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Neurons in the brain are seldom perfectly quiet. They continually receive input and generate output, resulting in highly variable patterns of ongoing activity. Yet the functional significance of this variability is not well understood. If brain signal variability is functionally relevant and serves as an important indicator of cognitive function, then it should be highly sensitive to the precise manner in which a cognitive system is engaged and/or relate strongly to differences in behavioral performance. To test this, we examined EEG activity in younger adults as they performed a cognitive skill learning task and during rest. Several measures of EEG variability and signal strength were calculated in overlapping time windows that spanned the trial interval. We performed a systematic examination of the factors that most strongly influenced the variability and strength of EEG activity. First, we examined the relative sensitivity of each measure to across-subject variation (within blocks) and across-block variation (within subjects). We found that the across-subject variation in EEG variability and signal strength was much stronger than the across-block variation. Second, we examined the sensitivity of each measure to different sources of across-block variation during skill acquisition. We found that key task-driven changes in EEG activity were best reflected in changes in the strength, rather than the variability, of EEG activity. Lastly, we examined across-subject variation in each measure and its relationship with behavior. We found that individual differences in response time measures were best reflected in individual differences in the variability, rather than the strength, of EEG activity. Importantly, we found that individual differences in EEG variability related strongly to stable indicators of subject identity rather than dynamic indicators of subject performance. We therefore suggest that EEG variability may provide a more sensitive subject-driven measure of individual differences than task-driven signal of interest.
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http://dx.doi.org/10.1016/j.neuroimage.2022.119034 | DOI Listing |
Aerosp Med Hum Perform
September 2025
Introduction: This study investigated pilot cognitive engagement patterns across diverse flight conditions using electroencephalography (EEG)-based measurements in a high-fidelity rotary-wing simulation environment.
Methods: A total of 8 experienced U.S.
Nat Sci Sleep
September 2025
Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China.
Aim: Obstructive sleep apnea (OSA) is characterized by repetitive upper airway collapse during sleep, resulting in frequent cortical arousals. However, currently used frequency-based arousal metrics do not sufficiently capture the heterogeneity and clinical significance of arousal responses. The odds ratio product (ORP) is a novel electroencephalographic marker that provides a continuous assessment of sleep depth and has the potential to serve as an objective measure of arousal intensity.
View Article and Find Full Text PDFJ Integr Neurosci
August 2025
School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, Guangdong, China.
Background: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition.
View Article and Find Full Text PDFJ Integr Neurosci
August 2025
CIBA Center for Advanced Biomedical Research, School of Medicine, Autonomous University of Queretaro, 76010 Querétaro, México.
Background: Neurofibrillary tangles, composed of hyperphosphorylated tau, have been implicated in the cognitive impairments observed in Alzheimer's disease. While the precise mechanism remains elusive, cognitive deficits in Alzheimer's disease have been associated with disrupted brain network activity. To investigate this mechanism, researchers have developed several tau transgenic models.
View Article and Find Full Text PDFNeurol Clin Pract
October 2025
Department of Neurology, Division of Neurocritical Care and Emergency Neurology, Program in Trauma, University of Maryland, Baltimore, MD.
Background And Objectives: Guidelines for super-refractory status epilepticus (SRSE) evaluation, management, and prognostication are lacking. Characterization of practice patterns could identify trends and potential areas for future inquiry. We surveyed clinicians who manage SRSE to better understand practice approaches to SRSE evaluation, management, and prognostication.
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