Faculty Bibliography

BACKGROUND: One-third of children diagnosed with autism spectrum disorders (ASDs) are reported to have had normal early development followed by an autistic regression between the ages of 2 and 3 years. This clinical profile partly parallels that seen in Landau-Kleffner syndrome (LKS), an acquired language disorder (aphasia) believed to be caused by epileptiform activity. Given the additional observation that one-third of autistic children experience one or more seizures by adolescence, epileptiform activity may play a causal role in some cases of autism. OBJECTIVE: To compare and contrast patterns of epileptiform activity in children with autistic regressions versus classic LKS to determine if there is neurobiological overlap between these conditions. It was hypothesized that many children with regressive ASDs would show epileptiform activity in a multifocal pattern that includes the same brain regions implicated in LKS. DESIGN: Magnetoencephalography (MEG), a noninvasive method for identifying zones of abnormal brain electrophysiology, was used to evaluate patterns of epileptiform activity during stage III sleep in 6 children with classic LKS and 50 children with regressive ASDs with onset between 20 and 36 months of age (16 with autism and 34 with pervasive developmental disorder-not otherwise specified). Whereas 5 of the 6 children with LKS had been previously diagnosed with complex-partial seizures, a clinical seizure disorder had been diagnosed for only 15 of the 50 ASD children. However, all the children in this study had been reported to occasionally demonstrate unusual behaviors (eg, rapid blinking, holding of the hands to the ears, unprovoked crying episodes, and/or brief staring spells) which, if exhibited by a normal child, might be interpreted as indicative of a subclinical epileptiform condition. MEG data were compared with simultaneously recorded electroencephalography (EEG) data, and with data from previous 1-hour and/or 24-hour clinical EEG, when available. Multiple-dipole, spatiotemporal modeling was used to identify sites of origin and propagation for epileptiform transients. RESULTS: The MEG of all children with LKS showed primary or secondary epileptiform involvement of the left intra/perisylvian region, with all but 1 child showing additional involvement of the right sylvian region. In all cases of LKS, independent epileptiform activity beyond the sylvian region was absent, although propagation of activity to frontal or parietal regions was seen occasionally. MEG identified epileptiform activity in 41 of the 50 (82%) children with ASDs. In contrast, simultaneous EEG revealed epileptiform activity in only 68%. When epileptiform activity was present in the ASDs, the same intra/perisylvian regions seen to be epileptiform in LKS were active in 85% of the cases. Whereas primary activity outside of the sylvian regions was not seen for any of the children with LKS, 75% of the ASD children with epileptiform activity demonstrated additional nonsylvian zones of independent epileptiform activity. Despite the multifocal nature of the epileptiform activity in the ASDs, neurosurgical intervention aimed at control has lead to a reduction of autistic features and improvement in language skills in 12 of 18 cases. CONCLUSIONS: This study demonstrates that there is a subset of children with ASDs who demonstrate clinically relevant epileptiform activity during slow-wave sleep, and that this activity may be present even in the absence of a clinical seizure disorder. MEG showed significantly greater sensitivity to this epileptiform activity than simultaneous EEG, 1-hour clinical EEG, and 24-hour clinical EEG. The multifocal epileptiform pattern identified by MEG in the ASDs typically includes the same perisylvian brain regions identified as abnormal in LKS. When epileptiform activity is present in the ASDs, therapeutic strategies (antiepileptic drugs, steroids, and even neurosurgery) aimed at its control can lead to a significa

We report two patients with intractable partial seizures who developed generalized nonconvulsive status epilepticus (NCSE) after receiving tiagabine (TGB). Neither had a history of absence seizures or generalized epileptic discharges on prior EEG monitoring. Clinicians need to be aware of a possible association between TGB and NCSE

PURPOSE: We report the development of an instrument to assess health-related quality of life (HRQOL) in adolescents with epilepsy. METHODS: A sample of 197 English-speaking adolescents (aged 11-17 years) with epilepsy completed a test questionnaire of 88 items. Also included were mastery and self-esteem scales to assess external validity. A parent simultaneously completed an 11-item questionnaire to evaluate the child's HRQOL. Both adolescent and parent questionnaires were repeated in 2-4 weeks. Demographic information and information pertaining to seizures were collected at baseline along with assessment of systemic and neurologic toxicity. RESULTS: The QOLIE-AD-48 contains 48 items in eight subscales: epilepsy impact (12 items), memory/concentration (10), attitudes toward epilepsy (four), physical functioning (five), stigma (six), social support (four), school behavior (four), health perceptions (three), and a total summary score, with higher scores indicating better HRQOL. Internal construct validity was demonstrated in a single-factor solution for the eight dimensions. All correlations were statistically significant at p < 0.05 level. Internal consistency reliability estimated by Cronbach's alpha coefficient was 0.74 for the summary score and ranged from a low of 0.52 (three-item Health Perceptions Scale) to 0.73-0.94 for the other individual scales. Good test-retest reliability was found for the overall measure (0.83). Summary score correlations with the two external validity scales, self-efficacy and self-esteem were 0.65 and 0.54, respectively. Statistically significant differences in summary scores indicating that HRQOL was increasingly better for adolescents as seizure severity decreases (no seizures = 77+/-13, low = 70+/-17, high = 63+/-17) were found among seizure-severity groups. CONCLUSIONS: These data describe the development of a robust instrument to evaluate HRQOL in adolescents with epilepsy. Empiric analyses provide strong evidence that the QOLIE-AD-48 is both a reliable and valid measure for adolescents with epilepsy

PURPOSE: Posterior, 'Wenicke's,' language areas have a high degree of between-subject variability, as shown by electrical-stimulation mapping. We investigated the possibility of an organized structure in the distribution of posterior language areas. METHODS: Extraoperative subdural grid stimulation was performed on 67 left hemisphere-dominant patients before resective epilepsy surgery during counting, naming, and reading. Intersubject-averaged language maps were generated in which stimulation disrupted only one language function and not the others, or combinations of language functions. RESULTS: Language sites, although highly variable between subjects, were not organized randomly and appeared to be arranged into several focal, non-contiguous, higher probability 'nodes' devoted to different aspects of language processing. Speech-arrest sites were concentrated in classic Wernicke's area. Areas where stimulation induced only reading errors were found in the posterior middle temporal gyrus and the inferior parietal lobule. These regions may correspond with an orthographic input lexicon. Areas eliciting only naming errors were found in the posterior inferior temporal gyrus extending into the mid-middle temporal gyrus and may represent a visual-representation input lexicon. Sites where stimulation elicited errors in both naming and reading were more variable in location than sites devoted to only one function, extended farther anteriorly along the temporal neocortex, and may correspond with a semantic lexicon. CONCLUSIONS: The existence of high-probability nodes in posterior language cortex supports a modality-specific modular architecture and the possibility of a conserved, universal structure

The aim of the study was to assess whether post-ictal symptoms can help distinguish patients who have epileptic seizures from those with non-epileptic seizures (NES). We reviewed the spontaneous responses to the question 'What symptoms do you have after a seizure?' in 16 patients with epileptic seizures (predominantly focal with secondary generalization or generalized tonic-clonic) and 23 NES patients. Six of the 16 patients (38%) vs. only one of 23 NES patients (4.3%) noted post-ictal headache (P = 0.008). Nine epilepsy patients (56%) vs. three NES patients (13%) reported post-ictal fatigue (P = 0.004). Confusion or other symptoms did not distinguish epilepsy patients from those with NES. All epilepsy patients had at least one post-ictal symptom while 12 NES patients (52%) had none (P = 0.001). Therefore, patients evaluated for epileptic vs. non-epileptic seizures who have post-ictal fatigue or headache, are more likely to have epileptic seizures. Patients with a diagnosis of NES who note post-ictal fatigue or headache should be investigated further

OBJECT: Because appropriate patient selection is essential for achieving successful outcomes after epilepsy surgery, the need for more robust methods of predicting postoperative seizure control has been created. Standard multivariate techniques have been only 75 to 80% accurate in this regard. Recent use of artificial intelligence techniques, including neural networks, for analyzing multivariate clinical data has been successful in predicting medical outcome. METHODS: The authors applied neural network techniques to 80 consecutive patients undergoing epilepsy surgery in whom data on demographic, seizure, operative, and clinical variables to predict postoperative seizures were collected. Neural networks could be used to predict postoperative seizures in up to 98% of cases. Student's t-tests or chi-square analysis performed on individual variables revealed that only the preoperative medication index was significantly different (p = 0.02) between the two outcome groups. Six different combinations of input variables were used to train the networks. Neural network accuracies differed in their ability to predict seizures: using all data (96%); all data minus electroencephalography concordance and operative side (93%); all data except intra- or postoperative variables such as tissue pathological category (98%); all data excluding pathological category, intelligence quotient (IQ) data, and Wada results (84%); only demographics and tissue pathological category (65%); and only IQ data (63%). CONCLUSIONS: Analysis of the results reveals that several networks that are trained with the usual accepted variables characterizing the typical evaluation of epilepsy patients can predict postoperative seizures with greater than 95% accuracy