(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.

There are two basic stages of fine motor learning: performance gain might occur during practice (online learning), and improvement might take place without any further practice (offline learning). Offline learning, also called consolidation, has a sleep-dependent stage in terms of both speed and accuracy of the learned movement. Sleep spindle or sigma band characteristics affect motor learning in typically developing individuals. Here we ask whether the earlier found, altered sigma activity in a neurodevelopmental disorder (Williams syndrome, WS) predicts motor learning. TD and WS participants practiced in a sequential finger tapping (FT) task for two days. Although WS participants started out at a lower performance level, TD and WS participants had a comparable amount of online and offline learning in terms of the accuracy of movement. Spectral analysis of WS sleep EEG recordings revealed that motor accuracy improvement is intricately related to WS-specific NREM sleep EEG features in the 8–16 Hz range profiles: higher 11–13.5 Hz z-transformed power is associated with higher offline FT accuracy improvement; and higher oscillatory peak frequencies are associated with lower offline accuracy improvements. These findings indicate a fundamental relationship between sleep spindle (or sigma band) activity and motor learning in WS.

According to recent studies anxiety has a significant impact on cognitive functioning, especially on decision-making. Alcohol dependent patients (ADP) achieve worse performance on decision-making simulation tasks compared to healthy controls. Our aim was to investigate how trait anxiety is connected to decision-making mechanisms in ADP. Methods: The data of 76 ADP have been analyzed. To examine decision-making mechanisms we used the "ABCD" version of the Iowa Gambling Task (IGT). The IGT total score was calculated and we divided the task into 5 equal blocks to study the pattern of the decision-making process. We administered the Spielberger Trait Anxiety Inventory (STAI) and the Wechsler Adult Intelligence Scale. The patient group was arranged into two subgroups with median split method based on the STAI scores. One group (N=38) was characterized by low trait anxiety level, and the other (N=38) had high level of trait anxiety. For comparing the two groups' decision-making mechanisms we used independent samples t-test. Results: The group with higher level of trait anxiety performed significantly poorer on the IGT (t=2.09, p=0.04). The detailed analysis of the two groups' decision-making mechanisms showed that the difference between the groups became significant in the 5th block (t=2.57, p=0.01). Conclusions: Decision-making deficit is not homogenous in the ADP group, as according to our results the trait anxiety level influences the adequacy of decision-making. Psycho-biological background of the inadequate decision-making needs further investigation and this knowledge could be used in the future to improve decision-making mechanisms of the ADP.

The COVID-19 pandemic had a major impact on higher education. Students were required to adopt a more independent way of learning, and instructors had to redesign courses to fit the digital space. Increasingly frequent e-learning research provides substantial support for the expansion of online education. The aim of this article is to investigate the effectiveness of e-learning materials among university students using a variety of research methodologies (Groningen Sleep Quality Scale, psychomotor vigilance task, verbal fluency and digit span tests, NASA Task Load indeX and eye tracking). In a pilot study conducted in a laboratory environment, 15 participants were divided into three groups and assigned to study from prepared course pages using content-equivalent e-learning materials. The results demonstrated that the applied research methodologies were appropriate for investigating the issue, allowing the pilot study to reveal a set of criteria encompassing the preferences of students for course structures and e-learning materials.