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Blood-brain barrier permeability changes: nonlinear analysis of ECoG based on wavelet and machine learning approaches

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Abstract

The blood-brain barrier plays a decisive role in protecting the brain from toxins and pathogens. The ability to analyze the BBB opening (OBBB) is crucial for the treatment of many brain diseases, but it is very difficult to noninvasively monitor OBBB. In this paper we analyze the EEG series of healthy rats in free behaviour and after music-induced OBBB. The research is performed using two completely different methods based on wavelet analysis and machine learning approach. The wavelet-approach demonstrates quantitative changes in the oscillatory structure in EEG signals after music listening, namely, a decrease in the number of patterns to the frequency band \(\varDelta f [1; 2.5] \) Hz. Using methods of machine learning we analyze the number of fragments of EEG realizations recognized as OBBB. After the music impact the number of recognized OBBB is increased in about 50%. Both methods enable us to recognize OBBB and are in a good agreement with each other. The comparative analysis was carried out using F-measures and ROC-curves.

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Acknowledgements

This work has been supported by the RF Government Grant No. 075-15-2019-1885 in part of the biological interpretation and machine learning approach. In the part of the development of numeric method of data analysis this work has been supported by the Council for Grants of the President of the Russian Federation for the State Support of Young Russian Scientists (Project No. MD-645.2020.9). The biological experiment has been partially supported by Russian Science Foundation Grant No. 18-75-10033.

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Authors and Affiliations

  1. Saratov State University, Astrakhanskaya str., 83, Saratov, 410012, Russia

    Nadezhda Semenova, Konstantin Segreev, Andrei Slepnev, Anastasiya Runnova, Maxim Zhuravlev, Inna Blokhina, Alexander Dubrovsky, Maria Klimova, Andrey Terskov, Oxana Semyachkina-Glushkovskaya & Jürgen Kurths

  2. Département d’Optique P. M. Duffieux, Institut FEMTO-ST, Université Bourgogne-Franche-Comté CNRS UMR 6174, Besançon, France

    Nadezhda Semenova

  3. State Medical University, B. Kazachaya str., 112, Saratov, 410012, Russia

    Anastasiya Runnova & Maxim Zhuravlev

  4. Physics Department, Humboldt University, Newtonstrasse 15, 12489, Berlin, Germany

    Oxana Semyachkina-Glushkovskaya & Jürgen Kurths

  5. Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473, Potsdam, Germany

    Jürgen Kurths

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  1. Nadezhda Semenova
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  2. Konstantin Segreev
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Semenova, N., Segreev, K., Slepnev, A. et al. Blood-brain barrier permeability changes: nonlinear analysis of ECoG based on wavelet and machine learning approaches. Eur. Phys. J. Plus 136, 736 (2021). https://doi.org/10.1140/epjp/s13360-021-01715-2

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