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End date: 2024 × Start date: 2020 × Publisher: [London] : Macmillan Publishers Limited, part of Springer Nature × Subject: Brain × WGL Institute: PIK ×

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    Modified wavelet analysis of ECoG-pattern as promising tool for detection of the blood–brain barrier leakage
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Runnova, Anastasiya; Zhuravlev, Maksim; Ukolov, Rodion; Blokhina, Inna; Dubrovski, Alexander; Lezhnev, Nikita; Sitnikova, Evgeniya; Saranceva, Elena; Kiselev, Anton; Karavaev, Anatoly; Selskii, Anton; Semyachkina-Glushkovskaya, Oxana; Penzel, Thomas; Kurths, Jurgen
    A new approach for detection oscillatory patterns and estimation of their dynamics based by a modified CWT skeleton method is presented. The method opens up additional perspectives for the analysis of subtle changes in the oscillatory activity of complex nonstationary signals. The method was applied to analyze unique experimental signals obtained in usual conditions and after the non-invasive increase in the blood–brain barrier (BBB) permeability in 10 male Wistar rats. The results of the wavelet-analysis of electrocorticography (ECoG) recorded in a normal physiological state and after an increase in the BBB permeability of animals demonstrate significant changes between these states during wakefulness of animals and an essential smoothing of these differences during sleep. Sleep is closely related to the processes of observed changes in the BBB permeability.
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    Frequency spectrum recurrence analysis
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Ladeira, Guênia; Marwan, Norbert; Destro-Filho, João-Batista; Davi Ramos, Camila; Lima, Gabriela
    In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.