Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy

Abstract

ObjectiveAccurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE.MethodsEPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex.ResultsEPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve a ~5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening.SignificanceAutomated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.

Diffusion and functional MRI in surgical neuromodulation

“…Rs-fMRI-derived seizure onset zone networks (SzNET) and more typical resting state networks (RSN) are connectivity patterns that can be derived from a data-driven solution, which detects differentiating oscillating signals over time and conform to established spatial and temporal network pattern criteria [ [65] , [66] , [67] ]. Hence, abnormal rs-fMRI connectivity has the potential to not only differentiate epilepsy from healthy but can classify epilepsy sub-types [ 68 , 69 ].…”

The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI

“…One of the challenges in relating ICA's resulting independent components (ICs) of rs-fMRI to SOZ is that abnormal BOLD correlations are variable across individuals, with activation foci varying across temporal, parietal, frontal lobes, hippocampus, and cortex ( 17 ). Furthermore, over 50% of the data comprises noise ICs, with the remaining 40–45% attributed to resting state network (RSN), leaving only a small percentage, around 5–10%, associated with SOZ ICs.…”

“…Moreover, the identified SOZs do not conform to the disease characteristics, as bilateral SOZ was identified for patients with unilateral focal TLE ( 19 ). Our prior research has demonstrated that automating the expert rules outlined in ( 16 ) and then implementing them in a carefully structured sequential manner result in a PPV of 65% (±7.8%), surpassing the performance of statistical approaches ( 17 ).…”

“…One of the primary reasons for poor PPV of statistical approaches is that abnormal BOLD correlations, meaning non-noise, not normal resting state fMRI network, and thus is interpreted to be pathological, form <10% of the rs-fMRI ICs from ICA ( 17 , 19 ). This triggers the fundamental Achillies heel of classification science, that is, class imbalance ( 20 ), where the statistical approach lacks enough pathological data to effectively distinguish abnormal patterns amidst significant individual variation.…”

“…Recent research can be broadly categorized into two main areas, as outlined in Table 1 : epilepsy detection ( 21 – 23 ), which entails classifying patients as either epileptic or non-epileptic based on EN identification, and SOZ localization ( 1 , 6 , 17 ), the primary focus of this study. Table 1 presents a comparative analysis of recent studies, considering factors such as the number of subjects, the proportion of the RE subgroup, age range, and the types of ICs identified.…”