Objective: Posttraumatic stress disorder (PTSD) is distinct from anxiety disorders in its etiology and clinical symptomatology, and was reclassified into trauma- and stressor-related disorders in DSM-5. This study aimed to find neurophysiological correlates differentiating PTSD from anxiety disorders using resting-state quantitative electroencephalography (qEEG).
Methods: Thirty-six patients with either PTSD or acute stress disorder and 79 patients with anxiety disorder were included in the analysis. qEEG data of absolute and relative powers and patients' medication status on the day of qEEG examination were obtained. Electrodes were grouped into frontal, central, and posterior regions to analyze for regional differences. General linear models were utilized to test for group differences in absolute and relative powers while controlling for medications.
Results: PTSD patients differed from those with anxiety disorders in overall absolute powers [F(5,327)=2.601, p=0.025]. Specifically, overall absolute delta powers [F(1,331)=4.363, p=0.037], and overall relative gamma powers [F(1,331)=3.965, p=0.047] were increased in PTSD group compared to anxiety disorder group. Post hoc analysis regarding brain regions showed that the increase in absolute delta powers were localized to the posterior region [F(1,107)=4.001, p=0.048]. Additionally, frontal absolute gamma powers [F(1,107)=4.138, p=0.044] were increased in PTSD group compared to anxiety disorder group.
Conclusion: Our study suggests increased overall absolute delta powers and relative gamma powers as potential markers that could differentiate PTSD from anxiety disorders. Moreover, increased frontal absolute gamma and posterior delta powers might pose as novel markers of PTSD, which may reflect its distinct symptomatology.
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http://dx.doi.org/10.30773/pi.2018.09.30 | DOI Listing |
Chem Commun (Camb)
January 2025
Functional Materials and Electrochemistry Lab, Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
Electrically rechargeable zinc-air batteries (ZABs) are emerging as promising energy storage devices in the post-lithium era, leveraging the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) at the air cathodes. Efficient bifunctional oxygen electrocatalysts, capable of catalyzing both the ORR and OER, are essential for the operation of rechargeable ZABs. Traditional Pt- and RuO/IrO-based catalysts are not ideal, as they lack sufficient bifunctional ORR and OER activity, exhibit limited long-term durability, require high overpotentials and are expensive.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt.
The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in the domain of ML, making wind energy the key player in fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analysis or by using Supervisory Control and Data Acquisition (SCADA) data of wind turbines, wherein conventional fault pattern identification is a time-consuming, guesswork process. This work proposed an intelligent automated approach to early fault detection through the implementation of the HARO (Huber Adam Regression Optimizer) model, which combines Transformer networks with Lasso Regression and the Adam optimizer.
View Article and Find Full Text PDFAnesthesiology
January 2025
Department of Anesthesiology, Brigham and Women's Hospital and Harvard Medical School, Boston MA, USA.
Introduction: Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. We sought to determine whether internal EEG subparameters extracted by the Bispectral Index (BIS) monitor, a device commonly used to estimate depth-of-anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest.
Methods: In this retrospective cohort study, we trained a 3-layer neural network to predict recovery of consciousness to the point of command following versus not based on 48 hours of continuous EEG recordings in 315 comatose patients admitted to a single US academic medical center after cardiac arrest (Derivation cohort: N=181; Validation cohort: N=134).
Brain Behav
January 2025
Department of Aerospace Hygiene, Faculty of Aerospace Medicine, Air Force Medical University, Xi'an, China.
Introduction: Multitasking during flights leads to a high mental workload, which is detrimental for maintaining task performance. Electroencephalography (EEG) power spectral analysis based on frequency-band oscillations and microstate analysis based on global brain network activation can be used to evaluate mental workload. This study explored the effects of a high mental workload during simulated flight multitasking on EEG frequency-band power and microstate parameters.
View Article and Find Full Text PDFBrain Behav
January 2025
Research Institute for Health Sciences and Technologies (SABITA), Neuroscience Research Center, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab, Istanbul Medipol University, Istanbul, Turkey.
Introduction: The neural substrates of reasoning, a cognitive ability we use constantly in daily life, are still unclear. Reasoning can be divided into two types according to how the inference process works and the certainty of the conclusions. In deductive reasoning, certain conclusions are drawn from premises by applying the rules of logic.
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