Detecting synchronization in brain activity

Abstract

Billions of neurons make up our brains where the emergence of synchronous behavior is one of the most fundamental questions in the field of neuroscience. In a system as complex as the human brain, synchronization of neuronal activity can be useful and necessary as during the sleep cycles and in consolidation of memory but can also be problematic and undesirable in disorders such as epilepsy and Parkinson's disease. The goal in this study is to shed light on a particular type of neuronal synchronization associated with epileptic seizures that result from a central nervous system disorder characterized by abnormal brain activity. The approach consists of analyzing electroencephalogram (EEG) data containing information about neuronal electrical activity of epileptic patients before, during and after a seizure. The database includes EEG recordings of 14 patients obtained from the Unit of Neurology and Neurophysiology of the University of Siena, with electrical activity collected from 29 brain areas through electrodes placed on the scalp of the patients [1]. The data is initially preprocessed using filters to reduce the noise level [3], and the phase of the filtered signal is extracted using the Hilbert Transform and the Phase Estimation by Means of Frequency (PEMF) methods [2]. The phase of each of the 29 signal is then compared over time with each of the other 28 signals to verify whether the signals have their phases in synchrony, or not. We compute the phase locking value (PLV) to quantify the level of synchronization between pairs of signals and obtain color maps for graphical visualization of the overall behavior of the brain electrical activity (Fig. 1, top panel). The functional connectivity in the pre, during, and post seizure of a patient experiencing a seizure is depicted in Fig. 1, bottom panel. Each line represents a functional connection were PLV was grater than 0.95. Our preliminary results show that there are more synchronized channels during the seizure across patients compared to pre and post seizure. Additionally, neurons of certain areas of the brain tend to be more synchronous than others during the epileptic seizure. The approach considered in this work can be extended beyond epilepsy, with potential implementation to study other neurological disorders including schizophrenia and Parkinson's disease, for example.