Short-Term Impact of Seizures and Mitigation Opportunities.

PURPOSE OF REVIEW: The burden of epilepsy is complex and consists of elements directly related to acute seizures as well as those associated with living with a chronic neurologic disorder. The purpose of this systematic review was to characterize short-term burdens of seizures and to explore the potential value of acute treatments to mitigate these burdens apart from reducing the risk of status epilepticus. RECENT FINDINGS: A systematic literature search was conducted using PubMed to identify articles published from January 1, 2017, to June 22, 2023, that described short-term burdens and acute treatments of seizures. Primary outcomes included those related to short-term burdens of seizures and the benefits of acute treatments to reduce short-term burdens. Of the 1332 articles identified through PubMed and 17 through other sources, 27 had relevant outcomes and were included in the qualitative synthesis. Seizure emergencies negatively affected short-term quality of life and the ability to conduct normal daily living activities and were associated with physical (injury) and financial (emergency transport, hospitalization) burdens. The use of acute treatment was associated with a rapid return (≤ 1 h) to normal function/self for both patients and caregivers and potentially lower healthcare utilization and costs. Seizure action plans may improve knowledge and comfort with seizure care, empowering patients and caregivers. The short-term burden of seizures can create a substantial negative impact on patients and caregivers. Acute treatments may reduce the short-term burdens of seizures in addition to their well-described role to reduce seizure activity and the risk for status epilepticus.

Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.

OBJECTIVE: To determine whether automated, electronic alerts increased referrals for epilepsy surgery. METHODS: We conducted a prospective, randomized controlled trial of a natural language processing-based clinical decision support system embedded in the electronic health record (EHR) at 14 pediatric neurology outpatient clinic sites. Children with epilepsy and at least two prior neurology visits were screened by the system prior to their scheduled visit. Patients classified as a potential surgical candidate were randomized 2:1 for their provider to receive an alert or standard of care (no alert). The primary outcome was referral for a neurosurgical evaluation. The likelihood of referral was estimated using a Cox proportional hazards regression model. RESULTS: Between April 2017 and April 2019, at total of 4858 children were screened by the system, and 284 (5.8%) were identified as potential surgical candidates. Two hundred four patients received an alert, and 96 patients received standard care. Median follow-up time was 24 months (range: 12-36 months). Compared to the control group, patients whose provider received an alert were more likely to be referred for a presurgical evaluation (3.1% vs 9.8%; adjusted hazard ratio [HR] = 3.21, 95% confidence interval [CI]: 0.95-10.8; one-sided p = .03). Nine patients (4.4%) in the alert group underwent epilepsy surgery, compared to none (0%) in the control group (one-sided p = .03). SIGNIFICANCE: Machine learning-based automated alerts may improve the utilization of referrals for epilepsy surgery evaluations.

Neuropsychological outcomes after epilepsy surgery: A comparison of stereo electroencephalography and subdural electrodes.

BACKGROUND AND PURPOSE: We analyzed the association of neuropsychological outcomes after epilepsy surgery with the intracranial electrode type (stereo electroencephalography [SEEG] and subdural electrodes [SDE]), and electrical stimulation mapping (ESM) of speech/language. METHODS: Drug-resistant epilepsy patients who underwent comprehensive neuropsychological evaluation before and 1 year after epilepsy surgery were included. SEEG and SDE subgroups were matched by age, handedness, operated hemisphere, and seizure freedom. Postsurgical neuropsychological outcomes (adjusted for presurgical scores) and reliable change indices were analyzed as functions of electrode type and ESM. RESULTS: Ninety-nine patients aged 6-29 years were included with similar surgical resection/ablation volumes in the SEEG and SDE subgroups. Most of the neuropsychological outcomes were comparable between SEEG and SDE subgroups; however, Working Memory and Processing Speed were significantly improved in the SEEG subgroup. Undergoing language ESM was associated with significant improvements in Spelling, Letter-Word Identification, Vocabulary, Verbal Comprehension, Verbal Learning, and Story Memory scores, but a decline in Calculation scores. CONCLUSIONS: Intracranial evaluations with SEEG and SDE are comparable in terms of long-term postsurgical neuropsychological outcomes. Our data suggest that SEEG may be associated with improvements in working memory and processing speed, representing cognitive domains served by spatially distributed networks. Our study also supports wider use of language ESM before epilepsy surgery, preferably using other language tasks in addition to visual naming. Rather than the type of electrode, postsurgical neuropsychological outcomes are driven by whether language ESM was performed or not, with beneficial effects of language mapping.

Supporting treatment adherence regimens in children with epilepsy: A randomized clinical trial.

OBJECTIVE: This study was undertaken to examine the efficacy of a family-tailored education and problem-solving behavioral intervention, Supporting Treatment Adherence Regimens (STAR), in young children (2-12 years old) with new onset epilepsy compared to an attention control (i.e., education only [EO]) intervention. Participants randomized to the STAR intervention were hypothesized to demonstrate significantly improved adherence at postintervention and 3-, 6-, and 12-month follow-up visits compared to the EO intervention. Seizure and health-related quality of life (HRQOL) outcomes were also examined. METHODS: Two hundred children with new onset epilepsy and their caregivers were recruited during routine epilepsy clinic visits. Baseline questionnaires were completed, and electronic adherence monitors were provided. Participants with adherence less than 95% during the run-in period were randomized to either STAR or EO intervention. Active intervention was provided to both groups for 4 months. Questionnaires were completed at conclusion of the active intervention phase and three follow-up time points (3, 6, and 12 months). Group differences in adherence, seizure outcomes, and HRQOL were examined using regression-based analyses of covariance and longitudinal mixed effect linear or logistical models. RESULTS: Adherence at 12-month follow-up was significantly different between the STAR (mean = 82.34, SD = 21.29) and EO intervention groups (mean = 61.77, SD = 28.29), with the STAR group demonstrating 20.6% greater adherence (b = 19.11, p = .04, 95% confidence interval = 1.00-37.22, d = .83). No significant differences were found between groups in seizure and HRQOL outcomes. SIGNIFICANCE: A family-based behavioral adherence intervention demonstrated sustained adherence improvements 1 year following epilepsy diagnosis compared to an epilepsy-specific education intervention. STAR is an efficacious adherence intervention that can easily be implemented into routine epilepsy care.

Clinical presentation of new onset refractory status epilepticus in children (the pSERG cohort).

OBJECTIVE: We aimed to characterize the clinical profile and outcomes of new onset refractory status epilepticus (NORSE) in children, and investigated the relationship between fever onset and status epilepticus (SE). METHODS: Patients with refractory SE (RSE) between June 1, 2011 and October 1, 2016 were prospectively enrolled in the pSERG (Pediatric Status Epilepticus Research Group) cohort. Cases meeting the definition of NORSE were classified as "NORSE of known etiology" or "NORSE of unknown etiology." Subgroup analysis of NORSE of unknown etiology was completed based on the presence and time of fever occurrence relative to RSE onset: fever at onset (≤24 h), previous fever (2 weeks-24 h), and without fever. RESULTS: Of 279 patients with RSE, 46 patients met the criteria for NORSE. The median age was 2.4 years, and 25 (54%) were female. Forty (87%) patients had NORSE of unknown etiology. Nineteen (48%) presented with fever at SE onset, 16 (40%) had a previous fever, and five (12%) had no fever. The patients with preceding fever had more prolonged SE and worse outcomes, and 25% recovered baseline neurological function. The patients with fever at onset were younger and had shorter SE episodes, and 89% recovered baseline function. SIGNIFICANCE: Among pediatric patients with RSE, 16% met diagnostic criteria for NORSE, including the subcategory of febrile infection-related epilepsy syndrome (FIRES). Pediatric NORSE cases may also overlap with refractory febrile SE (FSE). FIRES occurs more frequently in older children, the course is usually prolonged, and outcomes are worse, as compared to refractory FSE. Fever occurring more than 24 h before the onset of seizures differentiates a subgroup of NORSE patients with distinctive clinical characteristics and worse outcomes.

Factors associated with long-term outcomes in pediatric refractory status epilepticus.

OBJECTIVE: This study was undertaken to describe long-term clinical and developmental outcomes in pediatric refractory status epilepticus (RSE) and identify factors associated with new neurological deficits after RSE. METHODS: We performed retrospective analyses of prospectively collected observational data from June 2011 to March 2020 on pediatric patients with RSE. We analyzed clinical outcomes from at least 30 days after RSE and, in a subanalysis, we assessed developmental outcomes and evaluated risk factors in previously normally developed patients. RESULTS: Follow-up data on outcomes were available in 276 patients (56.5% males). The median (interquartile range [IQR]) follow-up duration was 1.6 (.9-2.7) years. The in-hospital mortality rate was 4% (16/403 patients), and 15 (5.4%) patients had died after hospital discharge. One hundred sixty-six (62.9%) patients had subsequent unprovoked seizures, and 44 (16.9%) patients had a repeated RSE episode. Among 116 patients with normal development before RSE, 42 of 107 (39.3%) patients with available data had new neurological deficits (cognitive, behavioral, or motor). Patients with new deficits had longer median (IQR) electroclinical RSE duration than patients without new deficits (10.3 [2.1-134.5] h vs. 4 [1.6-16] h, p = .011, adjusted odds ratio = 1.003, 95% confidence interval = 1.0008-1.0069, p = .027). The proportion of patients with an unfavorable functional outcome (Glasgow Outcome Scale-Extended score ≥ 4) was 22 of 90 (24.4%), and they were more likely to have received a continuous infusion. SIGNIFICANCE: About one third of patients without prior epilepsy developed recurrent unprovoked seizures after the RSE episode. In previously normally developing patients, 39% presented with new deficits during follow-up, with longer electroclinical RSE duration as a predictor.

Benzodiazepine administration patterns before escalation to second-line medications in pediatric refractory convulsive status epilepticus.

OBJECTIVE: This study was undertaken to evaluate benzodiazepine (BZD) administration patterns before transitioning to non-BZD antiseizure medication (ASM) in pediatric patients with refractory convulsive status epilepticus (rSE). METHODS: This retrospective multicenter study in the United States and Canada used prospectively collected observational data from children admitted with rSE between 2011 and 2020. Outcome variables were the number of BZDs given before the first non-BZD ASM, and the number of BZDs administered after 30 and 45 min from seizure onset and before escalating to non-BZD ASM. RESULTS: We included 293 patients with a median (interquartile range) age of 3.8 (1.3-9.3) years. Thirty-six percent received more than two BZDs before escalating, and the later the treatment initiation was after seizure onset, the less likely patients were to receive multiple BZD doses before transitioning (incidence rate ratio [IRR] = .998, 95% confidence interval [CI] = .997-.999 per minute, p = .01). Patients received BZDs beyond 30 and 45 min in 57.3% and 44.0% of cases, respectively. Patients with out-of-hospital seizure onset were more likely to receive more doses of BZDs beyond 30 min (IRR = 2.43, 95% CI = 1.73-3.46, p < .0001) and beyond 45 min (IRR = 3.75, 95% CI = 2.40-6.03, p < .0001) compared to patients with in-hospital seizure onset. Intermittent SE was a risk factor for more BZDs administered beyond 45 min compared to continuous SE (IRR = 1.44, 95% CI = 1.01-2.06, p = .04). Forty-seven percent of patients (n = 94) with out-of-hospital onset did not receive treatment before hospital arrival. Among patients with out-of-hospital onset who received at least two BZDs before hospital arrival (n = 54), 48.1% received additional BZDs at hospital arrival. SIGNIFICANCE: Failure to escalate from BZDs to non-BZD ASMs occurs mainly in out-of-hospital rSE onset. Delays in the implementation of medical guidelines may be reduced by initiating treatment before hospital arrival and facilitating a transition to non-BZD ASMs after two BZD doses during handoffs between prehospital and in-hospital settings.

Early identification of epilepsy surgery candidates: A multicenter, machine learning study.

OBJECTIVES: Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. Our objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery. MATERIALS & METHODS: In this multicenter, retrospective, longitudinal cohort study, ML algorithms were trained on n-grams extracted from free-text neurology notes, EEG and MRI reports, visit codes, medications, procedures, laboratories, and demographic information. Site-specific algorithms were developed at two epilepsy centers: one pediatric and one adult. Cases were defined as patients who underwent resective epilepsy surgery, and controls were patients with epilepsy with no history of surgery. The output of the ML algorithms was the estimated likelihood of candidacy for resective epilepsy surgery. Model performance was assessed using 10-fold cross-validation. RESULTS: There were 5880 children (n = 137 had surgery [2.3%]) and 7604 adults with epilepsy (n = 56 had surgery [0.7%]) included in the study. Pediatric surgical patients could be identified 2.0 years (range: 0-8.6 years) before beginning their presurgical evaluation with AUC =0.76 (95% CI: 0.70-0.82) and PR-AUC =0.13 (95% CI: 0.07-0.18). Adult surgical patients could be identified 1.0 year (range: 0-5.4 years) before beginning their presurgical evaluation with AUC =0.85 (95% CI: 0.78-0.93) and PR-AUC =0.31 (95% CI: 0.14-0.48). By the time patients began their presurgical evaluation, the ML algorithms identified pediatric and adult surgical patients with AUC =0.93 and 0.95, respectively. The mean squared error of the predicted probability of surgical candidacy (Brier scores) was 0.018 in pediatrics and 0.006 in adults. CONCLUSIONS: Site-specific machine learning algorithms can identify candidates for epilepsy surgery early in the disease course in diverse practice settings.

Recommendations for development of acute seizure action plans (ASAPs) from an expert panel.

PURPOSE OF REVIEW: Disease-related treatment action plans for acute exacerbations providing information that may be helpful for self-management for patients and caregivers are commonly used for chronic conditions such as asthma and diabetes. However, among patients with epilepsy, a review of the literature suggested that the majority did not have an action plan in place for acute seizure treatment. RECENT FINDINGS: Currently, there is a lack of unified guidance on seizure action plans (SAPs) in the literature. In the authors' opinion, available formats have limitations for practical use and may not be easily customizable to individual patients, and they are not often designed to provide simple-to-follow steps for rapid immediate steps to determine and initiate appropriate treatment of seizure emergencies. Our group reviewed current examples of SAPs and provided guidance on the development of acute seizure action plans (ASAPs) designed to facilitate rapid, appropriate acute care in the community and to be as useful as possible for a wide range of care partners, including those with limited experience. SUMMARY: This paper provides agreed upon expert opinion recommendations and considerations for goals, development process, types of content, and format for an ASAP.

Super-Refractory Status Epilepticus in Children: A Retrospective Cohort Study.

OBJECTIVES: To characterize the pediatric super-refractory status epilepticus population by describing treatment variability in super-refractory status epilepticus patients and comparing relevant clinical characteristics, including outcomes, between super-refractory status epilepticus, and nonsuper-refractory status epilepticus patients. DESIGN: Retrospective cohort study with prospectively collected data between June 2011 and January 2019. SETTING: Seventeen academic hospitals in the United States. PATIENTS: We included patients 1 month to 21 years old presenting with convulsive refractory status epilepticus. We defined super-refractory status epilepticus as continuous or intermittent seizures lasting greater than or equal to 24 hours following initiation of continuous infusion and divided the cohort into super-refractory status epilepticus and nonsuper-refractory status epilepticus groups. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified 281 patients (157 males) with a median age of 4.1 years (1.3-9.5 yr), including 31 super-refractory status epilepticus patients. Compared with nonsuper-refractory status epilepticus group, super-refractory status epilepticus patients had delayed initiation of first nonbenzodiazepine-antiseizure medication (149 min [55-491.5 min] vs 62 min [33.3-120.8 min]; p = 0.030) and of continuous infusion (495 min [177.5-1,255 min] vs 150 min [90-318.5 min]; p = 0.003); prolonged seizure duration (120 hr [58-368 hr] vs 3 hr [1.4-5.9 hr]; p < 0.001) and length of ICU stay (17 d [9.5-40 d] vs [1.8-8.8 d]; p < 0.001); more medical complications (18/31 [58.1%] vs 55/250 [22.2%] patients; p < 0.001); lower return to baseline function (7/31 [22.6%] vs 182/250 [73.4%] patients; p < 0.001); and higher mortality (4/31 [12.9%] vs 5/250 [2%]; p = 0.010). Within the super-refractory status epilepticus group, status epilepticus resolution was attained with a single continuous infusion in 15 of 31 patients (48.4%), two in 10 of 31 (32.3%), and three or more in six of 31 (19.4%). Most super-refractory status epilepticus patients (30/31, 96.8%) received midazolam as first choice. About 17 of 31 patients (54.8%) received additional treatments. CONCLUSIONS: Super-refractory status epilepticus patients had delayed initiation of nonbenzodiazepine antiseizure medication treatment, higher number of medical complications and mortality, and lower return to neurologic baseline than nonsuper-refractory status epilepticus patients, although these associations were not adjusted for potential confounders. Treatment approaches following the first continuous infusion were heterogeneous, reflecting limited information to guide clinical decision-making in super-refractory status epilepticus.

Correction: The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system.

PURPOSE: Clinicians and researchers must contextualize a patient's genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care. METHODS: Three of the nation's leading children's hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. RESULTS: Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype-phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. CONCLUSIONS: The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.

Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.

OBJECTIVE: Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epilepsy surgery candidacy scores. METHODS: The application was trained on notes from (1) patients with a diagnosis of epilepsy and a history of resective epilepsy surgery and (2) patients who were seizure-free without surgery. The testing set included all patients with unknown surgical candidacy status and an upcoming neurology visit. Training and testing sets were updated weekly for 1 year. One- to three-word phrases contained in patients' notes were used as features. Patients prospectively identified by the application as candidates for surgery were manually reviewed by two epileptologists. Performance metrics were defined by comparing NLP-derived surgical candidacy scores with surgical candidacy status from expert chart review. RESULTS: The training set was updated weekly and included notes from a mean of 519 ± 67 patients. The area under the receiver operating characteristic curve (AUC) from 10-fold cross-validation was 0.90 ± 0.04 (range = 0.83-0.96) and improved by 0.002 per week (P < .001) as new patients were added to the training set. Of the 6395 patients who visited the neurology clinic, 4211 (67%) were evaluated by the model. The prospective AUC on this test set was 0.79 (95% confidence interval [CI] = 0.62-0.96). Using the optimal surgical candidacy score threshold, sensitivity was 0.80 (95% CI = 0.29-0.99), specificity was 0.77 (95% CI = 0.64-0.88), positive predictive value was 0.25 (95% CI = 0.07-0.52), and negative predictive value was 0.98 (95% CI = 0.87-1.00). The number needed to screen was 5.6. SIGNIFICANCE: An electronic health record-integrated NLP application can accurately assign surgical candidacy scores to patients in a clinical setting.

Identifying epilepsy psychiatric comorbidities with machine learning.

OBJECTIVE: People with epilepsy are at increased risk for mental health comorbidities. Machine-learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine-learning classifiers that identify current or lifetime history of comorbid psychiatric conditions in teenagers and young adults with epilepsy. MATERIALS AND METHODS: Eligible participants were >12 years old with epilepsy. All participants were interviewed using the Mini International Neuropsychiatric Interview (MINI) or the MINI Kid Tracking and asked five open-ended conversational questions. N-grams and Linguistic Inquiry and Word Count (LIWC) word categories were used to construct machine learning classification models from language harvested from interviews. Data were analyzed for four individual MINI identified disorders and for three mutually exclusive groups: participants with no psychiatric disorders, participants with non-suicidal psychiatric disorders, and participants with any degree of suicidality. Performance was measured using areas under the receiver operating characteristic curve (AROCs). RESULTS: Classifiers were constructed from 227 interviews with 122 participants (7.5 ± 3.1 minutes and 454 ± 299 words). AROCs for models differentiating the non-overlapping groups and individual disorders ranged 57%-78% (many with P < .02). DISCUSSION AND CONCLUSION: Machine-learning classifiers of spoken language can reliably identify current or lifetime history of suicidality and depression in people with epilepsy. Data suggest identification of anxiety and bipolar disorders may be achieved with larger data sets. Machine-learning analysis of spoken language can be promising as a useful screening alternative when traditional approaches are unwieldy (eg, telephone calls, primary care offices, school health clinics).

Differential antiseizure medication sensitivity of the Affective Reactivity Index: A randomized controlled trial in new-onset pediatric focal epilepsy.

BACKGROUND: Irritability is a adverse effect of many antiseizure medications (ASMs), but there are no validated measures currently available to characterize this behavioral risk. We examined both child and parent/guardian versions of the Affective Reactivity Index (ARI), a validated measure developed for application in adolescent psychiatry, to determine its sensitivity to ASM-related irritability. We hypothesized irritability increases associated with levetiracetam (LEV) but not lamotrigine (LTG) or oxcarbazepine (OXC). METHOD: The ARI was administered to 71 child and parent/guardian pairs randomized to one of three common ASMs (LEV, LTG, OXC) used to treat new-onset focal (localization-related) epilepsy. Subjects were recruited as part of a prospective multicenter, randomized, open-label, parallel group design. The ARI was administered at baseline prior to treatment initiation and again at 3 months after ASM initiation. RESULTS: There was a significant increase in ARI ratings for both child and parent/guardian ratings for LEV but not LTG or OXC when assessed 3 months after treatment initiation. When examined on the individual subject level using a criterion of at least a 3-point ARI increase, there was an increase associated with LEV for child ratings but not parent/guardian scores. CONCLUSION: Both child and parent/guardian versions of the ARI appear sensitive to medication-induced irritability associated with LEV on both the group and individual levels. The findings extend the applicability of ARI from characterizing the presence of clinical irritability as a psychiatric diagnostic feature to a more modifiable aspect of behavior change related to medication management and support its use in clinical trial applications.

Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.

Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluations. To assess this, an NLP algorithm was trained to identify potential surgical candidates using 1097 notes from 175 epilepsy patients with a history of resective epilepsy surgery and 268 patients who achieved seizure freedom without surgery (total N = 443 patients). The model was tested on 8340 notes from 3776 patients with epilepsy whose surgical candidacy status was unknown (2029 male, 1747 female, median age = 9 years; age range = 0-60 years). Multiple linear regression using demographic variables as covariates was used to test for correlations between patient race and surgical candidacy scores. After accounting for other demographic and socioeconomic variables, patient race, gender, and primary language did not influence surgical candidacy scores (P > .35 for all). Higher scores were given to patients >18 years old who traveled farther to receive care, and those who had a higher family income and public insurance (P < .001, .001, .001, and .01, respectively). Demographic effects on surgical candidacy scores appeared to reflect patterns in patient referrals.

Development of information sharing in language neocortex in childhood-onset drug-resistant epilepsy.

OBJECTIVE: We studied age-related dynamics of information sharing among cortical language regions with electrocorticographic high-gamma modulation during picture-naming and story-listening tasks. METHODS: Seventeen epilepsy patients aged 4-19 years, undergoing extraoperative monitoring with left-hemispheric subdural electrodes, were included. Mutual information (MI), a nondirectional measure of shared information, between 16 pairs of cortical regions of interest, was computed from trial-averaged 70-150 Hz power modulations during language tasks. Impact of age on pairwise MI between language regions and their determinants were ascertained with regression analysis. RESULTS: During picture naming, significant increase in MI with age was seen between pairwise combinations of Broca's area, inferior precentral gyrus (iPreC), and frontal association cortex (FAC); Wernicke's area and posterior association cortex (PAC); and Broca's and Wernicke's areas. During story listening, significant age-related increase in MI was seen between Wernicke's area and either Broca's area, FAC, or PAC; and between Broca's area and FAC. Significant impact of baseline intelligence quotient was seen on the relationship between age and MI for all pairs, except between Broca's area and iPreC. The mean MI was higher during naming compared to listening for pairs including iPreC with Broca's area, FAC, or PAC and was lower for pairs of Wernicke's area or PAC with anterior language regions. SIGNIFICANCE: Information sharing matures with age "within" frontal and temporoparietal language cortices, and "between" Broca's and Wernicke's areas. This study provides evidence for distinct patterns of developmental plasticity within perisylvian language cortex and has implications for planning epilepsy surgery.