Non-invasive neuroimaging has revealed specific network-based resting-state dynamics in the human brain, yet the underlying neurophysiological mechanism remains unclear. We employed intracranial electroencephalography to characterize local field potentials within the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in 42 participants. We identified stronger within-network phase coherence at low frequencies (θ and α band) within the DMN, and at high frequencies (γ band) within the FPN. Hidden Markov modeling indicated that the DMN exhibited preferential low frequency phase coupling. Phase-amplitude coupling (PAC) analysis revealed that the low-frequency phase in the DMN modulated the high-frequency amplitude envelopes of the FPN, suggesting frequency-dependent characterizations of intrinsic brain networks at rest. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way brain networks communicate at rest.
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http://dx.doi.org/10.1016/j.neuroimage.2024.120773 | DOI Listing |
Sci Rep
December 2024
Department of Medical Microbiology, Radboudumc, Nijmegen, The Netherlands.
The aetiology of Alzheimer's disease (AD) and Parkinson's disease (PD) are unknown and tend to manifest at a late stage in life; even though these neurodegenerative diseases are caused by different affected proteins, they are both characterized by neuroinflammation. Links between bacterial and viral infection and AD/PD has been suggested in several studies, however, few have attempted to establish a link between fungal infection and AD/PD. In this study we adopted a nanopore-based sequencing approach to characterise the presence or absence of fungal genera in both human brain tissue and cerebrospinal fluid (CSF).
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December 2024
Academy of Medical Engineering and Translational Medicine (AMT), Tianjin University, Tianjin, China.
Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI.
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December 2024
Research Centre for Biomedical Engineering (RCBE), School of Science and Technology, City, University of London, Northampton Square, London, EC1V 0HB, UK.
Traditional methods for management of mental illnesses in the post-pandemic setting can be inaccessible for many individuals due to a multitude of reasons, including financial stresses and anxieties surrounding face-to-face interventions. The use of a point-of-care tool for self-management of stress levels and mental health status is the natural trajectory towards creating solutions for one of the primary contributors to the global burden of disease. Notably, cortisol is the main stress hormone and a key logical indicator of hypothalamic-pituitary adrenal (HPA) axis activity that governs the activation of the human stress system.
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December 2024
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.
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December 2024
Division of Cardiology, Department of Internal Medicine, The Jikei University School of Medicine, 3-25-8 Nishi-shimbashi, Minato-ku, Tokyo, 105-8461, Japan.
B-type natriuretic peptide (BNP) levels accurately reflect the degree of cardiac overload in heart failure. Considering cardiac morphology and intracardiac pressure, including the left ventricular end-systolic volume index (LVESVI) and left ventricular end-diastolic volume index (LVEDVI), is essential for cardiac overload assessment. These indexes influence plasma BNP levels, and high heart rate is likely associated with cardiac morphology.
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