Biology subjects

Munuera, J.

Publications and source records attributed to Munuera, J..

3 recordsLinked to original sources

bioRxiv2023

Deep brain stimulation and dopamine medication enhance free choice preference in Parkinson's disease

BackgroundHumans prefer making choices freely, even when they dont maximize future outcomes, suggesting free-choice is intrinsically rewarding. However, whether reward-related brain networks influence choice preference remains unclear. In Parkinsons disease (PD), value-based decision impairments are well-documented, but mechanisms underlying intrinsically motivated behavior are poorly understood. This study investigates how the dopaminergic and basal ganglia systems encode intrinsic reward in PD. MethodsWe designed a decision-making task dissociating free-choices intrinsic value from extrinsic reward. Twenty PD patients with subthalamic deep brain stimulation (STN-DBS) and twentyfive on dopamine (DA) therapy performed the task ON and OFF their treatments. Their performances were compared to twenty age-matched healthy controls. To explore neural mechanisms, we analyzed DBS active contacts, modeled the volume of tissue activated, and examined cortico-subthalamic connectivity using high-resolution diffusion MRI. ResultsPD patients OFF STN-DBS exhibited reduced free-choice preference, which increased when STN-DBS was ON, particularly in risky choices. This effect correlated with the recruitment of the right medial prefrontal cortex (mPFC). DA therapy did not modulate free-choice preference acutely, but higher chronic DA levels correlated with increased free-choice preference. ConclusionsOur findings suggest STN-DBS enhances free-choice preference via the right mPFC-STN network, while chronic DA therapy amplifies free-choice sensitivity. This implies that freechoice preference is influenced by mPFC modulation, increasing impulsivity toward risky choices, and dopamines role in enhancing sensitivity to both extrinsic and intrinsic rewards.

neuroscience↗

bioRxiv2022

Choice seeking is motivated by the intrinsic need for personal control

When deciding between options that do or do not lead to future choices, humans often choose to choose. We studied choice seeking by asking subjects to decide between a choice opportunity or performing a computer-selected action. Subjects preferred choice when these options were equally rewarded, even deterministically, and were willing to trade extrinsic rewards for the opportunity to choose. We explained individual variability in choice seeking using reinforcement learning models incorporating risk sensitivity and overvaluation of rewards obtained through choice. Degrading perceived controllability diminished choice preference, although willingness to repeat selection of choice opportunities remained unchanged. In choices following these repeats, subjects were sensitive to rewards following freely chosen actions, but ignored environmental information in a manner consistent with a desire to maintain personal control. Choice seeking appears to reflect the intrinsic need for personal control, which competes with extrinsic reward properties and external information to motivate behavior. Author summaryHuman decisions can often be explained by the balancing of potential rewards and punishments. However, some research suggests that humans also prefer opportunities to choose, even when these have no impact on future rewards or punishments. Thus, opportunities to choose may be intrinsically motivating, although this has never been experimentally tested against alternative explanations such as cognitive dissonance or exploration. We conducted behavioral experiments and used computational modelling to provide compelling evidence that choice opportunities are indeed intrinsically rewarding. Moreover, we found that human choice preference varied according to individual risk attitudes, and expressed a need for personal control that competes with maximizing reward intake.

neuroscience↗

bioRxiv2018

The geometry of abstraction in hippocampus and prefrontal cortex

The curse of dimensionality plagues models of reinforcement learning and decision-making. The process of abstraction solves this by constructing abstract variables describing features shared by different specific instances, reducing dimensionality and enabling generalization in novel situations. Here we characterized neural representations in monkeys performing a task where a hidden variable described the temporal statistics of stimulus-response-outcome mappings. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training. This type of generalization requires a particular geometric format of neural representations. Neural ensembles in dorsolateral pre-frontal cortex, anterior cingulate cortex and hippocampus, and in simulated neural networks, simultaneously represented multiple hidden and explicit variables in a format reflecting abstraction. Task events engaging cognitive operations modulated this format. These findings elucidate how the brain and artificial systems represent abstract variables, variables critical for generalization that in turn confers cognitive flexibility.

neuroscience↗