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Paper: PS-2A.38
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Computational fMRI Reveals Separable Representations Of Stimulus and Behavioral Choice In Auditory Cortex: A Tool for Studying the Locus Coeruleus Circuit
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Kimia Yaghoubi, Mahsa Alizadeh Shalchy, Sana Hussain, Xu Chen, Ilana Benette, University of California, Riverside, United States; Mara Mather, University of Southern California, United States; Xiaoping Hu, Aaron Seitz, Megan Peters, University of California, Riverside, United States
Abstract: The locus coeruleus (LC) influences many cognitive functions (e.g., arousal, attention, and perception) due to its broad noradrenergic projections throughout the brain. However, the computational mechanisms of LC’s influence are complex and so constitute an area of active investigation. One promising approach would be to observe how LC engagement changes stimulus encoding in sensory cortex. As a preliminary step towards this goal, we combined a novel auditory oddball discrimination task with high-resolution fMRI (2mm3 isotropic voxels) and multivariate pattern analysis in humans. Even with modest trial counts (~24-70 trials per condition), sparse logistic regression classifiers could decode both auditory stimulus identity and behavioral choices above chance in auditory cortex, in single subjects and in each of six oddball stimulus levels. Further, stimulus decoders were highly specific to each oddball level, but choice decoders generalized across levels; there was also little overlap between stimulus and choice decoders. These findings suggest that our paradigm and computational analyses provide a promising approach for investigating LC influences on sensory neural representations in humans in the future.