Learn how to interpret and use intracranial EEG findings.

Epilepsy surgery is the therapy of choice for many patients with drug-resistant focal epilepsy. Recognizing and describing ictal and interictal patterns with intracranial electroencephalography (EEG) recordings is important in order to most efficiently leverage advantages of this technique to accurately delineate the seizure-onset zone before undergoing surgery. In this seminar in epileptology, we address learning objective "1.4.11 Recognize and describe ictal and interictal patterns with intracranial recordings" of the International League against Epilepsy curriculum for epileptologists. We will review principal considerations of the implantation planning, summarize the literature for the most relevant ictal and interictal EEG patterns within and beyond the Berger frequency spectrum, review invasive stimulation for seizure and functional mapping, discuss caveats in the interpretation of intracranial EEG findings, provide an overview on special considerations in children and in subdural grids/strips, and review available quantitative/signal analysis approaches. To be as practically oriented as possible, we will provide a mini atlas of the most frequent EEG patterns, highlight pearls for its not infrequently challenging interpretation, and conclude with two illustrative case examples. This article shall serve as a useful learning resource for trainees in clinical neurophysiology/epileptology by providing a basic understanding on the concepts of invasive intracranial EEG.

Environmental risk factors for REM sleep behavior disorder: a multicenter case-control study.

OBJECTIVE: Idiopathic REM sleep behavior disorder is a parasomnia characterized by dream enactment and is commonly a prediagnostic sign of parkinsonism and dementia. Since risk factors have not been defined, we initiated a multicenter case-control study to assess environmental and lifestyle risk factors for REM sleep behavior disorder. METHODS: Cases were patients with idiopathic REM sleep behavior disorder who were free of dementia and parkinsonism, recruited from 13 International REM Sleep Behavior Disorder Study Group centers. Controls were matched according to age and sex. Potential environmental and lifestyle risk factors were assessed via standardized questionnaire. Unconditional logistic regression adjusting for age, sex, and center was conducted to investigate the environmental factors. RESULTS: A total of 694 participants (347 patients, 347 controls) were recruited. Among cases, mean age was 67.7 ± 9.6 years and 81.0% were male. Cases were more likely to smoke (ever smokers = 64.0% vs 55.5%, adjusted odds ratio [OR] = 1.43, p = 0.028). Caffeine and alcohol use were not different between cases and controls. Cases were more likely to report previous head injury (19.3% vs 12.7%, OR = 1.59, p = 0.037). Cases had fewer years of formal schooling (11.1 ± 4.4 years vs 12.7 ± 4.3, p < 0.001), and were more likely to report having worked as farmers (19.7% vs 12.5% OR = 1.67, p = 0.022) with borderline increase in welding (17.8% vs 12.1%, OR = 1.53, p = 0.063). Previous occupational pesticide exposure was more prevalent in cases than controls (11.8% vs 6.1%, OR = 2.16, p = 0.008). CONCLUSIONS: Smoking, head injury, pesticide exposure, and farming are potential risk factors for idiopathic REM sleep behavior disorder.

Replication of restless legs syndrome loci in three European populations.

BACKGROUND: Restless legs syndrome (RLS) is associated with common variants in three intronic and intergenic regions in MEIS1, BTBD9, and MAP2K5/LBXCOR1 on chromosomes 2p, 6p and 15q. METHODS: Our study investigated these variants in 649 RLS patients and 1230 controls from the Czech Republic (290 cases and 450 controls), Austria (269 cases and 611 controls) and Finland (90 cases and 169 controls). Ten single nucleotide polymorphisms (SNPs) within the three genomic regions were selected according to the results of previous genome-wide scans. Samples were genotyped using Sequenom platforms. RESULTS: We replicated associations for all loci in the combined samples set (rs2300478 in MEIS1, p = 1.26 x 10(-5), odds ratio (OR) = 1.47, rs3923809 in BTBD9, p = 4.11 x 10(-5), OR = 1.58 and rs6494696 in MAP2K5/LBXCOR1, p = 0.04764, OR = 1.27). Analysing only familial cases against all controls, all three loci were significantly associated. Using sporadic cases only, we could confirm the association only with BTBD9. CONCLUSION: Our study shows that variants in these three loci confer consistent disease risks in patients of European descent. Among the known loci, BTBD9 seems to be the most consistent in its effect on RLS across populations and is also most independent of familial clustering.

Transient restless legs syndrome after spinal anesthesia: a prospective study.

OBJECTIVE: To assess the incidence and time course of new-onset restless legs syndrome (RLS) after spinal anesthesia. METHODS: A total of 202 consecutive patients undergoing spinal anesthesia for various types of surgery were prospectively evaluated regarding the presence and severity of RLS symptoms 48 to 72 hours post surgery and after 1 week, 1 month, 3 months, and 6 months. A diagnosis of RLS was made according to the criteria of the International RLS Study Group (IRLSSG), and severity was assessed by the IRLSSG severity scale. RESULTS: Of 161 patients without any history of RLS, 8.7% developed first-onset RLS after spinal anesthesia. Symptoms were transient, with a mean duration of 33 +/- 30 days. Low mean corpuscular volume and mean corpuscular hemoglobin were associated with the occurrence of new-onset RLS after spinal anesthesia. CONCLUSIONS: Transient RLS can be induced by spinal anesthesia. The mechanisms by which spinal anesthesia can trigger RLS are unclear but may include deficits in spinal sensorimotor integration in susceptible individuals.