests MEG contains rich spatial information for decoding neural states.
Research Highlight: An algorithmic view of human object recognition
Artificial deep neural networks (DNNs) are so powerful computer vision models they now yield human performance levels on object categorization. By comparing a DNN tuned to the statistics of real world visual recognition with temporal (MEG) and spatial (fMRI) visual brain representations, we showed that the DNN captured the stages of human visual processing from early visual areas towards the dorsal and ventral streams. Our results published in Scientific Reports provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.
Research Highlight: Highly cited paper
According to the Web of Science, our Brainstorm article received enough citations to place it in the top 1% of the academic field of Neuroscience & Behavior based on a highly cited threshold for the field and publication year.