This method, which is actually one of the analytical stages of the standard principal component analysis (PCA), reflects the synchrony changes among original signals based on the variation in their eigenvalues. These methods, while useful, suffer from assumptions of time-series linearity and/or stationarity, and may possibly detect spurious levels of synchrony as a result of utilizing bivariate measures, while original signals themselves may consist of several oscillatory components.Įigenvalue decomposition of a linear correlation matrix has been used, to capture changes in synchrony level between a set of signals filtered below 70 Hz. Cross-correlation, , Fourier spectrum-based coherence, wavelet methods, and mutual information measures are among them. However, many of them rely on several assumptions regarding properties of brain electrophysiological signals that render them inappropriate for this purpose. Therefore, instantaneous synchrony analysis of the HFOs may not only reveal crucial insight into predicting seizures but also may provide a better understanding of the electrophysiological dynamics underlying them -.Ī variety of mathematical methods have been proposed to identify the instantaneous synchrony changes among neuronal networks in the brain. Recently, a number of researchers have reported that high-frequency oscillations (HFOs) including those in ripple (80–250 Hz) and fast-ripple (250–600 Hz) bands can be utilized as reliable biomarkers of epileptogenic brain tissues. However, identifying the level of synchrony among non-linear, non-stationary neuronal oscillators extracted from an intracranial electroencephalographic (iEEG) data or local field potential (LFP) signal represents a challenging issue in neurological science. Evaluating synchrony dynamics may not only reveal crucial components essential to EEG interpretation but may also provide insight into the dynamics of seizures as they evolve. This conclusion largely arises from the high-amplitude, often rhythmic neuronal activity associated with ictal activity during seizures. Seizures have historically been considered as hypersynchronous states arising from an imbalance between excitatory and inhibitory inputs among large networks of neurons in the brain. However, the synchrony level can be altered in several neurological disorders such as epilepsy, which itself may promote epileptic seizures. It has been widely shown that synchronous electrophysiological activity between neuronal networks underlies essential motor or cognitive processes in normal brain function. This result suggests that hyper-synchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.Įpilepsy is a chronic neurological disorder characterized by recurrent, spontaneous, paroxysmal neural discharges, called epileptic seizures. However, the network phase-synchrony started to increase towards seizure end and achieved its maximum level at seizure offset for both types of epilepsy. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1–600 Hz), ripple (80–250 Hz), and fast-ripple (250–600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode de-composition (NA-MEMD). In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |