The most widely used imaging method, functional magnetic resonance imaging (fMRI) provides precise information about where in the brain activity occurs, but it cannot detect with the same degree of precision when these events occur in the brain. This kind of temporal precision can be accomplished with magnetoencephalography (MEG), a tool developed at MIT and found in the Martinos Imaging Center at MIT. Dimitrios Pantazis’ research helps to bridge the gap between spatial and temporal brain imaging data. Director of the MEG lab, Pantazis develops new methods for extracting neural representations from MEG data, and the development of multimodal imaging techniques that give more holistic information about brain function. Using such approaches, he gets insight into processes such as how the brain handles information in the ventral visual stream.
Pantazis is a key developer of Brainstorm, an open-source environment dedicated to the analysis of brain recordings (MEG, EEG, NIRS, ECoG, depth electrodes, animal electrophysiology) with 13,000+ registered users and 400+ related publications.
Dimitrios Pantazis joined MIT in 2010 and is currently the director of the MEG lab housed within Athinoula A. Martinos Imaging Center at MIT. Before moving to MIT, he was research assistant professor at the University of Southern California from 2008-2010. He received his PhD in Electrical Engineering at the University of Southern California in 2006.
Ultra-rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway. Mohsenzadeh, Y., Qin, S., Cichy, R.M., Pantazis, D. (2018)
eLife 7, pii: e36329
Tu, Y, Pantazis, D, Wilson, G, Khan, S, Ahlfors, S, Kong, J et al.. How expectations of pain elicited by consciously and unconsciously perceived cues unfold over time. Neuroimage. 2021;235 :117985. doi: 10.1016/j.neuroimage.2021.117985. PubMed PMID:33762214 .
Xu, M, Wang, Z, Zhang, H, Pantazis, D, Wang, H, Li, Q et al.. A new Graph Gaussian embedding method for analyzing the effects of cognitive training. PLoS Comput Biol. 2020;16 (9):e1008186. doi: 10.1371/journal.pcbi.1008186. PubMed PMID:32941425 PubMed Central PMC7524000.