Rett syndrome (RTT) is a severe neurodevelopmental disorder (NDD) that is nearly always caused by loss of function mutations in Methyl-CpG-binding Protein 2 (MECP2) and shares many clinical features with other NDD. Genetic restoration of Mecp2 in symptomatic mice lacking MeCP2 expression can reverse symptoms, providing hope that disease modifying therapies can be identified for RTT. Effective and rapid clinical trial completion relies on well-defined clinical outcome measures and robust biomarkers of treatment responses. Studies on other NDD have found evidence of differences in neurophysiological measures that correlate with disease severity. However, currently there are no well-validated biomarkers in RTT to predict disease prognosis or treatment responses. To address this, we characterized neurophysiological features in a mouse model of RTT containing a knock-in nonsense mutation (p.R255X) in the Mecp2 locus. We found a variety of changes in heterozygous female Mecp2 mice including age-related changes in sleep/wake architecture, alterations in baseline EEG power, increased incidence of spontaneous epileptiform discharges, and changes in auditory evoked potentials. Furthermore, we identified association of some neurophysiological features with disease severity. These findings provide a set of potential non-invasive and translatable biomarkers that can be utilized in preclinical therapy trials in animal models of RTT and eventually within the context of clinical trials.
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http://dx.doi.org/10.1016/j.nbd.2020.105083 | DOI Listing |
Eur J Neurol
February 2025
Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy.
Objective: Disorders of arousal (DoA) are characterized by an intermediate state between wakefulness and deep sleep, leading to incomplete awakenings from NREM sleep. Multimodal studies have shown subtle neurophysiologic alterations even during wakefulness in DoA. The aim of this study was to explore the brain functional connectivity in DoA and the metabolic profile of the anterior and posterior cingulate cortex, given its pivotal role in cognitive and emotional processing.
View Article and Find Full Text PDFBrain Res Bull
January 2025
Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, The Second Affiliated Hospital of Air Force Medical University, No.569 Xinsi Road, Xi'an, 710038, Shaanxi, China. Electronic address:
Introduction: Cognitive fatigue is mainly caused by enduring mental stress or monotonous work, impairing cognitive and physical performance. Natural scene exposure is a promising intervention for relieving cognitive fatigue, but the efficacy of virtual reality (VR) simulated natural scene exposure is unclear. We aimed to investigate the effect of VR natural scene on cognitive fatigue and further explored its underlying neurophysiological alterations with electroencephalogram (EEG) microstates analysis.
View Article and Find Full Text PDFNeuropsychologia
January 2025
University of Texas at Austin, 110 Inner Campus Drive, Austin, TX, 78712, USA.
Semantic memory, a repository for concepts and factual information, plays a vital role in acquiring and retrieving knowledge. This study explores the impact of age-related knowledge accumulation on semantic cognition, investigating whether a denser representational space affects retrieval processes. Using a semantic feature verification task, we employ both behavioral (reaction time; RT) and neurophysiological (event-related potential; ERP) measures to explore these dynamics across young and older adults.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, South Tyrol, Italy.
Appraisal models, such as the Scherer's Component Process Model (CPM), represent an elegant framework for the interpretation of emotion processes, advocating for computational models that capture emotion dynamics. Today's emotion recognition research, however, typically classifies discrete qualities or categorised dimensions, neglecting the dynamic nature of emotional processes and thus limiting interpretability based on appraisal theory. In our research, we estimate emotion intensity from multiple physiological features associated to the CPM's neurophysiological component using dynamical models with the aim of bringing insights into the relationship between physiological dynamics and perceived emotion intensity.
View Article and Find Full Text PDFFront Neurosci
January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
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