Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model

Basal ganglia deep brain stimulation restores cognitive flexibility and exploration-exploitation balance disrupted by NMDA-R antagonism

Learning thrives on cognitive flexibility and exploration. Subjects with schizophrenia have impaired cognitive flexibility and maladaptive exploration patterns. The basal ganglia-dorsolateral prefrontal cortex (BG-DLPFC) network plays a significant role in learning processes. However, how this network maintains cognitive flexibility and exploration patterns and what alters these patterns in schizophrenia remains elusive. Using a combination of extracellular recordings, pharmacological manipulations, macro-stimulation techniques, and mathematical modeling, we show that in the nonhuman primate (NHP), the external segment of the globus pallidus (GPe, the central nucleus of the BG network) modulates cognitive flexibility and exploration patterns (experiments were done in females only). We found that chronic, low-dose administration of N-methyl-D-aspartate receptor (NMDA-R) antagonist, phencyclidine (PCP), decreases directed exploration but increases random exploration, as seen in schizophrenia. In line with adaptive working-memory reinforcement-learning models of the BG-DLPFC network, low-frequency GPe macro-stimulation restores the balance of both exploration types. Our findings suggest that exploration-exploitation imbalance reflects abnormal BG-DLPFC activity and that GPe stimulation may restore it.

Pallidal Deep Brain Stimulation Enhances Habitual Behavior in a Neuro-Computational Basal Ganglia Model During a Reward Reversal Learning Task

Deep brain stimulation (DBS) within the basal ganglia is a widely used therapeutic intervention for neurological disorders; however, its precise mechanisms of action remain unclear. This study investigates how DBS may affect decision-making processes through computational modeling of the basal ganglia. A rate-coded model incorporating direct, indirect, and hyperdirect pathways was utilized alongside a cortico-thalamic shortcut known for promoting habitual behavior. Simulations of a two-choice reward reversal learning task were conducted to replicate data from patients with dystonia in ON and OFF DBS conditions. We demonstrate that plasticity in the cortico-thalamic shortcut, which bypasses the basal ganglia, is crucial for reproducing the patients' behavioral data, emphasizing the role of habit formation. Simulated DBS increased habitual behavior following reward reversal. Integrating different DBS mechanisms revealed that suppression of stimulated neurons, stimulation of efferent axons, and a combined variant promoted habitual behavior. Analyses of thalamic inputs showed that, despite differing effects on the model's activity and plasticity, these DBS variants consistently reduced the influence of the basal ganglia while enhancing the role of the cortico-thalamic shortcut. Notably, the DBS variants were distinguishable by their divergent behavioral effects following discontinued stimulation. These findings underscore the potential multifaceted effects of DBS on decision-making processes. In particular, our model proposes that DBS modulates the balance between reward-guided and habitual behavior.

Quantitative Systems Pharmacology Development and Application in Neuroscience

Successful clinical development of therapeutics in neurology and psychiatry is challenging due to the complexity of the brain, the lack of validated surrogate markers and the nature of clinical assessments. On the other hand, tremendous advances have been made in unraveling the neurophysiology of the human brain thanks to technical developments in noninvasive biomarkers in both healthy and pathological conditions.Quantitative systems pharmacology (QSP) aims to integrate this increasing knowledge into a mechanistic model of key biological processes that drive clinical phenotypes with the objective to support research and development of successful therapies. This chapter describes both modeling of molecular pathways resulting in measurable biomarker changes, similar to modeling in other indications, as well as extrapolating in a mechanistic way these biomarker outcomes to predict changes in relevant functional clinical scales.Simulating the effect of therapeutic interventions on clinical scales uses the modeling methodology of computational neurosciences, which is based on the premise that human behavior is driven by firing activity of specific neuronal networks. While driven by pathology, the clinical behavior can also be influenced by various medications and common genotype variants. To address this occurrence, computational neuropharmacology QSP models can be developed and, in principle, applied as virtual twins, which are in silico clones of real patients.Overall, central nervous system (CNS) QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice. Overall, CNS QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice.