Fluctuations of fMRI activation patterns reveal theta-band dynamics of visual object priming

2019.11.21
Event

A "predictive coding" hypothesis proposes that the brain dynamically anticipates and generates predictions about upcoming stimuli to guide perception efficiently. Here, we combined functional magnetic resonance imaging (fMRI), multi-voxel pattern decoding, and an innovative time-resolved psychophysical paradigm to assess the temporal profile and spatial distributions of this prediction process. Strikingly, we demonstrate rhythmic population activity in several task-related brain areas. Specifically, multi-voxel activity patterns in the fusiform face area (FFA) and the parahippocampal place area (PPA) show temporal fluctuations at a theta-band (~5 Hz) rhythm that accompany effects in visual object priming. These results provide novel and essential constraints to understand the neuronal dynamics of predictive coding. Moreover, these results suggest a feasible fMRI strategy to measure temporal fluctuations of multi-voxel activity patterns in the human brain, providing a critical link between fMRI measurements and neurophysiological recordings to understand fine-scale spatiotemporal dynamics of attention and consciousness.

Topic：Fluctuations of fMRI activation patterns reveal theta-band dynamics of visual object priming

Date：November 21th, 2019, Thursday

Time: 17:00-18:00

Venue: Room 101, Teaching A

Speaker: Prof. Ming Meng

About the Speaker: Ming Meng, Professor of Cognitive Neuroscience at the South China Normal University, received his PhD from Princeton University, was a postdoctoral fellow at MIT and a faculty member at Dartmouth College. As documented in several high-impact peer-reviewed publications, he has been capable of decoding what neural mechanisms underlie visual cognition and attention, with and without visual awareness. These neural mechanisms correlate with activity in broad visual processing network and attentional neural network across brain regions in the left and right occipital, temporal and parietal lobes, shedding lights for the understanding of normal behavioral patterns as well as neurological disorders. Professor Meng is an expert of behavioral experimentation, functional brain imaging, machine learning and computational modeling. He has been a Principle Investigator (PI) of grants by the National Science Foundation (NSF) of the US and the National Natural Science Foundation of China (NSFC), a recipient of the Young Investigator Award by the Brain and Behavior Research Foundation.