Life Satisfaction Among Adolescents: Validation of the Adapted Multidimensional Students' Life Satisfaction Scale.

Life satisfaction is associated with adolescents' adaptability, academic achievement, and mental health, and it reflects the profile of a country's economic development. In this study, we assessed the psychometric properties of the Russian version of the Multidimensional Students' Life Satisfaction Scale (MSLSS). The initial adaptation of the MSLSS was performed using a sample of primary school students.

Task-specific topology of brain networks supporting working memory and inhibition.

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording.

Factor structure and psychometric properties of the Perceived Stress Scale in Russian adolescents.

This study involved a psychometric analysis of the 10-item Perceived Stress Scale (PSS-10). To investigate the Russian version of the PSS-10 for adolescents, 3530 adolescents aged 13-17 years were recruited. Confirmatory factor analysis revealed that the data corresponded to the expected two-factor configuration.

The Russian version of the Abbreviated Math Anxiety Scale: psychometric properties in adolescents aged 13-16 years.

This study is the first to assess the internal consistency and factor validity of the Abbreviated Math Anxiety Scale (AMAS) in a sample of Russian adolescents as well as gender differences and gender invariance. The study included 4,218 adolescents in grades 7-9 ( = 14.23, SD = 0.

Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model.

This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG study, affirming that gender prediction can be attained with noteworthy accuracy. The best performing model achieved an accuracy of 85% and an ROC AUC of 89%, surpassing all prior benchmarks set using EEG data and rivaling the top-tier results derived from fMRI studies.