Pediatric epilepsy surgery from 2000 to 2018: Changes in referral and surgical volumes, patient characteristics, genetic testing, and postsurgical outcomes

OBJECTIVE: Neurosurgery is a safe and effective form of treatment for select children with drug-resistant epilepsy. Still, there is concern that it remains underutilized, and that seizure freedom rates have not improved over time. We investigated referral and surgical practices, patient characteristics, and postoperative outcomes over the past two decades. METHODS: We performed a retrospective cohort study of children referred for epilepsy surgery at a tertiary center between 2000 and 2018. We extracted information from medical records and analyzed temporal trends using regression analyses. RESULTS: A total of 1443 children were evaluated for surgery. Of these, 859 (402 females) underwent surgical resection or disconnection at a median age of 8.5 years (interquartile range [IQR] = 4.6-13.4). Excluding palliative procedures, 67% of patients were seizure-free and 15% were on no antiseizure medication (ASM) at 1-year follow-up. There was an annual increase in the number of referrals (7%, 95% confidence interval [CI] = 5.3-8.6; p < .001) and surgeries (4% [95% CI = 2.9-5.6], p < .001) over time. Duration of epilepsy and total number of different ASMs trialed from epilepsy onset to surgery were, however, unchanged, and continued to exceed guidelines. Seizure freedom rates were also unchanged overall but showed improvement (odds ratio [OR] 1.09, 95% CI = 1.01-1.18; p = .027) after adjustment for an observed increase in complex cases. Children who underwent surgery more recently were more likely to be off ASMs postoperatively (OR 1.04, 95% CI = 1.01-1.08; p = .013). There was a 17% annual increase (95% CI = 8.4-28.4, p < .001) in children identified to have a genetic cause of epilepsy, which was associated with poor outcome. SIGNIFICANCE: Children with drug-resistant epilepsy continue to be put forward for surgery late, despite national and international guidelines urging prompt referral. Seizure freedom rates have improved over the past decades, but only after adjustment for a concurrent increase in complex cases. Finally, genetic testing in epilepsy surgery patients has expanded considerably over time and shows promise in identifying patients in whom surgery is less likely to be successful

Interpretable surface-based detection of focal cortical dysplasias:a Multi-centre Epilepsy Lesion Detection study

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy

Theorising children’s participation: Trans-disciplinary perspectives from South Africa

From text: Children’s participation is a popular rallying cry among child rights activists and community development groups, backed by the recognition of children’s participatory rights in the United Nations Convention on the Rights of the Child (UNCRC). Participation is both a guiding principle of the UNCRC and an explicit right. Article 12 establishes the right of children (who are capable of forming their own views) to express them freely in all matters affecting the children, and for their views to be given due weight, in accordance with age and maturity. In South Africa, children’s participatory rights are recognised in the Children’s Act, the Child Justice Act and, in a more circumscribed way, the South African Schools Act

Theorising children’s participation: Trans-disciplinary perspectives from South Africa

From text: Children’s participation is a popular rallying cry among child rights activists and community development groups, backed by the recognition of children’s participatory rights in the United Nations Convention on the Rights of the Child (UNCRC). Participation is both a guiding principle of the UNCRC and an explicit right. Article 12 establishes the right of children (who are capable of forming their own views) to express them freely in all matters affecting the children, and for their views to be given due weight, in accordance with age and maturity. In South Africa, children’s participatory rights are recognised in the Children’s Act, the Child Justice Act and, in a more circumscribed way, the South African Schools Act

Assessing the effects of subject motion on T 2 relaxation under spin tagging (TRUST) cerebral oxygenation measurements using volume navigators: Effects of Subject Motion on TRUST

© 2017 International Society for Magnetic Resonance in Medicine Purpose: Subject motion may cause errors in estimates of blood T2 when using the T2-relaxation under spin tagging (TRUST) technique on noncompliant subjects like neonates. By incorporating 3D volume navigators (vNavs) into the TRUST pulse sequence, independent measurements of motion during scanning permit evaluation of these errors. Methods: The effects of integrated vNavs on TRUST-based T2 estimates were evaluated using simulations and in vivo subject data. Two subjects were scanned with the TRUST+vNav sequence during prescribed movements. Mean motion scores were derived from vNavs and TRUST images, along with a metric of exponential fit quality. Regression analysis was performed between T2 estimates and mean motion scores. Also, motion scores were determined from independent neonatal scans. Results: vNavs negligibly affected venous blood T2 estimates and better detected subject motion than fit quality metrics. Regression analysis showed that T2 is biased upward by 4.1 ms per 1 mm of mean motion score. During neonatal scans, mean motion scores of 0.6 to 2.0 mm were detected. Conclusion: Motion during TRUST causes an overestimate of T2, which suggests a cautious approach when comparing TRUST-based cerebral oxygenation measurements of noncompliant subjects. Magn Reson Med 78:2283–2289, 2017. © 2017 International Society for Magnetic Resonance in Medicine

Rapid head‐pose detection for automated slice prescription of fetal‐brain MRI

In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment. As motion limits acquisitions to thick slices that preclude retrospective resampling, technologists repeat ~55-second stack-of-slices scans (HASTE) with incrementally reoriented field of view numerous times, deducing the head pose from previous stacks. To address this inefficient workflow, we propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take ~5 seconds to acquire. Our ~2-second procedure automatically locates the fetal brain and eyes, which we derive from maximally stable extremal regions (MSERs). The success rate of the method exceeds 94% in the third trimester, outperforming a trained technologist by up to 20%. The pipeline may be used to automatically orient the anatomical sequence, removing the need to estimate the head pose from 2D views and reducing delays during which motion can occur

Predicting seizure outcome after epilepsy surgery: do we need more complex models, larger samples, or better data?

OBJECTIVE: The accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if 1) training more complex models, 2) recruiting larger sample sizes, or 3) using data-driven selection of clinical predictors would improve our ability to predict post-operative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict post-operative seizure outcome. METHODS: We performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models - a logistic regression, a multilayer perceptron, and an XGBoost model - to predict one-year post-operative seizure outcome on our dataset. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up to N=2,000. Finally, we examined the impact of predictor selection on model performance. RESULTS: Our logistic regression achieved an accuracy of 72% (95% CI=68-75%,AUC=0.72), while our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP =67-74%,AUCMLP =0.70; 95% CIXGBoost own =68-75%,AUCXGBoost own =0.70). There was no significant difference in performance between our three models (all P>0.4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI=59-67%,AUC=0.62; PLR =0.005,PMLP =0.01,PXGBoost own =0.01) on our data. All models showed improved performance with increasing sample size, but limited improvements beyond our current sample. The best model performance was achieved with data-driven feature selection. SIGNIFICANCE: We show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict post-operative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field

Education, parenting and concepts of childhood in England, c. 1945 to c. 1979

Both education and parenting became increasingly ‘child-centred’, or ‘progressive’, in post-war England. This article contends that the impact of this shift for concepts of childhood, and for children themselves, was equivocal. Progressive methods were physically and emotionally demanding for both teachers and parents, and popularised versions of developmental psychology and psychoanalysis shaped a limiting concept of the child. This article also suggests, in line with recent work by Thomson and Shapira, that changing concepts of childhood map democratic selfhood because the capabilities that children lacked were those that must be possessed by the adult citizen. By exploring how children were defined in relation to adults, and how adults’ needs were increasingly subordinated to those of the child, this article also begins to question how we might usefully use age as a ‘category of historical analysis’

Amygdala–medial prefrontal cortex connectivity relates to stress and mental health in early childhood

Early life stress has been associated with disrupted functional connectivity between the amygdala and medial prefrontal cortex (mPFC), but it is unknown how early in development stress-related differences in amygdala-mPFC connectivity emerge. In a resting-state functional connectivity (rs-FC) analysis with 79 four- to seven-year-old children, we found a significant correlation between more adverse experiences and weaker amygdala-mPFC rs-FC. We also found that weaker amygdala-mPFC rs-FC was associated with higher levels of aggressive behavior and attention problems. These findings suggest that the impact of stress on emotional circuitry is detectable in early childhood and that this impact is associated with mental health difficulties. Connectivity in this circuit may be useful as a marker for mental health risk and for tracking the efficacy of early interventions