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The paper introduces a parametric resonance model for characterizing some features of the brain's electrical activity. This activity is assumed to be a fundamental aspect of brain functionality underpinning functions from basic sensory processing to complex cognitive operations such as memory, reasoning, and emotion. A pivotal element of the proposed parametric model is neuron synchronization which is crucial for generating detectable brain waves. The analysis of the frequency content of brain waves, categorized as delta (0÷4 Hz), theta (4÷7 Hz), alpha (8÷12 Hz), beta (13÷30 Hz), and gamma (30÷100 Hz) reveals, notably, that the mean frequency of each of these brain wave classes is, in sequence, approximately the double of that of the previous one. Based on this observation, the proposed parametric resonance model suggests a cascade of amplification effects. Following the proposed model, in the transition from wakefulness to sleep, the brain wave bands are energized at double frequency by higher frequency neighboring bands; on the contrary, in the sleep to awake transition, brain waves are energized at a half frequency by their lower frequency neighbor waves. Finally, the trend of increasing amplitude values from higher to lower frequencies lends empirical support to the parametric resonant brain model validity.
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http://dx.doi.org/10.1038/s41598-024-76610-8 | DOI Listing |
MAGMA
September 2025
Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3585CX, Utrecht, The Netherlands.
Objective: Within gradient-spoiled transient-state MR sequences like Magnetic Resonance Fingerprinting or Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT), it is examined whether an optimized RF phase modulation can help to improve the precision of the resulting relaxometry maps.
Methods: Using a Cramer-Rao based method called BLAKJac, optimized sequences of RF pulses have been generated for two scenarios (amplitude-only modulation and amplitude + phase modulation) and for several conditions. These sequences have been tested on a phantom, a healthy human brain and a healthy human leg, to reconstruct parametric maps ( and ) as well as their standard deviations.
MAGMA
September 2025
Department of Medical Imaging, (766), Radboud University Medical Center, Geert Grooteplein 10Radboudumc, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands.
Objective: To improve B field homogeneity in prostate MR imaging and spectroscopy using a custom-designed 16-channel external local shim coil array.
Methods: In vivo prostate imaging was performed in seven healthy volunteers (mean age: 40.7 years) without bowel preparation.
Phys Rev Lett
August 2025
Southern University of Science and Technology, Department of Physics, State Key Laboratory of Quantum Functional Materials, and Guangdong Basic Research Center of Excellence for Quantum Science, Shenzhen 518055, China.
Quantum computing is expected to provide an exponential speedup in machine learning. However, optimizing the data loading process, commonly referred to as "quantum data embedding," to maximize classification performance remains a critical challenge. In this Letter, we propose a neural quantum embedding (NQE) technique based on deterministic quantum computation with one qubit (DQC1).
View Article and Find Full Text PDFPhys Rev Lett
August 2025
State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China.
Using e^{+}e^{-} collision data collected with the BESIII detector operating at the Beijing electron positron collider, the cross section of e^{+}e^{-}→π^{+}π^{-}h_{c} is measured at 59 points with center-of-mass energy sqrt[s] ranging from 4.009 to 4.950 GeV with a total integrated luminosity of 22.
View Article and Find Full Text PDFAnn Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
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