Hierarchical cross-scale analysis identifies parallel ventral striatal networks coding for dynamic and stabilized olfactory reward predictions

Abstract

The unbiased identification of brain circuits responsible for behavior and their local cellular computations is a challenge for neuroscience. We establish here a hierarchical cross-scale approach from behavioral modeling and fMRI in task-performing mice to cellular network dynamics to identify how reward predictions are represented in the forebrain upon olfactory conditioning. fMRI identified functional segregation in reward prediction and error computations among olfactory cortices and subcortical circuits. Among them, the olfactory tubercle contributed both to dynamic reward predictions and prediction error. In this region, cellular recordings revealed two parallel neuronal populations for prediction coding. One population produced stabilized predictions as distributed stimulus-bound transient network activity; the other evolved during anticipatory waiting and fully reflected predicted value in single-units, dynamically integrating the recent cue-specific history of uncertain outcomes. Thus, the cross-scale approach revealed regional functional differentiation among the distributed forebrain circuits with a limbic hotspot for multiple non-redundant reward prediction coding.

Future Directions for Chemosensory Connectomes: Best Practices and Specific Challenges

“…The potential effects of acoustic fMRI-noise on the functional connectome in chemosensory studies have not yet been investigated. Nevertheless, efforts have been made to acclimatize subjects to the acoustic noise associated with magnetic resonance scanners, with mock scanning sessions common in human imaging, particularly in children ( O’Rawe et al, 2019 ), as well as habituation training for scanning in awake rodents ( Winkelmeier et al, 2021 ).…”