Beta oscillatory activity (13-30Hz) is pervasive within the cortico-basal ganglia (CBG) network. Studies in Parkinson’s disease (PD) patients and animal models suggested that beta-power increases with dopamine depletion. However, the exact relationship between oscillatory power, frequency and dopamine-tone remains unclear. We recorded neural activity in the CBG network of non-human-primates (NHP) while acutely up- and down-modulating dopamine levels. Further, we assessed changes in beta oscillations of PD patients following acute and chronic changes in dopamine-tone. Beta oscillation frequency was strongly coupled with dopamine-tone in both NHPs and human patients. In contrast, power, coherence between single-units and LFP, and spike-LFP phase-locking were not systematically regulated by dopamine levels. These results demonstrate via causal manipulations that frequency, rather than other properties, is the key property of pathological oscillations in the CBG networks. These insights can lead to improvements in understanding of CBG physiology, PD progression tracking and patient care.

Primates can quickly and advantageously adopt complex rule-based behaviors. We studied acquisition of rule-based classification while recording single neurons in the dorsal-anteriorcingulate-cortex (dACC) and the Striatum. Monkeys performed trial-by-trial classification on a rich set of multi-cue patterns, allowing de-novo rule-learning with varying conceptual complexities every few days. To examine neural dynamics during the learning itself, we represent each rule with a spanning set of the space formed by the stimulus features. Because neural preference can be expressed by feature combinations, we can track neural dynamics in geometrical terms in this space, allowing a compact universal description of neural trajectories by observing changes in either vector-magnitude and/or angle-to-rule. We find that a large fraction of cells in both regions follow the behavior during learning. Neurons in the dACC mainly rotate towards the rule, suggesting an increase in selectivity that approximates the rule; whereas in the Putamen we additionally find a prominent magnitude increase, suggesting strengthening of confidence. Moreover, magnitude increases in the striatum followed rotation in the dACC, and finally, the neural policy at the end of the session predicted next-day behavior. Using this novel framework enables tracking of neural dynamics during learning and suggests differential roles of confidence and policy for the different brain regions.

Emotional similarity refers to the tendency to group stimuli together because they evoke the same feelings in us, even when they look different and have different semantic meanings. It is still unclear which features define the similarity space of emotional categories. Additionally, whether emotional stimuli are perceived as more similar than neutral ones, and whether this difference is paralleled by differences in their neural representations, has never been investigated. We conducted a series of experiments to quantify behavioural similarity, and one that used fMRI and Representation similarity analysis to compute neural similarity. We hypothesised that the similarity between emotional stimuli will be greater than between non-emotional stimuli, paralleled by higher neural similarity among emotional stimuli. We tested these hypotheses with two measures of similarity and two different databases of complex negative and neutral pictures, the second of which allowed us to control semantic similarity. For the first time, we found a decoupling between subjective and objective measures of emotional similarity. Pictures taken from two distinct emotional and neutral categories were perceived as equally similar; however, their neural similarity was higher. This effect was detected in brain clusters localised in a constrained search volume. We conclude that features representing participants similarity space may have different weights in these regions than they do in explicit ratings.