Mauro Ursino

Purpose: Reinforcement learning with reversal is a paradigm frequently employed in neuropsychology to assess the adaptive capacity and flexibility in individuals in non-stationary environments. Each single behavior, however, depends on the superimposition of multiple factors, which are still not completely understood. Methods: We optimize a neurocomputational model of the Basal Ganglia to simulate the behavior of 18 patients with Parkinson’s disease (both ON and OFF medication) and 14 control subjects during a two-choice probabilistic reversal learning task (with 80–20% reward probabilities). The individual behavior (in terms of the cumulative number of correct responses during a 40-trials direct phase and a 40-trial reversal phase) is reproduced by fitting a few model parameters for each individual, representing the tonic dopamine level, Hebbian learning, and the exploratory attitude (noise level). Results: Results show that very different responses can be explained quite well, ascribing them to the varying combination of the aforementioned individual factors. The tonic dopamine level is significantly different for patients in ON and OFF medication, while the other parameters were not statistically different. A regression analysis reveals that the value of the Hebbian learning rate is correlated with the subject's sensitivity to punishments. In contrast, the noise standard deviation is correlated with exploration, i.e., the tendency to modify the choice even after a reward. Conclusion: The results provide a mechanistic explanation of the various factors that affect adaptation and flexibility in reinforcement learning, representing a first step toward characterizing and understanding the diverse behaviors on an individual basis.

Estimation of brain connectivity from neuroelectric data is a fundamental problem in modern neuroscience, and it is used to assess the network properties of brain function. In the present work, we critically assess the virtues and limitations of temporal Granger causality (using both conditional and unconditional formulations) for the estimation of functional brain connectivity, using a neural mass model as the ground truth. The model simulates transmission among different brain rhythms (in the θ, α, β, and γ bands) via excitatory and inhibitory synapses. The results show that Granger causality is able to detect relative changes in connectivity, but the estimated values are influenced by the operative conditions (sampling frequency, signal length, delay). Moreover, the absolute value of Granger causality depends on the particular rhythm transmitted and is affected by nonlinear phenomena, especially the activity level in the connected regions. In the case of complex connectivity networks, conditional Granger causality overwhelms the unconditional one, since the latter often discovers spurious connections. Finally, inhibitory connections can be revealed more easily by Granger causality than similar excitatory connections, a result generally neglected in brain network studies. The present results can drive the correct interpretation of Granger-causality-based connectivity networks derived from neuroelectric signals.

I enjoyed reading this manuscript because it deals with a fascinating problem (i.e., the role and meaning of dreaming) and contains much information on different aspects (theoretical and experimental). The author exhibits a wide knowledge of differen...

Astrocytes are responsible for maintaining homoeostasis and cognitive functions through calcium signalling, a process that is altered in brain diseases. Current bioelectronic tools are designed to study neurons and are not suitable for controlling calcium signals in astrocytes. Here, we show that electrical stimulation of astrocytes using electrodes coated with graphene oxide and reduced graphene oxide induces respectively a slow response to calcium, mediated by external calcium influx, and a sharp one, exclusively due to calcium release from intracellular stores. Our results suggest that the different conductivities of the substrate influence the electric field at the cell–electrolyte or cell–material interfaces, favouring different signalling events in vitro and ex vivo. Patch-clamp, voltage-sensitive dye and calcium imaging data support the proposed model. In summary, we provide evidence of a simple tool to selectively control distinct calcium signals in brain astrocytes for straightforward investigations in neuroscience and bioelectronic medicine.