Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning

TY - JOUR

T1 - Striatal hub of dynamic and stabilized prediction coding in forebrain networks for olfactory reinforcement learning

AU - Winkelmeier, Laurens

AU - Filosa, Carla

AU - Hartig, Renée

AU - Scheller, Max

AU - Sack, Markus

AU - Reinwald, Jonathan R

AU - Becker, Robert

AU - Wolf, David

AU - Gerchen, Martin Fungisai

AU - Sartorius, Alexander

AU - Meyer-Lindenberg, Andreas

AU - Weber-Fahr, Wolfgang

AU - Clemm von Hohenberg, Christian

AU - Russo, Eleonora

AU - Kelsch, Wolfgang

N1 - © 2022. The Author(s).

PY - 2022/6/8

Y1 - 2022/6/8

N2 - Identifying the circuits responsible for cognition and understanding their embedded 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 recordings, in order to disentangle local network contributions to olfactory reinforcement learning. At mesoscale, fMRI identifies a functional olfactory-striatal network interacting dynamically with higher-order cortices. While primary olfactory cortices respectively contribute only some value components, the downstream olfactory tubercle of the ventral striatum expresses comprehensively reward prediction, its dynamic updating, and prediction error components. In the tubercle, recordings reveal two underlying neuronal populations with non-redundant reward prediction coding schemes. One population collectively produces stabilized predictions as distributed activity across neurons; in the other, neurons encode value individually and dynamically integrate the recent history of uncertain outcomes. These findings validate a cross-scale approach to mechanistic investigations of higher cognitive functions in rodents.

AB - Identifying the circuits responsible for cognition and understanding their embedded 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 recordings, in order to disentangle local network contributions to olfactory reinforcement learning. At mesoscale, fMRI identifies a functional olfactory-striatal network interacting dynamically with higher-order cortices. While primary olfactory cortices respectively contribute only some value components, the downstream olfactory tubercle of the ventral striatum expresses comprehensively reward prediction, its dynamic updating, and prediction error components. In the tubercle, recordings reveal two underlying neuronal populations with non-redundant reward prediction coding schemes. One population collectively produces stabilized predictions as distributed activity across neurons; in the other, neurons encode value individually and dynamically integrate the recent history of uncertain outcomes. These findings validate a cross-scale approach to mechanistic investigations of higher cognitive functions in rodents.

U2 - 10.1038/s41467-022-30978-1

DO - 10.1038/s41467-022-30978-1

M3 - SCORING: Journal article

C2 - 35676281

VL - 13

JO - NAT COMMUN

JF - NAT COMMUN

SN - 2041-1723

IS - 1

M1 - 3305

ER -