Work

Low-dimensional representation of brain networks for seizure risk forecasting

Year: 2026

Type: article

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, B , that discriminates between interictal (seizure-free) and preictal (within 24 h of seizure) network states. Our method focuses on connectivity patterns among a subset of informative iEEG electrodes, identified through permutation-based testing, 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.

Source: Physica A Statistical Mechanics and its Applications

DOI: 10.1016/j.physa.2026.131751

Authors: Steven Rico-Aparicio, Martin Guillemaud, Alice Longhena, Vincent Navarro, Louis Cousyn, Mario Chavez