SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ekaterina Mikhaylets, Alexandra M. Razorenova, Vsevolod Chernyshev, Nikolay Syrov, Lev Yakovlev, Julia Boytsova, Elena Kokurina, Yulia Zhironkina, Svyatoslav Medvedev, Alexander Kaplan
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引用次数: 0

Abstract

The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.
SDA:应用于 Guhyasamaja 冥想脑电图的检测功能状态的数据驱动算法
本研究提出了一种用于检测时间序列数据中时间连续状态的新方法,称为状态检测算法(SDA)。SDA 可在无标记数据上运行,并基于具有时间连接性约束的 Ward 分层聚类集合,检测时间序列数据中内在功能状态的最佳变化点。该算法能选择最佳状态数和最佳状态边界,最大限度地提高聚类质量指标。我们还介绍了一系列方法,用于在无法获得地面实况注释时估计 SDA 的性能和置信度。这些方法包括信息值分析、配对统计检验和预测建模分析。SDA 在三位经验丰富的佛教修行者在生态环境中进行的 Guhyasamaja 冥想练习的脑电图记录上进行了验证。SDA 使用神经生理学描述符作为输入,包括 PSD、功率指数、相干性和 PLV。对获得的脑电图状态进行事后分析后发现,与基线状态和邻近状态相比,它们之间存在显著差异。研究发现,SDA 在状态顺序组织方面是稳定的,但在应用于随机洗牌的历时(即用作对照的替代受试者数据)时,聚类质量指标较差,状态之间没有统计学意义。SDA 可被视为一种通用的数据驱动方法,可检测与冥想或其他持续的心理和认知过程中的心理过程相关的隐藏功能状态。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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