Alzheimer's disease (AD) is pathologically defined by extracellular accumulation of amyloid-β (Aβ) peptides generated by the cleavage of amyloid precursor protein (APP), strings of hyperphosphorylated Tau proteins accumulating inside neurons known as neurofibrillary tangles (NFTs) and neuronal loss. The association between the two hallmarks and cognitive decline has been known since the beginning of the 20th century when the first description of the disease was carried out by Alois Alzheimer. Today, more than 40 million people worldwide are affected by AD that represents the most common cause of dementia and there is still no effective treatment available to cure the disease. In general, the aggregation of Aβ is considered an essential trigger in AD pathogenesis that gives rise to NFTs, neuronal dysfunction and dementia. During the process leading to AD, tau and Aβ first misfold and form aggregates in one brain region, from where they spread to interconnected areas of the brain thereby inducing its gradual morphological and functional deterioration. In this mini-review article, we present an overview of the current literature on the spreading mechanisms of Aβ and tau pathology in AD since a more profound understanding is necessary to design therapeutic approaches aimed at preventing or halting disease progression.In this article, we review recent advances in research on rhythm and musical beat perception, focusing on the role of predictive processes in auditory motor interactions. We suggest that experimental evidence of the motor system's role in beat perception, including in passive listening, may be explained by the generation and maintenance of internal predictive models, concordant with the Active Inference framework of sensory processing. We highlight two complementary hypotheses for the neural underpinnings of rhythm perception The Action Simulation for Auditory Prediction hypothesis (Patel and Iversen, 2014) and the Gradual Audiomotor Evolution hypothesis (Merchant and Honing, 2014) and review recent experimental progress supporting each of these hypotheses. While initial formulations of ASAP and GAE explain different aspects of beat-based timing-the involvement of motor structures in the absence of movement, and physical entrainment to an auditory beat respectively-we suggest that work under both hypotheses provide converging evidence toward understanding the predictive role of the motor system in the perception of rhythm, and the specific neural mechanisms involved. We discuss future experimental work necessary to further evaluate the causal neural mechanisms underlying beat and rhythm perception.Social interaction is critical to emotional well-being. Previous studies have suggested sex differences in the perception of social interaction. However, the findings depend on the nature of interactions and whether it involves facial emotions. Here, we explored sex differences in neural responses to the perception of social interaction using the Human Connectome Project data. Participants (n = 969, 505 women) were engaged in a social cognition task with geometric objects moving and colliding to simulate social interaction. Behaviorally, men relative to women demonstrated higher accuracy in perceiving social vs. random interactions. Men vs. women showed higher activation in the right superior temporal gyrus, bilateral occipital and posterior cingulate cortex and precuneus, and women vs. learn more men showed higher activation in the right inferior frontal cortex, during exposure to social vs. random interactions. In whole-brain regressions, the differences in accuracy rate in identifying social vs. random interactions (AR SOC - AR RAN ) were associated with higher activation in the paracentral lobule (PCL) and lower activation in bilateral anterior insula (AI), pre-supplementary motor area (preSMA), and left middle frontal gyrus (MFG) in men and women combined, lower activation in bilateral AI, preSMA and left MFG in men alone, and higher activation in the PCL and the medial orbitofrontal cortex in women alone. The latter sex differences were confirmed by slope tests. Further, the PCL activity mediated the correlation between an internalizing syndromal score, as assessed by the Achenbach Self-Report, and (AR SOC - AR RAN ) across all subjects. These findings highlighted sex differences in the behavioral and neural processes underlying the perception of social interaction, as well as the influence of internalizing traits on these processes.Recent statistics have shown that the main difficulty in detecting alcoholism is the unreliability of the information presented by patients with alcoholism; this factor confusing the early diagnosis and it can reduce the effectiveness of treatment. However, electroencephalogram (EEG) exams can provide more reliable data for analysis of this behavior. This paper proposes a new approach for the automatic diagnosis of patients with alcoholism and introduces an analysis of the EEG signals from a two-dimensional perspective according to changes in the neural activity, highlighting the influence of high and low-frequency signals. This approach uses a two-dimensional feature extraction method, as well as the application of recent Computer Vision (CV) techniques, such as Transfer Learning with Convolutional Neural Networks (CNN). The methodology to evaluate our proposal used 21 combinations of the traditional classification methods and 84 combinations of recent CNN architectures used as feature extractors combined with the following classical classifiers Gaussian Naive Bayes, K-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM). CNN MobileNet combined with SVM achieved the best results in Accuracy (95.33%), Precision (95.68%), F1-Score (95.24%), and Recall (95.00%). This combination outperformed the traditional methods by up to 8%. Thus, this approach is applicable as a classification stage for computer-aided diagnoses, useful for the triage of patients, and clinical support for the early diagnosis of this disease.A deep brain stimulation system capable of closed-loop neuromodulation is a type of bidirectional deep brain-computer interface (dBCI), in which neural signals are recorded, decoded, and then used as the input commands for neuromodulation at the same site in the brain. The challenge in assuring successful implementation of bidirectional dBCIs in Parkinson's disease (PD) is to discover and decode stable, robust and reliable neural inputs that can be tracked during stimulation, and to optimize neurostimulation patterns and parameters (control policies) for motor behaviors at the brain interface, which are customized to the individual. In this perspective, we will outline the work done in our lab regarding the evolution of the discovery of neural and behavioral control variables relevant to PD, the development of a novel personalized dual-threshold control policy relevant to the individual's therapeutic window and the application of these to investigations of closed-loop STN DBS driven by neural or kinematic inputs, using the first generation of bidirectional dBCIs.