Multi-areal neural dynamics encode human decision making
Overton, J. A.; Moxon, K. A.; Stickle, M. P.; Peters, L. M.; Lin, J. J.; Chang, E. F.; Knight, R. T.; Hsu, M.; Saez, I.
Show abstract
Value-based decision-making involves multiple cortical and subcortical brain areas, but the distributed nature of neurophysiological activity underlying economic choices in the human brain remains largely unexplored. Here, we use intracranial recordings from neurosurgical patients to show that risky choices are reflected in high frequency activity distributed across multiple prefrontal and subcortical brain regions, whereas reward-related computations are less widely represented and more modular. State space modeling reveals sub-second neural dynamics underlying choices during deliberation and allows high-accuracy trial-by-trial decoding of subjects choices robustly across patients despite differences in anatomical coverage. These results shed light into the neural basis of choice across brain areas and open the door to new intracranial approaches for brain state decoding.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making 97%
- Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation 97%
- Interplay between persistent activity and activity-silent dynamics in prefrontal cortex during working memory 97%
Similar papers in this journal
Similar papers in this journal
- Layer 6 ensembles can selectively regulate the behavioral impact and layer-specific representation of sensory deviants 97%
- Astrocytic modulation of population encoding in mouse visual cortex via GABA transporter 3 revealed by multiplexed CRISPR/Cas9 gene editing 96%
- Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.