Low-dimensional representation of brain networks for seizure risk forecasting

TL;DR

This paper presents a new low-dimensional embedding method for brain connectivity networks from intracranial EEG data, enabling improved seizure risk prediction through machine learning-based detection of preictal states.

Contribution

It introduces a novel framework that embeds brain connectivity into a low-dimensional space and defines a biomarker for seizure prediction, advancing real-time forecasting capabilities.

Findings

Low-dimensional embeddings effectively distinguish preictal from interictal states.

The biomarker $\\mathcal{B}$ achieves high classification accuracy.

Method demonstrates robustness across different patients and time periods.

Abstract

Identifying preictal states -- periods during which seizures are more likely to occur -- remains a central challenge in clinical computational neuroscience. In this study, we introduce a novel framework that embeds functional brain connectivity networks, derived from intracranial EEG (iEEG) recordings, into a low-dimensional Euclidean space. This compact representation captures essential topological features of brain dynamics and facilitates the detection of subtle connectivity changes preceding seizures. Using standard machine learning techniques, we define a dimensionless biomarker, $\mathcal{B}$, that discriminates between interictal (seizure-free) and preictal (within 24 hours of seizure) network states. Our method focuses on connectivity patterns among a subset of informative iEEG electrodes, enabling robust classification of brain states across time. We validate our approach using a leave-one-out cross-validation scheme and a pseudo-prospective forecasting strategy, assessing performance with metrics such as F1-score and balanced accuracy. Results show that low-dimensional Euclidean embeddings of iEEG connectivity yield interpretable and predictive markers of preictal activity, offering promising implications for real-time seizure forecasting and individualized therapeutic interventions.