2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-1A.69
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Synchronized and Propagating States of Human Auditory Processing
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1306-0
Authors: Joon-Young Moon, Kathrin Müsch, Johns Hopkins University, United States; Charles Schroeder, The Nathan S. Kline Institute for Psychiatric Research, United States; Christopher Honey, Johns Hopkins University, United States
Abstract: Human brain dynamics combine external drivers (e.g. sensory information) and internal drivers (e.g. expectations and memories). How do the patterns of inter-regional coupling change when the balance of external and internal information is altered? To investigate this question, we analyzed intracranial (ECoG) recordings from human listeners exposed to an auditory narrative. We measured the latencies of coupling across consecutive stages of cortical auditory processing and we investigated if and how the latencies varied as a function of the stimulus drive. We found that the latencies along the auditory pathway vary between no delay (“synchronized state”) and a small, nonzero delay (~20 ms, “propagating state”) depending on the external stimulation. The long-latency propagating state was most often observed in the absence of external information, during the silent boundaries between sentences. Moreover, propagating states were associated with transient increases in alpha-band (8-12 Hz) oscillatory processes. Both synchronized and propagating states were reproduced in a coupled oscillator model by altering the strength of external drive. The data and model suggest that cortical networks transition between i) synchronized dynamics driven by an external stimulus, and ii) long-latency propagating dynamics in the absence of an external stimulus.