Biology subjects

Kipinski, L.

Publications and source records attributed to Kipinski, L..

2 recordsLinked to original sources

bioRxiv2021

High-frequency changes in single-trial visual evoked potentials for unattended stimuli in chronic schizophrenia

BackgroundPatients with schizophrenia reveal changes in information processing associated with external stimuli, which is reflected in the measurements of brain evoked potentials. We discuss actual knowledge on electro- (EEG) and magnetoencephalographic (MEG) changes in schizophrenia. New methodThe commonly used averaging technique entails the loss of information regarding the generation of evoked responses. We propose a methodology to describe single-trial (non-averaged) visual evoked potentials (VEP) using spectral and statistical analyses. We analysed EEG data registered in the O1-Cz and O2-Cz leads during unattended pattern-reversal stimulation, collected from a group of adult patients with chronic schizophrenia, and compared them to those of healthy individuals. Short-time single-trial VEP were transformed to the frequency domain using the FFT algorithm. Changes of the spectral power were visualized using spectrograms which were created by stacking single-trial spectra across all trials. Measures of the absolute and the relative spectral power were calculated and compared statistically. ResultsIn schizophrenia, the energy density of VEP oscillations is shifted towards higher (gamma) frequencies, compared to healthy individuals. These differences are statistically significant in all analysed frequency bands for the relative power. This indicates distorted early processing of visual stimuli in schizophrenia. Comparison with existing methodsThe main advantage of the presented methodology is its simplicity and ease of interpretation of obtained results. The presented observations complement the knowledge on gamma oscillations acquired from computationally more complex methods of time-frequency analysis. ConclusionsHigh-frequency changes for single-trial VEPs are detected in chronic schizophrenia.

neuroscience↗

bioRxiv2021

Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

The nonstationarity of EEG/MEG signals is important for understanding the functioning of human brain. From the previous research we know that even very short, i.e. 250--500ms MEG signals are variance-nonstationary. The covariance of stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time. We analyze the data from 148-channel MEG, that represent rest state, unattented listening and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain using the FFT algorithm and for the dominant frequencies 8--12 Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity and gaussianity and based on their properties we propose the ARMA-modelling for their description. The analyzed time series have the weakly stationary properties independently of the functional state of brain and localization. Only their small percentage, mostly related to the cognitive task, still presents nonstationarity. The obtained mathematical models show that the spectral density of analyzed signals depends on only 2--3 previous trials. The presented method has limitations related to FFT resolution and univariate models, but it is not computationally complicated and allows to obtain a low-complex stochastic models of the EEG/MEG spectrum variability. Although the physiological short-time MEG signals are in principle nonstationary in time domain, its power spectrum at the dominant frequencies varies as weakly stationary stochastic process. Described technique has the possible applications in prediction of the EEG/MEG spectral properties in theoretical and clinical neuroscience.

neuroscience↗