Neural circuits for decision making

NIH RePORTER · NIH · R56 · $390,000 · view on reporter.nih.gov ↗

Abstract

PROJECT SUMMARY Different people facing similar situations make different choices, and different choices result in divergent life trajectories. Despite the pervasive impact on an individual’s life, the neural basis of idiosyncratic decision-making remains largely unknown. Beginning to address this important topic, we recently characterized idiosyncratic choice behavior quantitatively and identified a posterior network comprised of the cingulate cortex, posterior parietal cortex, and striatum in which the idiosyncratic choice biases are likely processed, transmitted, and integrated with other decision variables. The posterior network imbuing idiosyncrasy might operate in parallel to the well-known frontal network for rule- and value-based decision-making and the sensorimotor network for stimulus-response association, all of which dynamically contribute to the final choice. However, the detailed circuit mechanisms in the posterior network have yet to be discovered. We propose a series of experiments in mice to elaborate information flows and processing in the posterior network, utilizing well-established in-lab tools including behavior modeling, anterograde- and retrograde-transsynaptic labeling, optogenetics, and two-photon imaging. Understanding the neural circuits governing the moment-by-moment idiosyncratic history bias will shed light on the neural origins of idiosyncratic decisions. Furthermore, life-interfering, maladaptive choice behavior might be in part a manifestation of extremely deviated idiosyncratic bias, thus elaborating the less known posterior network may bring new insights into pathological decision-making in neurological conditions such as aging, dementia, and addiction.

Key facts

NIH application ID
10828455
Project number
5R56MH130488-02
Recipient
ROSALIND FRANKLIN UNIV OF MEDICINE & SCI
Principal Investigator
Eun Jung Hwang
Activity code
R56
Funding institute
NIH
Fiscal year
2024
Award amount
$390,000
Award type
5
Project period
2023-04-13 → 2026-03-31