Dopamine replacement treatment and dopamine-agonists have been associated with impulse-control disorder and impulsive-compulsive behavior able to affect social decision-making. Frontal-executive dysfunction determines an alteration of social functioning through a mechanism of subversion of online action-monitoring, which associates disinhibition with volition. Genetic polymorphisms, alterations of the nigro-striatal substance, and impairment in the medial prefrontal cortex and in the Default mode network (DMN) seem to be able to explain these mechanisms. This theoretical perspective article aims to present these topics in order to encourage an interdisciplinary discussion capable of generating new research and developing rehabilitative intervention to improve social decision-making in PD patients.Neurodegenerative diseases are neuronal disorders characterized by the loss of a large number of neurons in the human brain. Innate immunity-mediated neuroinflammation actively contributes to the onset and progression of neurodegenerative diseases. Inflammasomes are involved in the progression of the innate immune response and are responsible for the maturation of caspase-1 and inflammatory cytokines during neuroinflammation. The nucleotide-binding oligomerization domain leucine-rich repeat and pyrin domain-containing protein 3 (NLRP3) inflammasome, which is one of the most intensively investigated inflammasomes, has been reported to play a key role in neurodegenerative diseases. Here, we reviewed the mechanisms, role, and latest developments regarding the NLRP3 inflammasome with respect to three neurodegenerative diseases Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). Patient and animal model studies have found that abnormal protein aggregation of Aβ, synuclein, or copper-zinc superoxide dismutase-1 (SOD1), which are the main proteins expressed in the three diseases, respectively, can activate microglial cells, induce increased interleukin-1β (IL-1β) release, and activate the NLRP3 pathway, leading to neurodegeneration. In contrast, a deficiency of the components of the NLRP3 pathway may inhibit Aβ, synuclein, or SOD1-induced microglial activation. These studies indicate a positive correlation between NLRP3 levels and abnormal protein aggregation. However, in the case of ALS, not only microglia but also astrocytes express increased NLRP3 levels and contribute to activation of the NLRP3 pathway. In addition, in this review article, we also focus on the therapeutic implications of targeting novel inhibitors of the NLRP3 inflammasome or of novel drugs that mediate the NLRP3 pathway, which could play a role via NLRP3 in the treatment of neurodegenerative diseases.Although it has been demonstrated that edge-based information is more important than surface-based information in incidental category learning, it remains unclear how the two types of information play different roles in incidental category learning. To address this issue, the present study combined behavioral and event-related potential (ERP) techniques in an incidental category learning task in which the categories were defined by either edge- or surface-based features. The results from Experiment 1 showed that participants could simultaneously learn both edge- and surface-based information in incidental category learning, and importantly, there was a larger learning effect for the edge-based category than for the surface-based category. The behavioral results from Experiment 2 replicated those from Experiment 1, and the ERP results further revealed that the stimuli from the edge-based category elicited larger anterior and posterior P2 components than those from the surface-based category, whereas the stimuli from the surface-based category elicited larger anterior N1 and P3 components than those from the edge-based category. Taken together, the results suggest that, although surface-based information might attract more attention during feature detection, edge-based information plays more important roles in evaluating the relevance of information in making a decision in categorization.
Parkinson disease (PD) patients have difficulty with self-initiated (SI) movements, presumably related to basal ganglia thalamocortical (BGTC) circuit dysfunction, while showing less impairment with externally cued (EC) movements.

We investigate the role of BGTC in movement initiation and the neural underpinning of impaired SI compared to EC movements in PD using multifocal intracranial recordings and correlating signals with symptom severity.

We compared time-resolved neural activities within and between globus pallidus internus (GPi) and motor cortex during between SI and EC movements recorded invasively in 13 PD patients undergoing deep brain stimulation implantation. We compared cortical (but not subcortical) dynamics with those recorded in 10 essential tremor (ET) patients, who do not have impairments in movement initiation.

SI movements in PD are associated with greater low-beta (13-20 Hz) power suppression during pre-movement period in GPi and motor cortex compared to EC movements in PD and comout PD.Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions - such as their multifractality or information content -, that otherwise remain hidden from conventional static methods. https://www.selleckchem.com/products/pf-04965842.html Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age- and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5-4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases.