Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study

OBJECTIVE: This retrospective, cross-sectional study evaluated the feasibility and potential benefits of incorporating deep-learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug-resistant epilepsy. This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG. METHODS: A neural network classifier was applied to cortical features from MRI data from three cohorts. (1) The network was trained and cross-validated using 34 patients with visible focal cortical dysplasias (FCDs). (2) Specificity was assessed in 20 pediatric healthy controls. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier-predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10 mm between SOZ contacts and classifier-predicted lesions was considered colocalization. RESULTS: In patients with radiologically defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity = 100%). Of the total 34 sEEG patients, 21 patients had a focal cortical SOZ, of whom eight were histopathologically confirmed as having an FCD. The algorithm correctly detected seven of eight of these FCDs (86%). In patients with histopathologically heterogeneous focal cortical lesions, there was colocalization between classifier output and SOZ contacts in 62%. In three patients, the electroclinical profile was indicative of focal epilepsy, but no SOZ was localized on sEEG. In these patients, the classifier identified additional abnormalities that had not been implanted. SIGNIFICANCE: There was a high degree of colocalization between automated lesion detection and sEEG. We have created a framework for incorporation of deep-learning-based MRI lesion detection into sEEG implantation planning. Our findings support the prospective evaluation of automated MRI analysis to plan optimal electrode trajectories

Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study

OBJECTIVE: This retrospective, cross-sectional study evaluated the feasibility and potential benefits of incorporating deep-learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug-resistant epilepsy. This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG. METHODS: A neural network classifier was applied to cortical features from MRI data from three cohorts. (1) The network was trained and cross-validated using 34 patients with visible focal cortical dysplasias (FCDs). (2) Specificity was assessed in 20 pediatric healthy controls. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier-predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10 mm between SOZ contacts and classifier-predicted lesions was considered colocalization. RESULTS: In patients with radiologically defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity = 100%). Of the total 34 sEEG patients, 21 patients had a focal cortical SOZ, of whom eight were histopathologically confirmed as having an FCD. The algorithm correctly detected seven of eight of these FCDs (86%). In patients with histopathologically heterogeneous focal cortical lesions, there was colocalization between classifier output and SOZ contacts in 62%. In three patients, the electroclinical profile was indicative of focal epilepsy, but no SOZ was localized on sEEG. In these patients, the classifier identified additional abnormalities that had not been implanted. SIGNIFICANCE: There was a high degree of colocalization between automated lesion detection and sEEG. We have created a framework for incorporation of deep-learning-based MRI lesion detection into sEEG implantation planning. Our findings support the prospective evaluation of automated MRI analysis to plan optimal electrode trajectories

Early life serum neurofilament dynamics predict neurodevelopmental outcome of preterm infants

Background and purpose To determine whether neurofilament light chain (NfL), a promising serum and cerebrospinal fluid (CSF) biomarker of neuroaxonal damage, predicts functional outcome in preterm infants with neonatal brain injury. Methods Our prospective observational study used a sensitive single-molecule array assay to measure serum and CSF NfL concentrations in preterm infants with moderate to severe peri/intraventricular hemorrhage (PIVH). We determined temporal serum and CSF NfL profiles from the initial diagnosis of PIVH until term-equivalent age and their association with clinical and neurodevelopmental outcome until 2 years of age assessed by Bayley Scales of Infant Development (3rd edition). We fitted univariate and multivariate logistic regression models to determine risk factors for poor motor and cognitive development. Results The study included 48 infants born at < 32 weeks of gestation. Median serum NfL (sNfL) at PIVH diagnosis was 251 pg/mL [interquartile range (IQR) 139–379], decreasing markedly until term-equivalent age to 15.7 pg/mL (IQR 11.1–33.5). CSF NfL was on average 113-fold higher (IQR 40–211) than corresponding sNfL values. Additional cerebral infarction (n = 25)-but not post-hemorrhagic hydrocephalus requiring external ventricular drainage (n = 29) nor any other impairment-was independently associated with sNfL. Multivariate logistic regression models identified sNfL as an independent predictor of poor motor outcome or death at 1 and 2 years. Conclusions Serum neurofilament light chain dynamics in the first weeks of life predict motor outcome in preterm infants with PIVH