Macaque parvocellular mediodorsal thalamus: dissociable contributions to learning and adaptive decision‐making

Autor: Zakaria Ouhaz, Stuart Mason, Subhojit Chakraborty, Anna S. Mitchell
Rok vydání: 2018
Předmět:
Zdroj: The European Journal of Neuroscience
ISSN: 1460-9568
0953-816X
Popis: Distributed brain networks govern adaptive decision‐making, new learning and rapid updating of information. However, the functional contribution of the rhesus macaque monkey parvocellular nucleus of the mediodorsal thalamus (MDpc) in these key higher cognitive processes remains unknown. This study investigated the impact of MDpc damage in cognition. Preoperatively, animals were trained on an object‐in‐place scene discrimination task that assesses rapid learning of novel information within each session. Bilateral neurotoxic (NMDA and ibotenic acid) MDpc lesions did not impair new learning unless the monkey had also sustained damage to the magnocellular division of the MD (MDmc). Contralateral unilateral MDpc and MDmc damage also impaired new learning, while selective unilateral MDmc damage produced new learning deficits that eventually resolved with repeated testing. In contrast, during food reward (satiety) devaluation, monkeys with either bilateral MDpc damage or combined MDpc and MDmc damage showed attenuated food reward preferences compared to unoperated control monkeys; the selective unilateral MDmc damage left performance intact. Our preliminary results demonstrate selective dissociable roles for the two adjacent nuclei of the primate MD, namely, MDpc, as part of a frontal cortical network, and the MDmc, as part of a frontal‐temporal cortical network, in learning, memory and the cognitive control of behavioural choices after changes in reward value. Moreover, the functional cognitive deficits produced after differing MD damage show that the different subdivisions of the MD thalamus support distributed neural networks to rapidly and fluidly incorporate task‐relevant information, in order to optimise the animals’ ability to receive rewards.
Databáze: OpenAIRE