Habenula-ventral tegmental area functional coupling and risk aversion in humans.

Area of Science:

• Neuroscience
• Computational Psychiatry
• Decision Science

Background:

• Maladaptive responses to uncertainty, such as excessive risk-seeking or avoidance, are associated with various mental disorders.
• Provariance bias (PVB) is a specific manifestation of risk-seeking, characterized by a preference for options with higher variance or uncertainty.

Purpose of the Study:

• To investigate the computational and neural mechanisms underlying individual differences in provariance bias (PVB).
• To determine if asymmetric learning rates, differentiating between positive and negative prediction errors, explain individual variations in PVB.

Main Methods:

• Employed a magnitude learning task to assess individual differences in PVB.
• Utilized computational modeling to analyze learning rates from positive prediction errors (PPEs) and negative prediction errors (NPEs).
• Conducted high-resolution 7T functional magnetic resonance imaging (fMRI) to examine neural responses in value-sensitive regions.

Main Results:

• Individual differences in PVB were explained by a model incorporating asymmetric learning rates for PPEs and NPEs.
• Distinct neural responses to PPEs and NPEs were identified in the habenula (Hb), ventral tegmental area (VTA), nucleus accumbens (NAcc), and ventral medial prefrontal cortex (vmPFC).
• Prediction error signals in the NAcc and vmPFC were amplified for high-variance options, and NPE responses in the NAcc were linked to learning rate bias and Hb-VTA functional coupling.

Conclusions:

• Asymmetric learning rates, driven by differential processing of prediction errors, underlie individual differences in risk preferences.
• Functional coupling between the Hb and VTA during NPE encoding plays a crucial role in modulating learning rate bias and subsequent risk preferences.
• These findings provide insights into the neural basis of risk preferences and have implications for understanding psychopathology related to uncertainty processing.