• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Of all publications in the section: 2
Sort:
by name
by year
Article
Feurra M., Bianco G., Polizzotto N. et al. Frontiers in Neural Circuits. 2011. Vol. 5. P. 10.

Previous transcranial magnetic stimulation (TMS) studies showed functional connections between the parietal cortex (PC) and the primary motor cortex (M1) during tasks of different reaching-to-grasp movements. Here, we tested whether the same network is involved in cognitive processes such as imagined or observed actions. Single pulse TMS of the right and left M1 during rest and during a motor imagery and an action observation task (i.e., an index-thumb pinch grip in both cases) was used to measure corticospinal excitability changes before and after conditioning of the right PC by 10 min of cathodal, anodal, or sham transcranial direct current stimulation (tDCS). Corticospinal excitability was indexed by the size of motor-evoked potentials (MEPs) from the contralateral first dorsal interosseous (FDI; target) and abductor digiti minimi muscle (control) muscles. Results showed selective ipsilateral effects on the M1 excitability, exclusively for motor imagery processes: anodal tDCS enhanced the MEPs' size from the FDI muscle, whereas cathodal tDCS decreased it. Only cathodal tDCS impacted corticospinal facilitation induced by action observation. Sham stimulation was always uneffective. These results suggest that motor imagery, differently from action observation, is sustained by a strictly ipsilateral parieto-motor cortex circuits. Results might have implication for neuromodulatory rehabilitative purposes.

Added: Sep 13, 2015
Article
Deperrois N., Moiseeva V., Gutkin B. Frontiers in Neural Circuits. 2019. Vol. 12. No. 116. P. 1-17.

Dopamine (DA) neurons in the ventral tegmental area (VTA) are thought to encode reward prediction errors (RPE) by comparing actual and expected rewards. In recent years, much work has been done to identify how the brain uses and computes this signal. While several lines of evidence suggest the interplay of the DA and the inhibitory interneurons in the VTA implements the RPE computation, it still remains unclear how the DA neurons learn key quantities, for example the amplitude and the timing of primary rewards during conditioning tasks. Furthermore, endogenous acetylcholine and exogenous nicotine, also likely affect these computations by acting on both VTA DA and GABA (γ -aminobutyric acid) neurons via nicotinic-acetylcholine receptors (nAChRs). To explore the potential circuit-level mechanisms for RPE computations during classical-conditioning tasks, we developed a minimal computational model of the VTA circuitry. The model was designed to account for several reward-related properties of VTA afferents and recent findings on VTA GABA neuron dynamics during conditioning. With our minimal model, we showed that the RPE can be learned by a two-speed process computing reward timing and magnitude. By including models of nAChR-mediated currents in the VTA DA-GABA circuit, we showed that nicotine should reduce the acetylcholine action on the VTA GABA neurons by receptor desensitization and potentially boost DA responses to reward-related signals in a non-trivial manner. Together, our results delineate the mechanisms by which RPE are computed in the brain, and suggest a hypothesis on nicotine-mediated effects on reward-related perception and decision-making.

Added: Oct 23, 2019