Potential merits and flaws of large language models in epilepsy care: A critical review.

The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intelligence (AI) models and how LLMs can be of future use in the daily care of people with epilepsy. Considering the importance of clinical history taking in diagnosing and monitoring epilepsy-combined with the established use of electronic health records-a great potential exists to integrate LLMs in epilepsy care. We present the current available LLM studies in epilepsy. Furthermore, we highlight and compare the most commonly used LLMs and elaborate on how these models can be applied in epilepsy. We further discuss important drawbacks and risks of LLMs, and we provide recommendations for overcoming these limitations.

Transforming epilepsy research: A systematic review on natural language processing applications.

Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This wealth of information is typically stored in electronic health records and accumulated in medical journals in an unstructured manner, thereby restricting complete utilization in clinical decision-making. To this end, clinical researchers increasing apply natural language processing (NLP)-a branch of artificial intelligence-as it removes ambiguity, derives context, and imbues standardized meaning from free-narrative clinical texts. This systematic review presents an overview of the current NLP applications in epilepsy and discusses the opportunities and drawbacks of NLP alongside its future implications. We searched the PubMed and Embase databases with a "natural language processing" and "epilepsy" query (March 4, 2022) and included original research articles describing the application of NLP techniques for textual analysis in epilepsy. Twenty-six studies were included. Fifty-eight percent of these studies used NLP to classify clinical records into predefined categories, improving patient identification and treatment decisions. Other applications of NLP had structured clinical information retrieval from electronic health records, scientific papers, and online posts of patients. Challenges and opportunities of NLP applications for enhancing epilepsy care and research are discussed. The field could further benefit from NLP by replicating successes in other health care domains, such as NLP-aided quality evaluation for clinical decision-making, outcome prediction, and clinical record summarization.

Translation and validation of an epilepsy-screening questionnaire in three Nigerian languages.

OBJECTIVE: We describe the development, translation and validation of epilepsy-screening questionnaires in the three most popular Nigerian languages: Hausa, Igbo and Yoruba. METHODS: A 9-item epilepsy-screening questionnaire was developed by modifying previously validated English language questionnaires. Separate multilingual experts forward- and back-translated them to the three target languages. Translations were discussed with fieldworkers and community members for ethnolinguistic acceptability and comprehension. We used an unmatched affected-case versus unaffected-control design for the pilot study. Cases were people with epilepsy attending the tertiary hospitals where these languages are spoken. The controls were relatives of cases or people attending for other medical conditions. An affirmative response to any of the nine questions amounted to a positive screen for epilepsy. RESULTS: We recruited 153 (75 cases and 78 controls) people for the Hausa version, 106 (45 cases and 61 controls) for Igbo and 153 (66 cases and 87 controls) for the Yoruba. The sensitivity and specificity of the questionnaire were: Hausa (97.3% and 88.5%), Igbo (91.1% and 88.5%) and Yoruba (93.9% and 86.7%). The three versions reliably indicated epilepsy with positive predictive values of 85.9% (Hausa), 85.4% (Igbo) and 87.3% (Yoruba) and reliably excluded epilepsy with negative predictive values of 97.1% (Hausa), 93.1% (Igbo) and 95.1% (Yoruba). Positive likelihood ratios were all greater than one. CONCLUSIONS: Validated epilepsy screening questionnaires are now available for the three languages to be used for community-based epilepsy survey in Nigeria. The translation and validation process are discussed to facilitate usage and development for other languages in sub-Saharan Africa.

Increased gamma and decreased fast ripple connections of epileptic tissue: A high-frequency directed network approach.

OBJECTIVE: New insights into high-frequency electroencephalographic activity and network analysis provide potential tools to improve delineation of epileptic tissue and increase the chance of postoperative seizure freedom. Based on our observation of high-frequency oscillations "spreading outward" from the epileptic source, we hypothesize that measures of directed connectivity in the high-frequency range distinguish epileptic from healthy brain tissue. METHODS: We retrospectively selected refractory epilepsy patients with a malformation of cortical development or tumor World Health Organization grade I/II who underwent epilepsy surgery with intraoperative electrocorticography for tailoring the resection based on spikes. We assessed directed functional connectivity in the theta (4-8 Hz), gamma (30-80 Hz), ripple (80-250 Hz), and fast ripple (FR; 250-500 Hz) bands using the short-time direct directed transfer function, and calculated the total, incoming, and outgoing propagation strength for each electrode. We compared network measures of electrodes covering the resected and nonresected areas separately for patients with good and poor outcome, and of electrodes with and without spikes, ripples, and FRs (group level: paired t test; patient level: Mann-Whitney U test). We selected the measure that could best identify the resected area and channels with epileptic events using the area under the receiver operating characteristic curve, and calculated the positive and negative predictive value, sensitivity, and specificity. RESULTS: We found higher total and outstrength in the ripple and gamma bands in resected tissue in patients with good outcome (rippletotal : P = .01; rippleout : P = .04; gammatotal : P = .01; gammaout : P = .01). Channels with events showed lower total and instrength, and higher outstrength in the FR band, and higher total and outstrength in the ripple, gamma, and theta bands (FRtotal : P = .05; FRin : P < .01; FRout : P = .02; gammatotal : P < .01; gammain : P = .01; gammaout : P < .01; thetatotal : P = .01; thetaout : P = .01). The total strength in the gamma band was most distinctive at the channel level (positive predictive value [PPV]good  = 74%, PPVpoor  = 43%). SIGNIFICANCE: Interictally, epileptic tissue is isolated in the FR band and acts as a driver up to the (fast) ripple frequency range. The gamma band total strength seems promising to delineate epileptic tissue intraoperatively.

The relation between cortisol and functional connectivity in people with and without stress-sensitive epilepsy.

OBJECTIVE: The most common reported seizure-precipitant is stress. We recently showed a biologic basis for stress sensitivity of seizures: cortisol levels in people with stress-sensitive epilepsy correlated with focal interictal epileptiform discharges (IEDs) on electroencephalography (EEG). Here we aimed to determine whether the effect of cortisol on the epileptic brain is global or focal, and whether cortisol affects all brains or just those of stress-sensitive people. Because epilepsy is associated with changes in functional brain connectivity, we studied the relationship between cortisol and changes in global and focal (node-centered) functional connectivity measures for individuals with stress-sensitive and non-stress-sensitive epilepsy. METHODS: Seventeen people with epilepsy underwent long-term (>24 h) EEG recording. During the first 5 h after waking, saliva was collected every 15 min for cortisol measurements. Theta-band functional connectivity was assessed for every 15 min of the recording. We calculated the average phase-lag index (PLI) between all channels as a measure of global functional connectivity. We used network Strength, the averaged PLI per channel, as focal functional connectivity measure. We correlated cortisol, global, and focal functional connectivity (Strength) with IED frequency using linear mixed models. Analyses were split for people with and without stress-sensitivity of seizures. RESULTS: Cortisol was negatively correlated with global functional connectivity in people with stress-sensitive seizures (estimate -0.0020; P < .01), whereas not in those without stress-sensitivity (estimate -0.0003; P = .46). This relationship occurred irrespective of the presence of IEDs on a channel (channels without IEDs and stress-sensitivity: estimate -0.0019; P < .01, non-stress-sensitive -0.0003; P = .41). Global and focal functional connectivity were negatively correlated with IED frequency, irrespective of stress sensitivity of seizures or channel type. SIGNIFICANCE: People with stress-sensitive epilepsy have a whole-brain neuronal response to cortisol that is different from that of people with non-stress-sensitive epilepsy. This offers a basis for understanding seizure genesis in stress-sensitive epilepsy, which might require a different treatment approach.

Community-based rehabilitation offers cost-effective epilepsy treatment in rural Guinea-Bissau.

Treatment of epilepsy in low-income countries is a challenge considering the lack of resources, availability of antiepileptic drugs, and cultural beliefs. We used a community-based rehabilitation (CBR) service for the detection, monitoring, and treatment of epilepsy. A local network of trained community volunteers provided education, good quality antiepileptic drugs, and clinical follow-up for people with epilepsy (PWE). In a period of 2years, approximately 22,500 people were screened in central Guinea-Bissau, and 112 PWE were identified and registered. Monthly check-ups were offered to monitor treatment effect and increase compliance. Retrospective analysis on 81 records of patients under treatment in June 2016 showed a decrease of seizure frequency in 88.8% after treatment initiation and was maintained throughout the clinical follow-up of 15months. A conservative estimation of the treatment and monitoring of a single person with epilepsy revealed a daily cost of $0.73. Despite acknowledging epilepsy as a neglected condition by the World Health Organization (WHO), most PWE still lack appropriate treatment. Although CBR service has been suggested as efficient strategy to reduce the treatment gap, little information is available on the efficacy of the programs. Our experiences show that CBR service is a cost-effective approach to monitor treatment and increase compliance in PWE. This experience may be of value for other resource-poor settings.

Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices: the interplay of density, connectivity cost and life-time trajectory.

The organizational network changes in the human brain across the lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in the elderly severely compromises the usefulness of network analysis to study the aging brain. We successfully circumvented this problem by focusing on the critical structural network backbone, using a robust tree representation. Whole-brain networks' minimum spanning trees were determined in a dataset of diffusion-weighted images from 382 healthy subjects, ranging in age from 20.2 to 86.2 years. Tree-based metrics were compared with classical network metrics. In contrast to the tree-based metrics, classical metrics were highly influenced by age-related changes in network density. Tree-based metrics showed linear and non-linear correlation across adulthood and are in close accordance with results from previous histopathological characterizations of the changes in white matter integrity in the aging brain.

Increased power of resting-state gamma oscillations in autism spectrum disorder detected by routine electroencephalography.

Experimental studies suggest that increased resting-state power of gamma oscillations is associated with autism spectrum disorder (ASD). To extend the clinical applicability of this finding, we retrospectively investigated routine electroencephalography (EEG) recordings of 19 patients with ASD and 19 age- and gender-matched controls. Relative resting-state condition gamma spectral power was variable, but on average significantly increased in children with ASD. This effect remained when excluding electrodes associated with myogenic gamma activity. These findings further indicate that increased resting-state gamma activity characterizes a subset of ASD and may also be detected by routine EEG as a clinically accessible and well-tolerated investigation.

Does Saint Nicholas provoke seizures? Hints from Google Trends.

PURPOSE: Stress is the most often reported seizure-precipitant in epilepsy. As most evidence for the relation between stress and epilepsy is derived from human self-reports, observational studies including a larger part of the population could provide additional proof. A stressor often reported to increase seizure frequency in children with epilepsy in the Netherlands is the national celebration of Saint Nicholas' eve (December 5) and the weeks before; this is the main period of festivities for children in this country. To study the relation between stress and epilepsy, we analyzed epilepsy information-seeking behavior on the Internet, an indirect measure of seizure frequency, around this national children's celebration. METHODS: Google Trends was used to extract relative search percentages for 'epilepsy' on Google in the Netherlands, the United States, and the United Kingdom between 2004 and 2013. Relative search percentages during the Saint Nicholas period were compared with baseline. RESULTS: Epilepsy searches increased by 14% in the Saint Nicholas period compared with baseline (p<0.001). This effect was not found for searches performed in the same period in the United States or the United Kingdom, countries where this holiday is not celebrated. CONCLUSIONS: The increase in epilepsy information-seeking behavior in the Saint Nicholas period is possibly caused by an increased occurrence of epileptic seizures. This underscores the potential of health information-seeking behavior on the Internet to answer clinically relevant research questions and provides circumstantial evidence for a relation between stress and the occurrence of epileptic seizures.

Clinical characteristics and diagnoses of 1213 children referred to a first seizure clinic.

OBJECTIVE: New-onset seizure-like events (SLEs) are common in children, but differentiating between epilepsy and its mimics is challenging. This study provides an overview of the clinical characteristics, diagnoses, and corresponding etiologies of children evaluated at a first seizure clinic (FSC), which will be helpful for all physicians involved in the care of children with SLEs. METHODS: We included 1213 children who were referred to the FSC of a Dutch tertiary children's hospital over a 13-year period and described their clinical characteristics, first routine EEG recording results, and the distribution and specification of their eventual epilepsy and non-epilepsy diagnoses. The time interval to correct diagnosis and the diagnostic accuracy of the FSC were evaluated. RESULTS: "Epilepsy" was eventually diagnosed in 407 children (33.5%), "no epilepsy" in 737 (60.8%), and the diagnosis remained "unclear" in 69 (5.7%). Epileptiform abnormalities were seen in 60.9% of the EEG recordings in the "epilepsy" group, and in 5.7% and 11.6% of the "no epilepsy" and "unclear" group, respectively. Of all children with final "epilepsy" and "no epilepsy" diagnoses, 68.6% already received their diagnosis at FSC consultation, and 2.9% of the children were initially misdiagnosed. The mean time to final diagnosis was 2.0 months, and 91.3% of all children received their final diagnosis within 12 months after the FSC consultation. SIGNIFICANCE: We describe the largest pediatric FSC cohort to date, which can serve as a clinical frame of reference. The experience and expertise built at FSCs will improve and accelerate diagnosis in children with SLEs. PLAIN LANGUAGE SUMMARY: Many children experience events that resemble but not necessarily are seizures. Distinguishing between seizures and seizure mimics is important but challenging. Specialized first-seizure clinics can help with this. Here, we report data from 1213 children who were referred to the first seizure clinic of a Dutch children's hospital. One-third of them were diagnosed with epilepsy. In 68.8% of all children-with and without epilepsy-the diagnosis was made during the first consultation. Less than 3% were misdiagnosed. This study may help physicians in what to expect regarding the diagnoses in children who present with events that resemble seizures.

Are high frequency oscillations associated with altered network topology in partial epilepsy?

Neurophysiological studies have reported functional network alterations in epilepsy, most consistently in the theta frequency band. Highly interconnected brain regions (so-called 'hubs') seem to be important in these epileptic networks. High frequency oscillations (HFOs) in intracranial EEG recordings are recently discovered biomarkers that can identify the epileptogenic area and are thought to result from altered neuronal interactions. We studied whether the epileptogenic zone (identified by HFOs and seizure onset zone) is associated with pathological hubs. Bilateral depth electrode recordings from the hippocampus and amygdala were available from twelve patients suspected of temporal lobe epilepsy. HFOs, classified as ripples (80-250 Hz) and fast ripples (250-500 Hz), and epileptiform spikes were marked for all patients in a five-minute epoch of slow-wave sleep. For each channel, we computed hub-measures from a period without epileptiform spikes and found that the epileptogenic zone was associated with a decreased hub-value in the theta frequency band. The amount of HFOs, especially fast ripples, was negatively correlated with the hub-value per channel. Results from post-hoc analyses of other frequency bands, particularly the broad- and gamma frequency band, pointed in the same direction as the results for the theta frequency band. These findings suggest a pathological functional 'isolation' of the epileptogenic zone in the interictal state.

Functional and structural brain networks in epilepsy: what have we learned?

Brain functioning is increasingly seen as a complex interplay of dynamic neural systems that rely on the integrity of structural and functional networks. Recent studies that have investigated functional and structural networks in epilepsy have revealed specific disruptions in connectivity and network topology and, consequently, have led to a shift from "focus" to "networks" in modern epilepsy research. Disruptions in these networks may be associated with cognitive and behavioral impairments often seen in patients with chronic epilepsy. In this review, we aim to provide an overview that would introduce the clinical neurologist and epileptologist to this new theoretical paradigm. We focus on the application of a theory, called "network analysis," to characterize resting-state functional and structural networks and discuss current and future clinical applications of network analysis in patients with epilepsy.

Web-search trends shed light on the nature of lunacy: relationship between moon phases and epilepsy information-seeking behavior.

In old and modern times and across cultures, recurrent seizures have been attributed to the lunar phase. It is unclear whether this relationship should be classified as a myth or whether a true connection exists between moon phases and seizures. We analyzed the worldwide aggregated search queries related to epilepsy health-seeking behavior between 2005 and 2012. Epilepsy-related Internet searches increased in periods with a high moon illumination. The overall association was weak (r=0.11, 95% confidence interval: 0.07 to 0.14) but seems to be higher than most control search queries not related to epilepsy. Increased sleep deprivation during periods of full moon might explain this positive association and warrants further study into epilepsy-related health-seeking behavior on the Internet, the lunar phase, and its contribution to nocturnal luminance.

The importance of discriminative power rather than significance when evaluating potential clinical biomarkers in epilepsy research.

OBJECTIVE: The quest for epilepsy biomarkers is on the rise. Variables with statistically significant group-level differences are often misinterpreted as biomarkers with sufficient discriminative power. This study aimed to demonstrate the relationship between significant group-level differences and a variable's power to discriminate between individuals. METHODS: We simulated normal-distributed datasets from hypothetical populations with varying sample sizes (25-800), effect sizes (Cohen's d: .25-2.50), and variability (standard deviation: 10-35) to assess the impact of these parameters on significance and discriminative power. The simulation data were illustrated by assessing the discriminative power of a potential real-case biomarker-the EEG beta band power-to diagnose generalized epilepsy, using data from 66 children with generalized epilepsy and 385 controls. Additionally, we evaluated recently reported epilepsy biomarkers by comparing their effect sizes to our simulation-derived effect size criterion. RESULTS: Group size affects significance but not discriminative power. Discriminative power is much more related to variability and effect size. Our real data example supported these simulation results by demonstrating that group-level significance does not translate, one to one, into discriminative power. Although we found a significant difference in the beta band power between children with and without epilepsy, the discriminative power was poor due to a small effect size. A Cohen's d of at least 1.25 is required to reach good discriminative power in univariable prediction modeling. Slightly over 60% of the biomarkers in our literature search met this criterion. SIGNIFICANCE: Rather than statistical significance of group-level differences, effect size should be used as an indicator of a variable's biomarker potential. The minimal required effects size for individual biomarkers-a Cohen's d of 1.25-is large. This calls for multivariable approaches, in which combining multiple variables with smaller effect sizes could increase the overall effect size and discriminative power.

An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth.

Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95 % confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies.

Intraoperative electrocorticography using high-frequency oscillations or spikes to tailor epilepsy surgery in the Netherlands (the HFO trial): a randomised, single-blind, adaptive non-inferiority trial.

BACKGROUND: Intraoperative electrocorticography is used to tailor epilepsy surgery by analysing interictal spikes or spike patterns that can delineate epileptogenic tissue. High-frequency oscillations (HFOs) on intraoperative electrocorticography have been proposed as a new biomarker of epileptogenic tissue, with higher specificity than spikes. We prospectively tested the non-inferiority of HFO-guided tailoring of epilepsy surgery to spike-guided tailoring on seizure freedom at 1 year. METHODS: The HFO trial was a randomised, single-blind, adaptive non-inferiority trial at an epilepsy surgery centre (UMC Utrecht) in the Netherlands. We recruited children and adults (no age limits) who had been referred for intraoperative electrocorticography-tailored epilepsy surgery. Participants were randomly allocated (1:1) to either HFO-guided or spike-guided tailoring, using an online randomisation scheme with permuted blocks generated by an independent data manager, stratified by epilepsy type. Treatment allocation was masked to participants and clinicians who documented seizure outcome, but not to the study team or neurosurgeon. Ictiform spike patterns were always considered in surgical decision making. The primary endpoint was seizure outcome after 1 year (dichotomised as seizure freedom [defined as Engel 1A-B] vs seizure recurrence [Engel 1C-4]). We predefined a non-inferiority margin of 10% risk difference. Analysis was by intention to treat, with prespecified subgroup analyses by epilepsy type and for confounders. This completed trial is registered with the Dutch Trial Register, Toetsingonline ABR.NL44527.041.13, and ClinicalTrials.gov, NCT02207673. FINDINGS: Between Oct 10, 2014, and Jan 31, 2020, 78 individuals were enrolled to the study and randomly assigned (39 to HFO-guided tailoring and 39 to spike-guided tailoring). There was no loss to follow-up. Seizure freedom at 1 year occurred in 26 (67%) of 39 participants in the HFO-guided group and 35 (90%) of 39 in the spike-guided group (risk difference -23·5%, 90% CI -39·1 to -7·9; for the 48 patients with temporal lobe epilepsy, the risk difference was -25·5%, -45·1 to -6·0, and for the 30 patients with extratemporal lobe epilepsy it was -20·3%, -46·0 to 5·4). Pathology associated with poor prognosis was identified as a confounding factor, with an adjusted risk difference of -7·9% (90% CI -20·7 to 4·9; adjusted risk difference -12·5%, -31·0 to 5·9, for temporal lobe epilepsy and 5·8%, -7·7 to 19·5, for extratemporal lobe epilepsy). We recorded eight serious adverse events (five in the HFO-guided group and three in the spike-guided group) requiring hospitalisation. No patients died. INTERPRETATION: HFO-guided tailoring of epilepsy surgery was not non-inferior to spike-guided tailoring on intraoperative electrocorticography. After adjustment for confounders, HFOs show non-inferiority in extratemporal lobe epilepsy. This trial challenges the clinical value of HFOs as an epilepsy biomarker, especially in temporal lobe epilepsy. Further research is needed to establish whether HFO-guided intraoperative electrocorticography holds promise in extratemporal lobe epilepsy. FUNDING: UMCU Alexandre Suerman, EpilepsieNL, RMI Talent Fellowship, European Research Council, and MING Fund.

Epidemiology of Epilepsy in Nigeria: A Community-Based Study From 3 Sites.

BACKGROUND: We determined the prevalence, incidence, and risk factors for epilepsy in Nigeria. METHODS: We conducted a door-to-door survey to identify cases of epilepsy in 3 regions. We estimated age-standardized prevalence adjusted for nonresponse and sensitivity and the 1-year retrospective incidence for active epilepsy. To assess potential risk factors, we conducted a case-control study by collecting sociodemographic and risk factor data. We estimated odds ratios using logistic regression analysis and corresponding population attributable fractions (PAFs). RESULTS: We screened 42,427 persons (age ≥6 years), of whom 254 had confirmed active epilepsy. The pooled prevalence of active epilepsy per 1,000 was 9.8 (95% confidence interval [CI] 8.6-11.1), 17.7 (14.2-20.6) in Gwandu, 4.8 (3.4-6.6) in Afikpo, and 3.3 (2.0-5.1) in Ijebu-Jesa. The pooled incidence per 100,000 was 101.3 (95% CI 57.9-167.6), 201.2 (105.0-358.9) in Gwandu, 27.6 (3.3-128.0) in Afikpo, and 23.9 (3.2-157.0) in Ijebu-Jesa. Children's significant risk factors included febrile seizures, meningitis, poor perinatal care, open defecation, measles, and family history in first-degree relatives. In adults, head injury, poor perinatal care, febrile seizures, family history in second-degree relatives, and consanguinity were significant. Gwandu had more significant risk factors. The PAF for the important factors in children was 74.0% (71.0%-76.0%) and in adults was 79.0% (75.0%-81.0%). CONCLUSION: This work suggests varied epidemiologic numbers, which may be explained by differences in risk factors and population structure in the different regions. These variations should differentially determine and drive prevention and health care responses.

The power of language: Functional brain network topology of deaf and hearing in relation to sign language experience.

Prolonged auditory sensory deprivation leads to brain reorganization. This is indicated by functional enhancement in remaining sensory systems and known as cross-modal plasticity. In this study we investigated differences in functional brain network topology between deaf and hearing individuals. We also studied altered functional network responses between deaf and hearing individuals with a recording paradigm containing an eyes-closed and eyes-open condition. Electroencephalography activity was recorded in a group of sign language-trained deaf (N = 71) and hearing people (N = 122) living in rural Africa. Functional brain networks were constructed from the functional connectivity between fourteen electrodes distributed over the scalp. Functional connectivity was quantified with the phase lag index based on bandpass filtered epochs of brain signal. We studied the functional connectivity between the auditory, somatosensory and visual cortex and performed whole-brain minimum spanning tree analysis to capture network backbone characteristics. Functional connectivity between different regions involved in sensory information processing tended to be stronger in deaf people during the eyes-closed condition in both the alpha and beta frequency band. Furthermore, we found differences in functional backbone topology between deaf and hearing individuals. The backbone topology altered during transition from the eyes-closed to eyes-open condition irrespective of deafness, but was more pronounced in deaf individuals. The transition of backbone strength was different between individuals with congenital, pre-lingual or post-lingual deafness. Functional backbone characteristics correlated with the experience of sign language. Overall, our study revealed more insights in functional network reorganization caused by auditory deprivation and cross-modal plasticity. It further supports the idea of a brain plasticity potential in deaf and hearing people. The association between network organization and acquired sign language experience reflects the ability of ongoing brain adaptation in people with hearing disabilities.

A Prediction Model to Determine Childhood Epilepsy After 1 or More Paroxysmal Events.

OBJECTIVES: The clinical profile of children who had possible seizures is heterogeneous, and accuracy of diagnostic testing is limited. We aimed to develop and validate a prediction model that determines the risk of childhood epilepsy by combining available information at first consultation. METHODS: We retrospectively collected data of 451 children who visited our outpatient department for diagnostic workup related to 1 or more paroxysmal event(s). At least 1 year of follow-up was available for all children who were diagnosed with epilepsy or in whom diagnosis remained inconclusive. Clinical characteristics (sex, age of first seizure, event description, medical history) and EEG report were used as predictor variables for building a multivariate logistic regression model. Performance was validated in an external cohort (n = 187). RESULTS: Model discrimination was excellent, with an area under the receiver operating characteristic curve of 0.86 (95% confidence interval [CI]; 0.80-0.92), a positive predictive value of 0.93 (95% CI 0.83-0.97) and a negative predictive value of 0.76 (95% CI 0.70-0.80). Model discrimination in a selective subpopulation of children with uncertain diagnosis after initial clinical workup was good, with an area under the receiver operating characteristic curve of 0.73 (95% CI 0.58-0.87). CONCLUSIONS: This model may prove to be valuable because predictor variables together with a first interictal EEG can be available at first consultation. A Web application is provided (http://epilepsypredictiontools.info/first-consultation) to facilitate the diagnostic process for clinicians who are confronted with children with paroxysmal events, suspected of having an epileptic origin.

Electroencephalography based functional networks in newly diagnosed childhood epilepsies.

OBJECTIVE: It remains unclear to what extent brain networks are altered at an early stage of epilepsy, which may be important to improve our understanding on the course of network alterations and their association with recurrent seizures and cognitive deficits. METHODS: 89 Drug-naïve children with newly diagnosed focal or generalized epilepsies and 179 controls were included. Brain networks were based on interictal electroencephalography recordings obtained at first consultation. Conventional network metrics and minimum spanning tree (MST) metrics were computed to characterize topological network differences, such integration and segregation and a hub measures (betweenness centrality). RESULTS: Network alterations between groups were only identified by MST metrics and most pronounced in the delta band, in which a loss of network integration and a significant lower betweenness centrality was found in children with focal epilepsies compared to healthy controls (p<0.01). A reversed group difference was found in the upper alpha band. The network topology in generalized epilepsies was relatively spared. CONCLUSIONS: Interictal network alterations - only identifiable with the MST method - are already present at an early stage of focal epilepsy. SIGNIFICANCE: We argue that these alterations are subtle at the early stage and aggravate later as a result of persisting seizures.