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 might 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 neuropsychological 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 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 treatment at 1-year follow-up showed an 8- 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 with children who were seizure free and still receiving antiseizure medication. In conclusion, by providing seizure freedom and the opportunity for antiseizure medication cessation, epilepsy surgery might 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, to prevent it from occurring in the first place, children with focal epilepsy should be considered for epilepsy surgery as early as possible after diagnosis.

Global dynamics of neural mass models.

Neural mass models are used to simulate cortical dynamics and to explain the electrical and magnetic fields measured using electro- and magnetoencephalography. Simulations evince a complex phase-space structure for these kinds of models; including stationary points and limit cycles and the possibility for bifurcations and transitions among different modes of activity. This complexity allows neural mass models to describe the itinerant features of brain dynamics. However, expressive, nonlinear neural mass models are often difficult to fit to empirical data without additional simplifying assumptions: e.g., that the system can be modelled as linear perturbations around a fixed point. In this study we offer a mathematical analysis of neural mass models, specifically the canonical microcircuit model, providing analytical solutions describing slow changes in the type of cortical activity, i.e. dynamical itinerancy. We derive a perturbation analysis up to second order of the phase flow, together with adiabatic approximations. This allows us to describe amplitude modulations in a relatively simple mathematical format providing analytic proof-of-principle for the existence of semi-stable states of cortical dynamics at the scale of a cortical column. This work allows for model inversion of neural mass models, not only around fixed points, but over regions of phase space that encompass transitions among semi or multi-stable states of oscillatory activity. Crucially, these theoretical results speak to model inversion in the context of multiple semi-stable brain states, such as the transition between interictal, pre-ictal and ictal activity in epilepsy.

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.

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.

Noise induced quiescence of epileptic spike generation in patients with epilepsy.

Clinical scalp electroencephalographic recordings from patients with epilepsy are distinguished by the presence of epileptic discharges i.e. spikes or sharp waves. These often occur randomly on a background of fluctuating potentials. The spike rate varies between different brain states (sleep and awake) and patients. Epileptogenic tissue and regions near these often show increased spike rates in comparison to other cortical regions. Several studies have shown a relation between spike rate and background activity although the underlying reason for this is still poorly understood. Both these processes, spike occurrence and background activity show evidence of being at least partly stochastic processes. In this study we show that epileptic discharges seen on scalp electroencephalographic recordings and background activity are driven at least partly by a common biological noise. Furthermore, our results indicate noise induced quiescence of spike generation which, in analogy with computational models of spiking, indicate spikes to be generated by transitions between semi-stable states of the brain, similar to the generation of epileptic seizure activity. The deepened physiological understanding of spike generation in epilepsy that this study provides could be useful in the electrophysiological assessment of different therapies for epilepsy including the effect of different drugs or electrical stimulation.

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.

Post-stroke epilepsy in an ischemic stroke cohort-Incidence and diagnosis.

OBJECTIVES: Stroke is a common cause of adult-onset epilepsy (post-stroke epilepsy, PSE). Despite an increasing awareness, there is a concern for underdiagnosis of the condition. We aimed to study the adherence to the latest updated epilepsy definition, as well as the incidence and diagnosis of PSE in an ischemic stroke cohort admitted to a tertiary University Hospital. MATERIALS AND METHODS: We retrospectively investigated the occurrence and diagnosis of unprovoked seizures and PSE in all ischemic stroke patients admitted to Karolinska University Hospital in Stockholm during 2015 and registered in the Swedish Stroke Register. Patient records were scrutinized for the presence of post-stroke seizures/epilepsy. RESULTS: A total of 240 patients fulfilling the inclusion criteria were surveyed. Median follow-up time was 1062 days (IQR 589-1195 days). Thirteen patients were diagnosed with PSE according to the study criteria, the incidence of PSE 23/1000 person-years (95% CI 13-38/1000 person-years). Median time to PSE from stroke-onset was 237 days (IQR 33-688). Eleven of 13 PSE patients received an epilepsy diagnosis, eight patients after one unprovoked seizure, and three patients after two. CONCLUSIONS: The majority of PSE patients were given a correct epilepsy diagnosis and treated with antiepileptic drugs. However, this study suggests that there still is potential for improvement in the adherence to the latest updated epilepsy definition. The incidence of PSE in a Swedish ischemic stroke cohort using updated epilepsy definitions is similar to previous studies. Larger studies are needed to confirm our findings on the incidence of PSE.

Towards a Neuronal Gauge Theory.

Given the amount of knowledge and data accruing in the neurosciences, is it time to formulate a general principle for neuronal dynamics that holds at evolutionary, developmental, and perceptual timescales? In this paper, we propose that the brain (and other self-organised biological systems) can be characterised via the mathematical apparatus of a gauge theory. The picture that emerges from this approach suggests that any biological system (from a neuron to an organism) can be cast as resolving uncertainty about its external milieu, either by changing its internal states or its relationship to the environment. Using formal arguments, we show that a gauge theory for neuronal dynamics--based on approximate Bayesian inference--has the potential to shed new light on phenomena that have thus far eluded a formal description, such as attention and the link between action and perception.

The Cumulative Effects of Predictability on Synaptic Gain in the Auditory Processing Stream.

Stimulus predictability can lead to substantial modulations of brain activity, such as shifts in sustained magnetic field amplitude, measured with magnetoencephalography (MEG). Here, we provide a mechanistic explanation of these effects using MEG data acquired from healthy human volunteers (N = 13, 7 female). In a source-level analysis of induced responses, we established the effects of orthogonal predictability manipulations of rapid tone-pip sequences (namely, sequence regularity and alphabet size) along the auditory processing stream. In auditory cortex, regular sequences with smaller alphabets induced greater gamma activity. Furthermore, sequence regularity shifted induced activity in frontal regions toward higher frequencies. To model these effects in terms of the underlying neurophysiology, we used dynamic causal modeling for cross-spectral density and estimated slow fluctuations in neural (postsynaptic) gain. Using the model-based parameters, we accurately explain the sensor-level sustained field amplitude, demonstrating that slow changes in synaptic efficacy, combined with sustained sensory input, can result in profound and sustained effects on neural responses to predictable sensory streams.SIGNIFICANCE STATEMENT Brain activity can be strongly modulated by the predictability of stimuli it is currently processing. An example of such a modulation is a shift in sustained magnetic field amplitude, measured with magnetoencephalography. Here, we provide a mechanistic explanation of these effects. First, we establish the oscillatory neural correlates of independent predictability manipulations in hierarchically distinct areas of the auditory processing stream. Next, we use a biophysically realistic computational model to explain these effects in terms of the underlying neurophysiology. Finally, using the model-based parameters describing neural gain modulation, we can explain the previously unexplained effects observed at the sensor level. This demonstrates that slow modulations of synaptic gain can result in profound and sustained effects on neural activity.

Dynamic causal modelling of seizure activity in a rat model.

This paper presents a physiological account of seizure activity and its evolution over time using a rat model of induced epilepsy. We analyse spectral activity recorded in the hippocampi of three rats who received kainic acid injections in the right hippocampus. We use dynamic causal modelling of seizure activity and Bayesian model reduction to identify the key synaptic and connectivity parameters that underlie seizure onset. Using recent advances in hierarchical modelling (parametric empirical Bayes), we characterise seizure onset in terms of slow fluctuations in synaptic excitability of specific neuronal populations. Our results suggest differences in the pathophysiology - of seizure activity in the lesioned versus the non-lesioned hippocampus - with pronounced changes in excitation-inhibition balance and temporal summation on the lesioned side. In particular, our analyses suggest that marked reductions in the synaptic time constant of the deep pyramidal cells and the self-inhibition of inhibitory interneurons (in the lesioned hippocampus) are sufficient to explain changes in spectral activity. Although these synaptic changes are consistent over rats, the resulting electrophysiological phenotype can be quite diverse.

Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating.

Seizure activity in EEG recordings can persist for hours with seizure dynamics changing rapidly over time and space. To characterise the spatiotemporal evolution of seizure activity, large data sets often need to be analysed. Dynamic causal modelling (DCM) can be used to estimate the synaptic drivers of cortical dynamics during a seizure; however, the requisite (Bayesian) inversion procedure is computationally expensive. In this note, we describe a straightforward procedure, within the DCM framework, that provides efficient inversion of seizure activity measured with non-invasive and invasive physiological recordings; namely, EEG/ECoG. We describe the theoretical background behind a Bayesian belief updating scheme for DCM. The scheme is tested on simulated and empirical seizure activity (recorded both invasively and non-invasively) and compared with standard Bayesian inversion. We show that the Bayesian belief updating scheme provides similar estimates of time-varying synaptic parameters, compared to standard schemes, indicating no significant qualitative change in accuracy. The difference in variance explained was small (less than 5%). The updating method was substantially more efficient, taking approximately 5-10min compared to approximately 1-2h. Moreover, the setup of the model under the updating scheme allows for a clear specification of how neuronal variables fluctuate over separable timescales. This method now allows us to investigate the effect of fast (neuronal) activity on slow fluctuations in (synaptic) parameters, paving a way forward to understand how seizure activity is generated.

Reference values for jitter recorded by concentric needle electrodes in healthy controls: A multicenter study.

INTRODUCTION: The aim of this study was to create reference values for jitter measured with concentric needle electrodes. METHODS: Operators worldwide contributed recordings from orbicularis oculi (OO), frontalis (FR), and extensor digitorum (ED) muscles in healthy controls. Criteria for acceptable signal quality were agreed upon in advance. Fifteen or 20 recordings of acceptable quality from each muscle were required for voluntary and electrical stimulation recordings, respectively. RESULTS: Recordings from 59 to 92 subjects were obtained for each muscle and activation type. Outlier limits for mean consecutive difference and individual jitter data for voluntary activation were: OO, 31 and 45 µs; FR, 28 and 38 µs; ED, 30 and 43 µs; and for electrical stimulation they were: OO, 27 and 36 µs; FR, 21 and 28 µs; ED, 24 and 35 µs. CONCLUSION: Reference jitter values from concentric needle electrode recordings were developed from signals of defined quality while seeking to avoid creating supernormal values.

Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling.

We characterised the pathophysiology of seizure onset in terms of slow fluctuations in synaptic efficacy using EEG in patients with anti-N-methyl-d-aspartate receptor (NMDA-R) encephalitis. EEG recordings were obtained from two female patients with anti-NMDA-R encephalitis with recurrent partial seizures (ages 19 and 31). Focal electrographic seizure activity was localised using an empirical Bayes beamformer. The spectral density of reconstructed source activity was then characterised with dynamic causal modelling (DCM). Eight models were compared for each patient, to evaluate the relative contribution of changes in intrinsic (excitatory and inhibitory) connectivity and endogenous afferent input. Bayesian model comparison established a role for changes in both excitatory and inhibitory connectivity during seizure activity (in addition to changes in the exogenous input). Seizures in both patients were associated with a sequence of changes in inhibitory and excitatory connectivity; a transient increase in inhibitory connectivity followed by a transient increase in excitatory connectivity and a final peak of excitatory-inhibitory balance at seizure offset. These systematic fluctuations in excitatory and inhibitory gain may be characteristic of (anti NMDA-R encephalitis) seizures. We present these results as a case study and replication to motivate analyses of larger patient cohorts, to see whether our findings generalise and further characterise the mechanisms of seizure activity in anti-NMDA-R encephalitis.

Modelling cortical network dynamics.

We have investigated the theoretical constraints of the interactions between coupled cortical columns. Each cortical column consists of a set of neural populations where each population is modelled as a neural mass. The existence of semi-stable states within a cortical column is dependent on the type of interaction between the neuronal populations, i.e., the form of the synaptic kernels. Current-to-current coupling has been shown, in contrast to potential-to-current coupling, to create semi-stable states within a cortical column. The interaction between semi-stable states of the cortical columns is studied where we derive the dynamics for the collected activity. For small excitations the dynamics follow the Kuramoto model; however, in contrast to previous work we derive coupled equations between phase and amplitude dynamics with the possibility of defining connectivity as a stationary and dynamic variable. The turbulent flow of phase dynamics which occurs in networks of Kuramoto oscillators would indicate turbulent changes in dynamic connectivity for coupled cortical columns which is something that has been recorded in epileptic seizures. We used the results we derived to estimate a seizure propagation model which allowed for inversions using the Laplace assumption (Dynamic Causal Modelling). The seizure propagation model was trialed on simulated data, and future work will investigate the estimation of the connectivity matrix from empirical data. This model can be used to predict changes in seizure evolution after virtual changes in the connectivity network, something that could be of clinical use when applied to epilepsy surgical cases.

Interictal epileptiform discharges in focal epilepsy are preceded by increase in low-frequency oscillations.

OBJECTIVE: Interictal epileptiform discharges (IEDs) constitute a diagnostic signature of epilepsy. These events reflect epileptogenic hypersynchronization. Previous studies indicated that IEDs arise from slow neuronal activation accompanied by metabolic and hemodynamic changes. These might induce cortical inhibition followed hypersynchronization at IED onset. As cortical inhibition is mediated by low-frequency oscillations, we aimed to analyze the role of low-frequency oscillations prior the IED using magnetencephalography (MEG). METHODS: Low-frequency (1-8 Hz) oscillations pre-IED ([-1000 milliseconds (ms), IED onset]) were analyzed using MEG in 14 focal epilepsy patients (median age = 23 years, range = 7-46 age). Occurrence of local pre-IED oscillations was analyzed using Beamformer Dynamical Imaging of Coherent Sources (DICS) and event-related desynchronization/synchronization (ERD-ERS) maps constructed using cluster-based permutation tests. The development of pre-IED oscillations was characterized using Hilbert transformation. RESULTS: All patients exhibited statistically significant increase in delta (1-4 Hz) and/or theta (4-8 Hz) oscillations pre-IED compared to baseline [-2000 ms, -1000 ms]. Furthermore, all patients exhibited low-frequency power increase up to IED onset. CONCLUSIONS: We demonstrated consistently occurring, low-frequency oscillations prior to IED onset. SIGNIFICANCE: As low-frequency activity mediates cortical inhibition, our study demonstrates that a focal inhibition precedes hypersynchronization at IED onset.

On the physiology of cognitive decline in type 1 diabetes.

OBJECTIVES: Type 1 diabetes mellitus (T1DM) may be associated with cognitive impairment and notably a decline in psychomotor speed, information processing speed and attention. The mechanism for this decline is uncertain. Previous studies by our group and others have demonstrated a decline in EEG-power and event-related potential amplitude in T1DM. The objectives of the present study were to explore whether 1) the association between event-related potential (N100) amplitude and psychomotor speed is different between T1DM and healthy subjects, and 2) the decline in N100 amplitude depends on duration of diabetes. METHODS: Patients with T1DM (N = 204) and healthy control subjects (N = 358) were included in a cross-sectional study. Event-related brain potentials were recorded with auditory reaction tasks. Psychomotor speed was evaluated with the Grooved Pegboard test in a subset of the patients (N = 70) and the healthy control subjects (N = 89). RESULTS: Patients with T1DM had a decrease in the N100 amplitude that correlated with a decline in psychomotor speed, longer duration of diabetes and increasing age. In healthy controls, the N100 amplitude did not decrease with age and the association between psychomotor speed and N100 amplitude was absent. CONCLUSION: The association between psychomotor speed and N100 amplitude is likely to be a specific trait for T1DM since it was not found in healthy controls and was dependent on diabetes duration. Our findings indicate that the pathogenesis of cognitive decline in T1DM may involve a disease-related factor with a long-term influence on the N100 amplitude.

Post-encephalitic epilepsy in childhood: results from a prospective cohort study.

To investigate whether it is possible to predict outcome of post-encephalitic epilepsy based on findings during the acute phase of disease. Children (28 days to 17 years) diagnosed with acute encephalitis at Karolinska University Hospital between 2011 and 2016 were included in this study (n=89). They were examined clinically, with repeated electroencephalographic examinations and analysis of cerebrospinal fluid during the acute illness. Thereafter, patients were followed up to 24 months and evaluated for post-encephalitic epilepsy. Variables determined during the acute illness were used to predict the development of post-encephalitic epilepsy: electroencephalographic parameters, cerebrospinal fluid parameters, aetiology and clinical parameters. Fisher's exact test was used to estimate any predictors of epilepsy among the acutely measured parameters. The prevalence of post-encephalitic epilepsy was 9% (n=8) at 24 months. Of these, 3/8 responded to monotherapy with antiepileptic drugs and 5/8 required two or more and 3/8 were medically refractory at 24 months. Presence of acute seizures during admission, epileptic activity on electroencephalographic recordings and new-onset structural lesions demonstrated a significant association with development of post-encephalitic epilepsy (p<0.03) with an odds ratio greater than 5. Using the three above-mentioned parameters, we designed an algorithm to predict cohorts of patients with increased risk of developing post-encephalitic epilepsy. Moreover, patients who developed post-encephalitic epilepsy had a longer duration of hospital admission and longer care in intensive care units in comparison to those who did not. This study demonstrates that the risk of developing post-encephalitic epilepsy was mainly seen among patients with acute seizures, epileptic encephalographic activity in the acute setting or new-onset structural lesions. A simple algorithm could be used to predict the risk of post-encephalitic epilepsy.

Mechanism of visual network dysfunction in relapsing-remitting multiple sclerosis and its relation to cognition.

OBJECTIVE: To investigate if changes in brain network function and connectivity contribute to the abnormalities in visual event related potentials (ERP) in relapsing-remitting multiple sclerosis (RRMS), and explore their relation to a decrease in cognitive performance. METHODS: We evaluated 72 patients with RRMS and 89 healthy control subjects in a cross-sectional study. Visual ERP were generated using illusory and non-illusory stimuli and recorded using 21 EEG scalp electrodes. The measured activity was modelled using Dynamic Causal Modelling. The model network consisted of 4 symmetric nodes including the primary visual cortex (V1/V2) and the Lateral Occipital Complex. Patients and controls were tested with a neuropsychological test battery consisting of 18 cognitive tests covering six cognitive domains. RESULTS: We found reduced cortical connectivity in bottom-up and interhemispheric connections to the right lateral occipital complex in patients (p < 0.001). Furthermore, interhemispherical connections were related to cognitive dysfunction in several domains (attention, executive function, visual perception and organization, processing speed and global cognition) for patients (p < 0.05). No relation was seen between cortical network connectivity and cognitive function in the healthy control subjects. CONCLUSION: Changes in the functional connectivity to higher cortical regions provide a neurobiological explanation for the changes of the visual ERP in RRMS. SIGNIFICANCE: This study suggests that changes in connectivity to higher cortical regions partly explain visual network dysfunction in RRMS where a lower interhemispheric connectivity may contribute to impaired cognitive function.

Integrating navigated transcranial magnetic stimulation motor mapping in hypofractionated and single-dose gamma knife radiosurgery: A two-patient case series and a review of literature.

BACKGROUND: The aim of the study was to demonstrate the feasibility of integrating navigated transcranial magnetic stimulation (nTMS) in preoperative gamma knife radiosurgery (GKRS) planning of motor eloquent brain tumors. CASE DESCRIPTION: The first case was a 53-year-old female patient with metastatic breast cancer who developed focal epileptic seizures and weakness of the left hand. The magnetic resonance imaging (MRI) scan demonstrated a 30 mm metastasis neighboring the right precentral gyrus and central sulcus. The lesion was treated with adaptive hypofractionated GKRS following preoperative nTMS-based motor mapping. Subsequent follow-up imaging (up to 12 months) revealed next to complete tumor ablation without toxicity. The second case involved a previously healthy 73-year-old male who similarly developed new left-handed weakness. A subsequent MRI demonstrated a 26 mm metastatic lesion, located in the right postcentral gyrus and 5 mm from the hand motor area. The extracranial screening revealed a likely primary lung adenocarcinoma. The patient underwent preoperative nTMS motor mapping prior to treatment. Perilesional edema was noted 6 months postradiosurgery; nevertheless, long- term tumor control was demonstrated. Both patients experienced motor function normalization shortly after treatment, continuing to final follow-up. CONCLUSION: Integrating preoperative nTMS motor mapping in treatment planning allowed us to reduce dose distributions to perilesional motor fibers while achieving salvage of motor function, lasting seizure freedom, and tumor control. These initial data along with our review of the available literature suggest that nTMS can be of significant assistance in brain radiosurgery. Prospective studies including larger number of patients are still warranted.

Prospective Evaluation of Childhood Encephalitis: Predisposing Factors, Prevention and Outcome.

BACKGROUND: Acute encephalitis in childhood is a serious condition. The severity varies between studies, partly reflecting differences in study design where only severe cases from referral centers often are reported. The aim of this study was to prospectively study the clinical picture and etiology of acute encephalitis in childhood at a primary and tertiary pediatric hospital in Sweden. METHODS: All children with acute encephalitis were prospectively included from 2011 to 2016. Laboratory tests, investigations and follow-up were performed according to standardized study protocols. RESULTS: Eighty-nine children were included (46 female and 43 male) with a median age of 53 months. An etiology was established in 61/89. Tick-borne encephalitis virus, enterovirus and rotavirus dominated and 34% were caused by a virus preventable by vaccination. Immune-mediated encephalitis was seen in 7 children. An abnormal electroencephalography picture was seen in 77/86, pathologic findings on neuroimaging in 13/49, and 38/89 children had seizures. Sequelae were reported by 49%. A high prevalence of previous contact with child and adolescent psychiatry was seen and, although not statistically significant, the need for extra support at school before encephalitis and the presence of central nervous system disease in the family seemed to predispose for a longer hospital stay. CONCLUSION: Encephalitis is a condition with long-term consequences. Most children need admission to hospital, and many need surveillance in the intensive care unit. The etiology can be determined in a majority of cases, and 1/3 could have been prevented by vaccination. This study corroborates electroencephalography as a cornerstone in diagnosis.