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Paper Detail

Paper: PS-1B.57
Session: Poster Session 1B
Location: H Fläche 1.OG
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: Measuring prefrontal representational geometry: fMRI adaptation vs pattern analysis
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Apoorva Bhandari, Brown University, United States; Marcus Benna, Columbia University, United States; Mattia Rigotti, IBM Research AI, United States; Stefano Fusi, Columbia University, United States; David Badre, Brown University, United States
Abstract: The prefrontal cortex (PFC) is necessary for the expression of flexible behavior. In over-trained monkeys, lateral PFC neurons represent a variety of task-relevant information in a high-dimensional code. In humans, the relatively low reliability of fMRI BOLD activity patterns and the difficulty of decoding their information content poses an obstacle to measuring PFC representational geometry. We systematically evaluated multi-voxel pattern analysis (MVPA) and the alternate method of fMRI adaptation for their reliability in estimating representational geometry and dimensionality in lateral PFC. Subjects solved a 3-dimension, audio-visual, parity task over 5 fMRI sessions. Leveraging the large amount of within-participant data, we estimated all pair-wise pattern distances and cross-condition adaptation effects in lateral PFC and visual cortex. We show that fMRI adaptation provides significantly more reliable estimates of the distances between task conditions in the lateral PFC’s representational space compared to MVPA.