Long-term neuropsychological trajectories in children with epilepsy: does surgery halt decline?

Neuropsychological impairments are common in children with drug-resistant epilepsy. It has been proposed that epilepsy surgery may alleviate these impairments by providing seizure freedom; however, findings from prior studies have been inconsistent. We mapped long-term neuropsychological trajectories in children before and after undergoing epilepsy surgery, to measure the impact of disease course and surgery on functioning. We performed a retrospective cohort study of 882 children who had undergone epilepsy surgery at Great Ormond Street Hospital (1990-2018). We extracted patient information and neuropsychological functioning - obtained from IQ tests (domains: Full-Scale IQ, Verbal IQ, Performance IQ, Working Memory, and Processing Speed) and tests of academic attainment (Reading, Spelling and Numeracy) - and investigated changes in functioning using regression analyses. We identified 500 children (248 females) who had undergone epilepsy surgery (median age at surgery = 11.9 years, interquartile range = [7.8,15.0]) and neuropsychology assessment. These children showed declines in all domains of neuropsychological functioning in the time leading up to surgery (all p-values ≤ 0.001; e.g., ßFSIQ = -1.9, SEFSIQ = 0.3, pFSIQ < 0.001). Children lost on average one to four points per year, depending on the domain considered; 27-43% declined by 10 or more points from their first to their last preoperative assessment. At the time of presurgical evaluation, most children (46-60%) scored one or more standard deviations below the mean (<85) on the different neuropsychological domains; 37% of these met the threshold for intellectual disability (Full-Scale IQ < 70). On a group level, there was no change in performance from pre- to postoperative assessment on any of the domains (all p-values > 0.128). However, children who became seizure-free through surgery showed higher postoperative neuropsychological performance (e.g., rrb-FSIQ = 0.37, p < 0.001). These children continued to demonstrate improvements in neuropsychological functioning over the course of their long-term follow-up (e.g., ßFSIQ = 0.9, SEFSIQ = 0.3, pFSIQ = 0.004). Children who had discontinued antiseizure medication (ASM) treatment at one-year follow-up showed an eight-to-13-point advantage in postoperative Working Memory, Processing Speed, and Numeracy, and greater improvements in Verbal IQ, Working Memory, Reading, and Spelling (all p-values < 0.034) over the postoperative period compared to children who were seizure-free and still receiving ASMs. In conclusion, by providing seizure freedom and the opportunity for ASM cessation, epilepsy surgery may not only halt but reverse the downward trajectory that children with drug-resistant epilepsy display in neuropsychological functioning. To halt this decline as soon as possible, or potentially prevent it from occurring in the first place, children with focal epilepsy should be considered for epilepsy surgery as early as possible after diagnosis.

Cognitive outcome and its neural correlates after cardiorespiratory arrest in childhood.

Hypoxia-ischaemia (HI) can result in structural brain abnormalities, which in turn can lead to behavioural deficits in various cognitive and motor domains, in both adult and paediatric populations. Cardiorespiratory arrest (CA) is a major cause of hypoxia-ischaemia in adults, but it is relatively rare in infants and children. While the effects of adult CA on brain and cognition have been widely studied, to date, there are no studies examining the neurodevelopmental outcome of children who suffered CA early in life. Here, we studied the long-term outcome of 28 children who suffered early CA (i.e., before age 16). They were compared to a group of control participants (n = 28) matched for age, sex and socio-economic status. The patient group had impairments in the domains of memory, language and academic attainment (measured using standardised tests). Individual scores within the impaired range were most commonly found within the memory domain (79%), followed by academic attainment (50%), and language (36%). The patient group also had reduced whole brain grey matter volume, and reduced volume and fractional anisotropy of the white matter. In addition, lower performance on memory tests was correlated with bilaterally reduced volume of the hippocampi, thalami, and striatum, while lower attainment scores were correlated with bilateral reduction of fractional anisotropy in the superior cerebellar peduncle, the main output tract of the cerebellum. We conclude that patients who suffered early CA are at risk of developing specific cognitive deficits associated with structural brain abnormalities. RESEARCH HIGHLIGHTS: Our data shed light on the long-term outcome and associated neural mechanisms after paediatric hypoxia-ischaemia as a result of cardiorespiratory arrest. Patients had impaired scores on memory, language and academic attainment. Memory impairments were associated with smaller hippocampi, thalami, and striatum. Lower academic attainment correlated with reduced fractional anisotropy of the superior cerebellar peduncle.

Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm.

AIM: To evaluate a lesion detection algorithm designed to detect focal cortical dysplasia (FCD) in children undergoing stereoelectroencephalography (SEEG) as part of their presurgical evaluation for drug-resistant epilepsy. METHOD: This was a prospective, single-arm, interventional study (Idea, Development, Exploration, Assessment, and Long-Term Follow-Up phase 1/2a). After routine SEEG planning, structural magnetic resonance imaging sequences were run through an FCD lesion detection algorithm to identify putative clusters. If the top three clusters were not already sampled, up to three additional SEEG electrodes were added. The primary outcome measure was the proportion of patients who had additional electrode contacts in the SEEG-defined seizure-onset zone (SOZ). RESULTS: Twenty patients (median age 12 years, range 4-18 years) were enrolled, one of whom did not undergo SEEG. Additional electrode contacts were part of the SOZ in 1 out of 19 patients while 3 out of 19 patients had clusters that were part of the SOZ but they were already implanted. A total of 16 additional electrodes were implanted in nine patients and there were no adverse events from the additional electrodes. INTERPRETATION: We demonstrate early-stage prospective clinical validation of a machine learning lesion detection algorithm used to aid the identification of the SOZ in children undergoing SEEG. We share key lessons learnt from this evaluation and emphasize the importance of robust prospective evaluation before routine clinical adoption of such algorithms. WHAT THIS PAPER ADDS: The focal cortical dysplasia detection algorithm collocated with the seizure-onset zone (SOZ) in 4 out of 19 patients. The algorithm changed the resection boundaries in 1 of 19 patients undergoing stereoelectroencephalography for drug-resistant epilepsy. The patient with an altered resection due to the algorithm was seizure-free 1 year after resective surgery. Overall, the algorithm did not increase the proportion of patients in whom SOZ was identified.

Networks Underlie Temporal Onset of Dysplasia-Related Epilepsy: A MELD Study.

OBJECTIVE: The purpose of this study was to evaluate if focal cortical dysplasia (FCD) co-localization to cortical functional networks is associated with the temporal distribution of epilepsy onset in FCD. METHODS: International (20 center), retrospective cohort from the Multi-Centre Epilepsy Lesion Detection (MELD) project. Patients included if >3 years old, had 3D pre-operative T1 magnetic resonance imaging (MRI; 1.5 or 3 T) with radiologic or histopathologic FCD after surgery. Images processed using the MELD protocol, masked with 3D regions-of-interest (ROI), and co-registered to fsaverage_sym (symmetric template). FCDs were then co-localized to 1 of 7 distributed functional cortical networks. Negative binomial regression evaluated effect of FCD size, network, histology, and sulcal depth on age of epilepsy onset. From this model, predictive age of epilepsy onset was calculated for each network. RESULTS: Three hundred eighty-eight patients had median age seizure onset 5 years (interquartile range [IQR] = 3-11 years), median age at pre-operative scan 18 years (IQR = 11-28 years). FCDs co-localized to the following networks: limbic (90), default mode (87), somatomotor (65), front parietal control (52), ventral attention (32), dorsal attention (31), and visual (31). Larger lesions were associated with younger age of onset (p = 0.01); age of epilepsy onset was associated with dominant network (p = 0.04) but not sulcal depth or histology. Sensorimotor networks had youngest onset; the limbic network had oldest age of onset (p values <0.05). INTERPRETATION: FCD co-localization to distributed functional cortical networks is associated with age of epilepsy onset: sensory neural networks (somatomotor and visual) with earlier onset, and limbic latest onset. These variations may reflect developmental differences in synaptic/white matter maturation or network activation and may provide a biological basis for age-dependent epilepsy onset expression. ANN NEUROL 2022;92:503-511.

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.

Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice.

Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%-50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment.

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 postoperative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict postoperative 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 1-year postoperative seizure outcome on our data set. 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 = 2000. Finally, we examined the impact of predictor selection on model performance. RESULTS: Our logistic regression achieved an accuracy of 72% (95% confidence interval [CI] = 68%-75%, area under the curve [AUC] = .72), whereas our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP = 67%-74%, AUCMLP = .70; 95% CIXGBoost own = 68%-75%, AUCXGBoost own = .70). There was no significant difference in performance between our three models (all p > .4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI = 59%-67%, AUC = .62; pLR = .005, pMLP = .01, pXGBoost own = .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 postoperative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.

Towards network-guided neuromodulation for epilepsy.

Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.

Sleep homeostasis, seizures, and cognition in children with focal epilepsy.

AIM: To investigate the link between sleep disruption and cognitive impairment in childhood epilepsy by studying the effect of epilepsy on sleep homeostasis, as reflected in slow-wave activity (SWA). METHOD: We examined SWA from overnight EEG-polysomnography in 19 children with focal epilepsy (mean [SD] age 11 years 6 months [3 years], range 6 years 6 months-15 years 6 months; 6 females, 13 males) and 18 age- and sex-matched typically developing controls, correlating this with contemporaneous memory consolidation task scores, full-scale IQ, seizures, and focal interictal discharges. RESULTS: Children with epilepsy did not differ significantly from controls in overnight SWA decline (p = 0.12) or gain in memory performance with sleep (p = 0.27). SWA was lower in patients compared to controls in the first hour of non-rapid eye movement sleep (p = 0.021), although not in those who remained seizure-free (p = 0.26). Full-scale IQ did not correlate with measures of SWA in patients or controls. There was no significant difference in SWA measures between focal and non-focal electrodes. INTERPRETATION: Overnight SWA decline is conserved in children with focal epilepsy and may underpin the preservation of sleep-related memory consolidation in this patient group. Reduced early-night SWA may reflect impaired or immature sleep homeostasis in those with a higher seizure burden. WHAT THIS PAPER ADDS: The decline in slow-wave activity (SWA) across the night, reflecting global synaptic downscaling, was preserved in children with focal lesional epilepsies. Sleep benefited memory consolidation in this group of patients, as in typically developing children. Reduced early-night SWA was associated with increased likelihood of a subsequent seizure.

Quantitative MRI susceptibility mapping reveals cortical signatures of changes in iron, calcium and zinc in malformations of cortical development in children with drug-resistant epilepsy.

OBJECTIVE: Malformations of cortical development (MCD), including focal cortical dysplasia (FCD), are the most common cause of drug-resistant focal epilepsy in children. Histopathological lesion characterisation demonstrates abnormal cell types and lamination, alterations in myelin (typically co-localised with iron), and sometimes calcification. Quantitative susceptibility mapping (QSM) is an emerging MRI technique that measures tissue magnetic susceptibility (χ) reflecting it's mineral composition. We used QSM to investigate abnormal tissue composition in a group of children with focal epilepsy with comparison to effective transverse relaxation rate (R2*) and Synchrotron radiation X-ray fluorescence (SRXRF) elemental maps. Our primary hypothesis was that reductions in χ would be found in FCD lesions, resulting from alterations in their iron and calcium content. We also evaluated deep grey matter nuclei for changes in χ with age. METHODS: QSM and R2* maps were calculated for 40 paediatric patients with suspected MCD (18 histologically confirmed) and 17 age-matched controls. Patients' sub-groups were defined based on concordant electro-clinical or histopathology data. Quantitative investigation of QSM and R2* was performed within lesions, using a surface-based approach with comparison to homologous regions, and within deep brain regions using a voxel-based approach with regional values modelled with age and epilepsy as covariates. Synchrotron radiation X-ray fluorescence (SRXRF) was performed on brain tissue resected from 4 patients to map changes in iron, calcium and zinc and relate them to MRI parameters. RESULTS: Compared to fluid-attenuated inversion recovery (FLAIR) or T1-weighted imaging, QSM improved lesion conspicuity in 5% of patients. In patients with well-localised lesions, quantitative profiling demonstrated decreased χ, but not R2*, across cortical depth with respect to the homologous regions. Contra-lateral homologous regions additionally exhibited increased χ at 2-3 mm cortical depth that was absent in lesions. The iron decrease measured by the SRXRF in FCDIIb lesions was in agreement with myelin reduction observed by Luxol Fast Blue histochemical staining. SRXRF analysis in two FCDIIb tissue samples showed increased zinc and calcium in one patient, and decreased iron in the brain region exhibiting low χ and high R2* in both patients. QSM revealed expected age-related changes in the striatum nuclei, substantia nigra, sub-thalamic and red nucleus. CONCLUSION: QSM non-invasively revealed cortical/sub-cortical tissue alterations in MCD lesions and in particular that χ changes in FCDIIb lesions were consistent with reduced iron, co-localised with low myelin and increased calcium and zinc content. These findings suggest that measurements of cortical χ could be used to characterise tissue properties non-invasively in epilepsy lesions.

NMDA-receptor antibodies alter cortical microcircuit dynamics.

NMDA-receptor antibodies (NMDAR-Abs) cause an autoimmune encephalitis with a diverse range of EEG abnormalities. NMDAR-Abs are believed to disrupt receptor function, but how blocking this excitatory synaptic receptor can lead to paroxysmal EEG abnormalities-or even seizures-is poorly understood. Here we show that NMDAR-Abs change intrinsic cortical connections and neuronal population dynamics to alter the spectral composition of spontaneous EEG activity and predispose brain dynamics to paroxysmal abnormalities. Based on local field potential recordings in a mouse model, we first validate a dynamic causal model of NMDAR-Ab effects on cortical microcircuitry. Using this model, we then identify the key synaptic parameters that best explain EEG paroxysms in pediatric patients with NMDAR-Ab encephalitis. Finally, we use the mouse model to show that NMDAR-Ab-related changes render microcircuitry critically susceptible to overt EEG paroxysms when these key parameters are changed, even though the same parameter fluctuations are tolerated in the in silico model of the control condition. These findings offer mechanistic insights into circuit-level dysfunction induced by NMDAR-Ab.

The Pair Test: A computerised measure of learning and memory.

There is increasing interest in the assessment of learning and memory in typically developing children as well as in children with neurodevelopmental disorders. However, neuropsychological assessments have been hampered by the dearth of standardised tests that enable direct comparison between distinct memory processes or between types of stimulus materials. We developed a tablet-based paired-associate learning paradigm, the Pair Test, based on neurocognitive models of learning and memory. The aims are to (i) establish the utility of this novel memory tool for use with children across a wide age range, and (ii) examine test validity, reliability and reproducibility of the construct. The convergent validity of the test was found to be adequate, and higher test reliability was shown for the Pair Test compared to standardised measures. Moderate test-retest reproducibility was shown, despite a long time interval between sessions (14 months). Moreover, the Pair Test is able to capture developmental changes in memory, and can therefore chart the developmental trajectory of memory and learning functions across childhood and adolescence. Finally, we used this novel instrument to acquire normative data from 130 typically developing children, aged 8-18 years. Age-stratified normative data are provided for learning, delayed recall and delayed recognition, for measures of verbal and non-verbal memory. The Pair Test thus provides measures of learning and memory accounting for encoding, consolidation and retrieval processes. As such, the standardised test results can be used to determine the status of learning and memory in healthy children, and also to identify deficits in paediatric patients at risk of damage to the neural network underlying mnemonic functions.

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.

MRI profiling of focal cortical dysplasia using multi-compartment diffusion models.

OBJECTIVE: Focal cortical dysplasia (FCD) lesion detection and subtyping remain challenging on conventional MRI. New diffusion models such as the spherical mean technique (SMT) and neurite orientation dispersion and density imaging (NODDI) provide measurements that potentially produce more specific maps of abnormal tissue microstructure. This study aims to assess the SMT and NODDI maps for computational and radiological lesion characterization compared to standard fractional anisotropy (FA) and mean diffusivity (MD). METHODS: SMT, NODDI, FA, and MD maps were calculated for 33 pediatric patients with suspected FCD (18 histologically confirmed). Two neuroradiologists scored lesion visibility on clinical images and diffusion maps. Signal profile changes within lesions and homologous regions were quantified using a surface-based approach. Diffusion parameter changes at multiple cortical depths were statistically compared between FCD type IIa and type IIb. RESULTS: Compared to fluid-attenuated inversion recovery (FLAIR) or T1-weighted imaging, lesions conspicuity on NODDI intracellular volume fraction (ICVF) maps was better/equal/worse in 5/14/14 patients, respectively, while on SMT intra-neurite volume fraction (INVF) in 3/3/27. Compared to FA or MD, lesion conspicuity on the ICVF was better/equal/worse in 27/4/2, while on the INVF in 20/7/6. Quantitative signal profiling demonstrated significant ICVF and INVF reductions in the lesions, whereas SMT microscopic mean, radial, and axial diffusivities were significantly increased. FCD type IIb exhibited greater changes than FCD type IIa. No changes were detected on FA or MD profiles. SIGNIFICANCE: FCD lesion-specific signal changes were found in ICVF and INVF but not in FA and MD maps. ICVF and INVF showed greater contrast than FLAIR in some cases and had consistent signal changes specific to FCD, suggesting that they could improve current presurgical pediatric epilepsy imaging protocols and can provide features useful for automated lesion detection.

Developmental conduction aphasia after neonatal stroke.

OBJECTIVE: Impairment of speech repetition following injury to the dorsal language stream is a feature of conduction aphasia, a well-described "disconnection syndrome" in adults. The impact of similar lesions sustained in infancy has not been established. METHODS: We compared language outcomes in term-born individuals with confirmed neonatal stroke (n = 30, age = 7-18 years, left-sided lesions in 21 cases) to matched controls (n = 40). Injury to the dorsal and/or ventral language streams was assessed using T1 - and T2 -weighted magnetic resonance imaging (MRI) and diffusion tractography. Language lateralization was determined using functional MRI. RESULTS: At the group level, left dorsal language stream injury was associated with selective speech repetition impairment for nonwords (p = 0.021) and sentences (p < 0.0001). The majority of children with significant repetition impairment had retained left hemisphere language representation, but right hemisphere dominance was correlated with minimal or absent repetition deficits. Post hoc analysis of the repetition-impaired group revealed additional language-associated deficits, but these were more subtle and variable. INTERPRETATION: We conclude that (1) despite the considerable plasticity of the infant brain, early dorsal language stream injury can result in specific and long-lasting problems with speech repetition that are similar to the syndrome of conduction aphasia seen in adults; and (2) language reorganization to the contralateral hemisphere has a protective effect. Ann Neurol 2018;83:664-675 Ann Neurol 2018;83:664-675.

Do behavior and emotions improve after pediatric epilepsy surgery? A systematic review.

OBJECTIVE: The objective was to systematically review studies that have focused on behavioral and emotional functioning before (baseline) and after (follow-up) pediatric epilepsy surgery. METHODS: The systematic review was carried out according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed and EMBASE were searched from inception. Findings are described with respect to (1) changes in behavior and emotions between baseline and follow-up, (2) factors associated with changes in behavior and emotions, and (3) impact of study quality on findings. RESULTS: Fifteen studies met inclusion criteria. The majority of studies employed parent report screening checklists. In these studies, scores were reported to have significantly improved at follow-up on at least one domain in seven studies and not to have changed significantly in two studies. In no studies was a deterioration in behavior noted. In studies that used Diagnostic and Statistical Manual of Mental Disorders (DSM) clinical diagnoses, no significant change was noted in the numbers of children diagnosed at baseline and at follow-up. In total, 21 children lost diagnoses, whereas 16 children developed new diagnoses. A better seizure outcome was associated with improvements in behavioral-emotional functioning at follow-up in three of the four studies where it was considered. In terms of study quality, none of the studies was rated as strong (ie, had no weak ratings on a quality assessment tool). SIGNIFICANCE: There is some evidence of improvement in emotional and behavioral functioning after epilepsy surgery. However, this is confined to scores on parent-reported screening measures of emotional and behavioral symptoms and not clinical diagnoses. Future research should focus on including responses from multiple respondents (child, parent, teacher) when using screening instruments, but also diagnostic interviews. There is a need for long-term follow-up (beyond 2 years) with sufficiently large sample sizes including data from nonsurgery controls to understand factors associated with changes in functioning postsurgery.

Determinants of IQ outcome after focal epilepsy surgery in childhood: A longitudinal case-control neuroimaging study.

OBJECTIVE: Intelligence quotient (IQ) outcomes after pediatric epilepsy surgery show significant individual variation. Clinical factors such as seizure cessation or antiepileptic medication discontinuation have been implicated, but do not fully account for the heterogeneity seen. Less is known about the impact of neurobiological factors, such as brain development and resection location. This study examines clinical and neuroimaging factors associated with cognitive outcome after epilepsy surgery in childhood. METHODS: Fifty-two children (28 boys, 24 girls) were evaluated for epilepsy surgery and reassessed on average 7.7 years later. In the intervening time, 13 were treated pharmacologically and 39 underwent focal surgery (17 temporal, 16 extratemporal, six multilobar; mean age at surgery = 14.0 years). Pre- and postsurgical assessments included IQ tests and T1-weighted brain images. Predictors of IQ change were investigated, including voxel-based analyses of resection location, and gray and white matter volume change. RESULTS: Overall modest IQ improvement was seen in children treated surgically, but not in those treated pharmacologically only. Applying a ≥10-point change threshold, 39% of the surgically treated children improved, whereas 10% declined. Clinical factors associated with IQ increases were lower preoperative IQ and longer follow-up duration, whereas seizure and antiepileptic medication cessation were not predictive. Among neuroimaging factors, we observed that left anterior temporal resections impacted negatively on verbal reasoning, linked to full-scale IQ decline. In contrast, gray matter volume change in ipsi- and contralesional hemispheres was positively correlated with IQ change. Voxel-based morphometry identified the gray matter volume change in the contralesional dorsolateral frontal cortex as most strongly associated with IQ improvement. SIGNIFICANCE: We show that a variety of factors are likely to contribute to patterns of postsurgical change in IQ. Neuroimaging results indicate that left anterior temporal resections constrain development of verbal cognition, whereas simultaneously cortical growth after surgical treatment can support improvements in IQ.

Calcium imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures.

Pathophysiological explanations of epilepsy typically focus on either the micro/mesoscale (e.g. excitation-inhibition imbalance), or on the macroscale (e.g. network architecture). Linking abnormalities across spatial scales remains difficult, partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved. Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole. Spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models, allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics. This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale. Fluctuations of both synaptic connection strength and their temporal dynamics are required to explain observed seizure patterns. These findings highlight distinct changes in local (intrinsic) and long-range (extrinsic) synaptic transmission dynamics as a possible seizure pathomechanism and illustrate how our Bayesian model inversion approach can be used to link existing neural mass models of seizure activity and novel experimental methods.

Cognitive neuroscience using wearable magnetometer arrays: Non-invasive assessment of language function.

Recent work has demonstrated that Optically Pumped Magnetometers (OPMs) can be utilised to create a wearable Magnetoencephalography (MEG) system that is motion robust. In this study, we use this system to map eloquent cortex using a clinically validated language lateralisation paradigm (covert verb generation: 120 trials, ∼10 min total duration) in healthy adults (n = 3). We show that it is possible to lateralise and localise language function on a case by case basis using this system. Specifically, we show that at a sensor and source level we can reliably detect a lateralising beta band (15-30 Hz) desynchronization in all subjects. This is the first study of human cognition using OPMs and not only highlights this technology's utility as tool for (developmental) cognitive neuroscience but also its potential to contribute to surgical planning via mapping of eloquent cortex, especially in young children.

Topographic principles of cortical fluid-attenuated inversion recovery signal in temporal lobe epilepsy.

OBJECTIVE: In drug-resistant temporal lobe epilepsy (TLE), relative to the large number of whole-brain morphological studies, neocortical T2 changes have not been systematically investigated. The aim of this study was to assess the anatomical principles that govern the distribution of neocortical T2-weighted fluid-attenuated inversion recovery (FLAIR) signal intensity and uncover its topographic principles. METHODS: Using a surface-based sampling scheme, we mapped neocortical FLAIR intensity of 61 TLE patients relative to 38 healthy controls imaged at 3 T. To address topographic principles of the susceptibility to FLAIR signal changes in TLE, we assessed associations with normative data on tissue composition using 2 complementary approaches. First, we evaluated whether the degree of TLE-related FLAIR intensity changes differed across cytoarchitectonic classes as defined by Von Economo-Koskinas taxonomy. Second, as a proxy to map regions with similar intracortical composition, we carried out a FLAIR intensity covariance paradigm in controls by seeding systematically from all cortical regions, and identified those networks that were the best spatial predictors of the between-group FLAIR changes. RESULTS: Increased intensities were observed in bilateral limbic and paralimbic cortices (hippocampus, parahippocampus, cingulate, temporopolar, insular, orbitofrontal). Effect sizes were highest in periallocortical limbic and insular classes as defined by the Von Economo-Koskinas cytoarchitectonic taxonomy. Furthermore, systematic FLAIR intensity covariance analysis in healthy controls revealed that similarity patterns characteristic of limbic cortices, most notably the hippocampus, served as sensitive predictors for the topography of FLAIR hypersignal in patients. FLAIR intensity findings were robust against correction for morphological confounds. Patients with a history of febrile convulsions showed more marked signal changes in parahippocampal and retrosplenial cortices, known to be strongly connected to the hippocampus. SIGNIFICANCE: FLAIR intensity mapping and covariance analysis provide a model of TLE gray matter pathology based on shared vulnerability of periallocortical and limbic cortices.