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Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

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Title: Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.
Authors: Sukeda, Issey1,2 (AUTHOR) sukeda-issei006@g.ecc.u-tokyo.ac.jp, Matsuda, Takeru1,2 (AUTHOR) matsuda@mist.i.u-tokyo.ac.jp
Source: Neural Computation. Jul2026, Vol. 38 Issue 7, p1135-1179. 45p.
Subjects: Electroencephalography, Sparse graphs, Statistics, Electrophysiology, Functional connectivity
Abstract: Identifying phase coupling from electrophysiological signals recorded by multiple electrodes, such as electroencephalogram (EEG) and electrocorticography (ECoG), helps neuroscientists and clinicians understand underlying brain structures or mechanisms. From a statistical perspective, these signals are multidimensional circular measurements that are correlated with one another and can be effectively modeled using a torus graph model designed for circular random variables. Using the torus graph model avoids the issue of detecting spurious correlations. However, the naive estimation of this model tends to lead to a dense network structure that is difficult to interpret. Therefore, to enhance the interpretability of the brain network structure, this review proposes a sparse estimation method for the torus graph model using regularized score matching combined with information criteria. In numerical simulations, our method successfully recovered the true dependence structure from a synthetic data set. Furthermore, we present analyses of two real data sets, one involving human EEG and the other marmoset ECoG, demonstrating that our method can be widely applied to phase-coupling analysis across different types of neural data. We found that when we used our proposed method, the modularity of the estimated network structure revealed more resolved brain structures and demonstrated differences in trends among individuals. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Jul2026, Vol. 38 Issue 7, p1135-1179. 45p.
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  Data: Identifying phase coupling from electrophysiological signals recorded by multiple electrodes, such as electroencephalogram (EEG) and electrocorticography (ECoG), helps neuroscientists and clinicians understand underlying brain structures or mechanisms. From a statistical perspective, these signals are multidimensional circular measurements that are correlated with one another and can be effectively modeled using a torus graph model designed for circular random variables. Using the torus graph model avoids the issue of detecting spurious correlations. However, the naive estimation of this model tends to lead to a dense network structure that is difficult to interpret. Therefore, to enhance the interpretability of the brain network structure, this review proposes a sparse estimation method for the torus graph model using regularized score matching combined with information criteria. In numerical simulations, our method successfully recovered the true dependence structure from a synthetic data set. Furthermore, we present analyses of two real data sets, one involving human EEG and the other marmoset ECoG, demonstrating that our method can be widely applied to phase-coupling analysis across different types of neural data. We found that when we used our proposed method, the modularity of the estimated network structure revealed more resolved brain structures and demonstrated differences in trends among individuals. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1162/NECO.a.1536
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      – Code: eng
        Text: English
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        PageCount: 45
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      – SubjectFull: Electroencephalography
        Type: general
      – SubjectFull: Sparse graphs
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Electrophysiology
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      – SubjectFull: Functional connectivity
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      – TitleFull: Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.
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              Text: Jul2026
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              Y: 2026
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