Hyperbolic embedding of brain networks as a tool for epileptic seizures forecasting

Abstract

The evidence indicates that intracranial EEG connectivity, as estimated from daily resting state recordings from epileptic patients, may be capable of identifying preictal states. In this study, we employed hyperbolic embedding of brain networks to capture non-trivial patterns that discriminate between connectivity networks from days with (preictal) and without (interictal) seizure. A statistical model was constructed by combining hyperbolic geometry and machine learning tools, which allowed for the estimation of the probability of an upcoming seizure. The results demonstrated that representing brain networks in a hyperbolic space enabled an accurate discrimination (85%) between interictal (no-seizure) and preictal (seizure within the next 24 hours) states. The proposed method also demonstrated excellent prediction performances, with an overall accuracy of 87% and an F1-score of 89% (mean Brier score and Brier skill score of 0.12 and 0.37, respectively). In conclusion, our findings indicate that representations of brain connectivity in a latent geometry space can reveal a daily and reliable signature of the upcoming seizure(s), thus providing a promising biomarker for seizure forecasting.

References in corpus (10)

• Kernel methods in machine learning
• Graph analysis of functional brain networks: practical issues in translational neuroscience
• Network Geometry
• Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space
• Network Mapping by Replaying Hyperbolic Growth
• A topological criterion for filtering information in complex brain networks
• Geometric renormalization unravels self-similarity of the multiscale human connectome
• Navigable maps of structural brain networks across species
• Link prediction with hyperbolic geometry
• Detecting local perturbations of networks in a latent hyperbolic embedding space