State-aware detection of sensory stimuli in the cortex of the awake mouse.

• Type of Material: Article
• Authors: Audrey J. Sederberg, Emory University; Aurelle Pala, Emory University; He J. V. Zheng, Emory University; Biyu J. He, New York University; Garrett Stanley, Emory University
• Language: English
• Date: 2019-05
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2019 Sederberg et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pcbi.1006716
• Title of Journal or Parent Work: PLoS Computational Biology
• ISSN: 1553-734X
• Volume: 15
• Issue: 5
• Start Page: e1006716
• End Page: e1006716
• Grant/Funding Information: This work was supported by the National Institutes of Health (NIH R01NS48285, U01NS094302 to GBS); Swiss National Science Foundation Early and Advanced Postdoc Mobility fellowship (AP); NSF CAREER Award (BCS-1753218 to BJH); Klingenstein-Simons Neuroscience Fellowship to BJH; and a Burroughs-Wellcome Collaborative Research Travel Grant to AJS.
• Research Categories: Biology, Neuroscience; Engineering, Biomedical

Abstract

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states.