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| Hauptseite > Publikationsdatenbank > Towards classification of spatio-temporal wave patterns based on principal component analysis > print |
| Hauptseite > Publikationsdatenbank > Towards classification of spatio-temporal wave patterns based on principal component analysis > print |
| 001 | 1009393 | ||
| 005 | 20240313094859.0 | ||
| 037 | _ | _ | |a FZJ-2023-02797 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Ito, Junji |0 P:(DE-Juel1)144576 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 32nd Annual Computational Neuroscience Meeting |g CNS2023 |c Leipzig |d 2023-07-15 - 2023-07-19 |w Germany |
| 245 | _ | _ | |a Towards classification of spatio-temporal wave patterns based on principal component analysis |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a Spatio-temporal oscillatory dynamics are found in a variety of subjects in the natural sciences. [1]They are mathematically described in terms of a complex-valued field variable Z(r, t), from whichone can uniquely derive the oscillation amplitude A(r, t) = |Z(r, t)| and oscillation phase θ(r, t) = argZ(r, t), as functions of location r and time t. In a wide class of systems, the phase variable exhibitsspecific spatio-temporal patterns, such as planar wave, radial wave, rotating wave, and so on.These patterns have also been observed in the cerebral cortex of the brain as spatio-temporal waves(STWs) of local field potential (LFP) signals [2-5]. Previous studies have suggested that specific phasepatterns, in particular planar waves, are related to the coordination of spiking activity of singleneurons, and therefore might play a fundamental role in neuronal information processing [6-8].Studying the implications of the STWs for brain function requires a systematic classification methodto group given phase patterns into distinct wave types (e.g. planar, radial, and rotating). Thestrategies taken in previous studies rely on defining a characteristic measure quantifying a feature ofthe phase variable θ(r, t) for each wave type, and setting a threshold on this measure to assign awave type to an episode of data. An inherent shortcoming of this approach is that it requires the adhoc and eventually arbitrary selection of characteristic measures and thresholds.Here we propose a method to quantify phase pattern characteristics based on principal componentanalysis (PCA), which can be used for a non-parametric classification of wave types. In addition tothe standard PCA, we also employ the complex PCA, which works on a complex-valued data matrixand decompose it into components represented by complex-valued vectors (see Figure). We showthat the principal components (PCs) obtained via the complex PCA can naturally represent phaserelationships among variables. We apply both methods to Utah array recordings of LFPs from themacaque motor cortex, which has been reported to exhibit various types of wave patterns, anddiscuss the commonalities and differences between the PCs obtained by the two methods.Furthermore, we relate the time course of the obtained PCs to the time course of the characteristicmeasures of wave types, which were used in previous studies, and examine how individual PCscorrespond to one or multiple of the characteristic measures.We thereby employ the phase pattern quantification with (the standard or complex) PCA as analternative method of wave type classification. Further, decomposing the cortical waves into“eigenmodes” and studying their relations to neuronal and behavioral covariates would provide apromising approach for investigating the functional implications of the waves.References1. Winfree (1980) The geometry of biological time. Vol. 2.2. Ermentrout et al. (2001) Neuron 29(1):33–44. doi: 10.1016/S0896-6273(01)00178-73. Heitmann et al. (2012) Front. Comput. Neurosci. 6:67. doi: 10.3389/fncom.2012.000674. Denker et al. (2018) Sci. Rep. 8(1):5200. doi: 10.1038/s41598-018-22990-75. Townsend et al. (2018) PLoS CB 14(12):e1006643. doi: 10.1371/journal.pcbi.10066436. Takahashi et al. (2015) Nat. Commun. 6(1):1–11. doi: 10.1038/ncomms81697. Vinck and Bosman (2016) Front. Syst. Neurosci. 10:35. doi: 10.3389/fnsys.2016.000358. Davis et al. (2020) Nat. Commun. 12(1):6057. doi: 10.1038/s41467-021-26175-1 |
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