Dual-Task Graph Neural Network for Joint Seizure Onset Zone Localization and Outcome Prediction using Stereo EEG

Accurately localizing the brain regions that triggers seizures and predicting whether a patient will be seizure-free after surgery are vital for surgical planning and patient management in drug-resistant epilepsy. Stereo-electroencephalography (sEEG) delivers high-fidelity intracranial recordings that enable clinicians to precisely locate epileptogenic networks. However, the clinical identification is subjective and dependent on the expertise of the clinical team. Data driven approaches in this domain are sparse, despite the fact that sEEG offers high temporal-fidelity related to seizure dynamics that can be leveraged using graph structures ideal for imitating brain networks. In this study, we introduce a dual-task graph-neural network (GNN) framework that operates on windowed sEEG recordings to jointly predict seizure-freedom outcomes and identify seizure-onset-zone (SOZ) channels. We assemble non-overlapping 10 second windows from 51 clinical seizures spread across 20 pediatric patients, with sEEG data annotated by clinical experts. For each temporal window we construct a functional connectivity graph via thresholded Pearson correlations and extract rich node features (spectral, statistical, wavelet, Hjorth and local graph features), alongside six global graph descriptors. We optimize a combined cross-entropy loss with a tunable task-weight, and select model hyper-parameters via Optuna. Under window-level 10-fold cross-validation, the model achieves a mean graph-level accuracy of $89.31 \pm 0.0976 \%$ for seizure-freedom prediction and a node-level SOZ localization accuracy of $94.72. \pm 0.0041 \%$. For the best performing model, we ran additive and leave-one-out ablation studies to explore feature importance for graph and node-level accuracy.

Graph-Based Deep Learning on Stereo EEG for Predicting Seizure Freedom in Epilepsy Patients

Predicting seizure freedom is essential for tailoring epilepsy treatment. But accurate prediction remains challenging with traditional methods, especially with diverse patient populations. This study developed a deep learning-based graph neural network (GNN) model to predict seizure freedom from stereo electroencephalography (sEEG) data in patients with refractory epilepsy. We utilized high-quality sEEG data from 15 pediatric patients to train a deep learning model that can accurately predict seizure freedom outcomes and advance understanding of brain connectivity at the seizure onset zone. Our model integrates local and global connectivity using graph convolutions with multi-scale attention mechanisms to capture connections between difficult-to-study regions such as the thalamus and motor regions. The model achieved an accuracy of 92.4% in binary class analysis, 86.6% in patient-wise analysis, and 81.4% in multi-class analysis. Node and edge-level feature analysis highlighted the anterior cingulate and frontal pole regions as key contributors to seizure freedom outcomes. The nodes identified by our model were also more likely to coincide with seizure onset zones. Our findings underscore the potential of new connectivity-based deep learning models such as GNNs for enhancing the prediction of seizure freedom, predicting seizure onset zones, connectivity analysis of the brain during seizure, as well as informing AI-assisted personalized epilepsy treatment planning.

Geometric Deep Learning for Automated Landmarking of Maxillary Arches on 3D Oral Scans from Newborns with Cleft Lip and Palate

Rapid advances in 3D model scanning have enabled the mass digitization of dental clay models. However, most clinicians and researchers continue to use manual morphometric analysis methods on these models such as landmarking. This is a significant step in treatment planning for craniomaxillofacial conditions. We aimed to develop and test a geometric deep learning model that would accurately and reliably label landmarks on a complicated and specialized patient population -- infants, as accurately as a human specialist without a large amount of training data. Our developed pipeline demonstrated an accuracy of 94.44% with an absolute mean error of 1.676 +/- 0.959 mm on a set of 100 models acquired from newborn babies with cleft lip and palate. Our proposed pipeline has the potential to serve as a fast, accurate, and reliable quantifier of maxillary arch morphometric features, as well as an integral step towards a future fully automated dental treatment pipeline.