  Surgical disconnection of Seizure Onset Zones (SOZs) at an early age is an
effective treatment for Pharmaco-Resistant Epilepsy (PRE). Pre-surgical
localization of SOZs with intra-cranial EEG (iEEG) requires safe and effective
depth electrode placement. Resting-state functional Magnetic Resonance Imaging
(rs-fMRI) combined with signal decoupling using independent component (IC)
analysis has shown promising SOZ localization capability that guides iEEG lead
placement. However, SOZ ICs identification requires manual expert sorting of
100s of ICs per patient by the surgical team which limits the reproducibility
and availability of this pre-surgical screening. Automated approaches for SOZ
IC identification using rs-fMRI may use deep learning (DL) that encodes
intricacies of brain networks from scarcely available pediatric data but has
low precision, or shallow learning (SL) expert rule-based inference approaches
that are incapable of encoding the full spectrum of spatial features. This
paper proposes DeepXSOZ that exploits the synergy between DL based spatial
feature and SL based expert knowledge encoding to overcome performance
drawbacks of these strategies applied in isolation. DeepXSOZ is an
expert-in-the-loop IC sorting technique that a) can be configured to either
significantly reduce expert sorting workload or operate with high sensitivity
based on expertise of the surgical team and b) can potentially enable the usage
of rs-fMRI as a low cost outpatient pre-surgical screening tool. Comparison
with state-of-art on 52 children with PRE shows that DeepXSOZ achieves
sensitivity of 89.79%, precision of 93.6% and accuracy of 84.6%, and reduces
sorting effort by 6.7-fold. Knowledge level ablation studies show a pathway
towards maximizing patient outcomes while optimizing the machine-expert
collaboration for various scenarios.
