Background: Phase-amplitude coupling (PAC) is a key neuronal mechanism. Here, a novel method for quantifying PAC via the Wasserstein distance is presented.
New Method: The Wasserstein distance is an optimization algorithm for minimizing transportation cost and distance. For the first time, the author has applied this distance function to quantify PAC and named the Wasserstein Modulation Index (wMI). As the wMI accommodates the product of the amplitude value in each phase position and the coupling phase position, it allows for extraction of more detailed PAC features from the data.
Results: The validity of the wMI calculations was examined using various simulation data, including sinusoidal and non-sinusoidal waves and empirical data sets. The current findings showed that the wMI is a more robust and stable index for quantifying PAC under various measuring conditions. Specifically, it can better reflect the timing of coupling and distinguish the shape of the coupling distribution than other measurements, both of which are the most significant parameters related to the functionality of PAC. Furthermore, the wMI is also suitable for many applications, such as more data-driven approaches and direct comparisons.
Comparison With Existing Method(s): Compared with Euler-based PAC methods and the Modulation Index (MI), the wMI is not easily affected by the non-sinusoidal nature of neural oscillation and the short data length and enables better reflection of the natures of PAC, such as the timing of coupling and the amplitude distribution in the phase plane, than the MI.
Conclusion: The wMI is expected to extract more detailed PAC characteristics, which could considerably contribute to the neuroscience field.
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http://dx.doi.org/10.1016/j.jneumeth.2022.109578 | DOI Listing |
BMC Musculoskelet Disord
January 2025
Division of Orthopaedic Surgery, Department of Surgery , University of Toronto, Toronto, ON, Canada.
Background: Achilles tendon ruptures are the most common lower extremity tendinous rupture. While there has been extensive research into the management of mid-substance Achilles tendon ruptures, there is a paucity of literature on the management of myotendinous Achilles tendon ruptures.
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Med Image Anal
January 2025
Department of Information Science, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China. Electronic address:
Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain age prediction models to multi-center datasets, particularly those with small-sample sizes, remains a significant challenge that is yet to be addressed. To this end, we propose a multi-center data correction method, which employs a domain adaptation correction strategy with Wasserstein distance of optimal transport, along with maximum mean discrepancy to improve the generalizability of brain-age prediction models on small-sample datasets.
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Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States.
Single-cell technologies have enabled the high-dimensional characterization of cell populations at an unprecedented scale. The innate complexity and increasing volume of data pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e.
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The Medical Big Data Research Center, Northwest University, Xi'an, 710127 China.
Insomnia, as a common sleep disorder, is the most common complaints in medical practice affecting a large proportion of the population on a situational, recurrent or chronic basis. It has been demonstrated that, during wakefulness, patients with insomnia exhibit increased EEG power in theta, beta, and gamma band. However, the relevant mechanisms underlying such power changes are still lack of understanding.
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December 2024
Computer Science Department, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrrique Erro No. 1, Sta. María Tonantzintla, Puebla 72840, Mexico.
Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features.
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