It is assumed that cognitive processes are provided by the regulatory interactions of different brain networks. The three most stable resting state networks, among which the default mode network (DMN), the central executive network (CEN) and the salience network (SN) are considered to be the key neurocognitive networks for understanding higher cognitive functions. Peculiarities of changes in the connectivity of resting state networks of an individual entering a new environment and after a year of adaptation in this environment remain poorly studied. The aim of this study was to investigate the peculiarities of the connectivity of resting state networks calculated in EEG data in students right after moving to an unfamiliar environment and one year after moving. 128-channel EEGs were recorded in the resting state in 45 students (all men) aged from 18 to 29 years, who moved to the North region of Russia (Yakutsk, Republic of Sakha (Yakutia)). Resting state networks were calculated by the seed-based method. The subjects had increased SN connectivity with the sensorimotor cortex and the posterior node of DMN (posterior cingulate cortex and precuneus) in the condition when they were exposed to a new unfamiliar environment, compared to the condition after a year in the same environment. In general, the obtained data are consistent with the notion of increased SN functioning when encountering new significant stimuli and tasks, i.e. new environmental conditions, and the representation of SN as closely related to the function of homeostasis regulation according to organism's internal goals and environmental requirements.
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http://dx.doi.org/10.1016/j.neulet.2022.137012 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130.
Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions.
View Article and Find Full Text PDFInt J Clin Health Psychol
October 2024
Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China.
Ruminative reflection has been linked to enhanced executive control in processing internally represented emotional information, suggesting it may serve as an adaptive strategy for emotion regulation. Investigating the neural substrates of reflection can deepen our understanding of its adaptive properties. This study used network-based statistic (NBS)-Predict methodology to identify resting state functional connectivity (FC)-based predictors of ruminative reflection in a healthy sample.
View Article and Find Full Text PDFCureus
December 2024
Medical Education, ABWA Medical College, Faisalabad, PAK.
Background: The inclusion of artificial intelligence in medical education, specifically through the use of ChatGPT (OpenAI, San Francisco, CA), has transformed learning and generated many ethical questions. This study aims to analyze the medical students' ethical concerns about using ChatGPT in medical education, focusing on privacy, accuracy, and professional integrity.
Methods: The study format was a cross-sectional survey distributed to 219 medical students at ABWA Medical College, Pakistan.
Alzheimers Dement (Amst)
January 2025
Introduction: Cross-sectional resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed altered complexity with advanced Alzheimer's disease (AD) stages. The current study conducted longitudinal rsfMRI complexity analyses in AD.
Methods: Linear mixed-effects (LME) models were implemented to evaluate altered rates of disease progression in complexity across disease groups.
Cureus
January 2025
General Practice, Wad Medani Hospital, Wad Medani, SDN.
To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology.
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