Abstract
As a brain disorder, epilepsy is characterized with abnormal hypersynchronous neural firings. It is known that seizures initiate and propagate in different brain regions. Long-term intracranial multichannel electroencephalography (EEG) reflects broadband ictal activity under seizure occurrence. Network-based techniques are efficient in discovering brain dynamics and offering finger-print features for specific individuals. In this study, we adopt link prediction for proposing a novel workflow aiming to quantify seizure dynamics and uncover pathological mechanisms of epilepsy. A dataset of EEG signals was enrolled that recorded from 8 patients with 3 different types of pharmocoresistant focal epilepsy. Weighted networks are obtained from phase locking value (PLV) in subband EEG oscillations. Common neighbor (CN), resource allocation (RA), Adamic-Adar (AA), and Sorenson algorithms are brought in for link prediction performance comparison. Results demonstrate that RA outperforms its rivals. Similarity, matrix was produced from the RA technique performing on EEG networks later. Nodes are gathered to form sequences by selecting the ones with the highest similarity. It is demonstrated that variations are in accordance with seizure attack in node sequences of gamma band EEG oscillations. What is more, variations in node sequences monitor the total seizure journey including its initiation and termination.
Highlights
Link prediction works on revealing edge production based on network topology and node attributes
We propose a novel work ow for depicting seizure occurrence in refractory focal epilepsy. is network-based work ow detects the seizure occurrence and monitors the total seizure course, which is the rst work aiming at monitoring alteration of network reorganizations throughout seizures as far as we know. e key point is that link prediction technique is adopted to describe connection transitions under seizure attack by way of similarity matrix
It nds out that network variation detecting outburst as well as termination of seizures according to gamma band EEG oscillations in frontal and temporal focal epilepsy
Summary
Link prediction works on revealing edge production based on network topology and node attributes. It is unclear that link prediction could be adopted for uncovering brain dynamics. Brain is a complex system where multiple components work together for cognitive function. When brain is taken as a network, individual brain areas are considered as nodes and their interaction are links. Time-varying network changes reveal spatiotemporal alterations in the brain [5]. Pathological condition might stem from disconnection or rearrangement in the brain [7,8,9]. Investigation of pathological brain architecture changes aids in clinical diagnosis and early warning, and mechanism discovery. More subjective description techniques are needed for investigation of evolutionary process, especially of brain progressions under pathological condition
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