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Purpose: To evaluate EEG differences among syndromes in genetic generalized epilepsy based on quantified data. Method(s): Twenty-four-hour ambulatory EEGs were recorded in consecutive patients diagnosed with genetic generalized epilepsy. All epileptiform EEG abnormalities were quantified into density scores (total duration of epileptiform discharges per hour). One-way analysis of variance was conducted to find out differences in EEG density scores among the syndromes. Generalized linear mixed models were also fitted to explore the association between the proportion of "pure" generalized spike-wave paroxysms and fragments (without intervening polyspikes/polyspike-waves) and the syndromes. Result(s): In total, 6,923 epileptiform discharges were analyzed from 105 abnormal EEGs. In the analysis of variance, six EEG variables were significantly different among syndromes: total spike density (P = 0.001), total polyspike and polyspike-wave density (P = 0.049), generalized spike-wave-only density (P , 0.001), generalized paroxysm density (P , 0.001), generalized paroxysm duration mean (P = 0.018), and generalized paroxysm duration maximum (P = 0.009). The density of epileptiform discharges and the paroxysm durations were the highest in juvenile absence epilepsy followed by juvenile myoclonic epilepsy, childhood absence epilepsy, and generalized epilepsy with tonic-clonic seizures only. Generalized linear mixed models revealed that "pure" generalized spike-wave discharges (without intervening polyspikes/polyspike waves) tended to be more frequent in absence epilepsies, although the difference was not statistically significant (P = 0.21). Conclusion(s): The findings of this study suggest that the density and duration of epileptiform discharges can help differentiate among genetic generalized epilepsy syndromes.Copyright © 2016 by the American Clinical Neurophysiology Society.

Background: We investigated the association between epileptiform EEG abnormalities and the preceding duration of seizure freedom in genetic generalized epilepsies (GGE). Method(s): We analyzed 24-hour ambulatory EEG recordings of patients with GGE diagnosed and classified according to the International League Against Epilepsy criteria. We quantified epileptiform EEG abnormalities into density scores (total duration of epileptiform discharges per hour) and estimated the preceding seizure-free duration at the time of EEG recording based on the last self-reported seizure. We then employed regression analysis to quantitate the relationship between the duration of seizure freedom and EEG variables. Result(s): We analyzed 6,923 epileptiform discharges from 105 patients with abnormal 24-hour EEGs. In the regression analysis exploring the crude associations, we found significant correlations between 6 EEG variables and the duration of seizure freedom indicating that shorter duration of seizure freedom was associated with higher spike densities and longer paroxysms. These associations were not affected by confounders such as syndrome, age at EEG, age at epilepsy onset, sex, duration of epilepsy, or number of antiepileptic drugs. Conclusion(s): Higher densities and longer durations of epileptiform discharges may be retrospectively associated with a shorter duration of self-reported seizure freedom. Hence, EEG can potentially be used as a biomarker of prognosis in GGE. These findings need to be validated in a prospective study in order to define EEG markers of future seizure freedom.Copyright © 2016 American Academy of Neurology.

Purpose: To evaluate EEG differences among syndromes in genetic generalized epilepsy based on quantified data. Method(s): Twenty-four-hour ambulatory EEGs were recorded in consecutive patients diagnosed with genetic generalized epilepsy. All epileptiform EEG abnormalities were quantified into density scores (total duration of epileptiform discharges per hour). One-way analysis of variance was conducted to find out differences in EEG density scores among the syndromes. Generalized linear mixed models were also fitted to explore the association between the proportion of "pure" generalized spike-wave paroxysms and fragments (without intervening polyspikes/polyspike-waves) and the syndromes. Result(s): In total, 6,923 epileptiform discharges were analyzed from 105 abnormal EEGs. In the analysis of variance, six EEG variables were significantly different among syndromes: total spike density (P = 0.001), total polyspike and polyspike-wave density (P = 0.049), generalized spike-wave-only density (P , 0.001), generalized paroxysm density (P , 0.001), generalized paroxysm duration mean (P = 0.018), and generalized paroxysm duration maximum (P = 0.009). The density of epileptiform discharges and the paroxysm durations were the highest in juvenile absence epilepsy followed by juvenile myoclonic epilepsy, childhood absence epilepsy, and generalized epilepsy with tonic-clonic seizures only. Generalized linear mixed models revealed that "pure" generalized spike-wave discharges (without intervening polyspikes/polyspike waves) tended to be more frequent in absence epilepsies, although the difference was not statistically significant (P = 0.21). Conclusion(s): The findings of this study suggest that the density and duration of epileptiform discharges can help differentiate among genetic generalized epilepsy syndromes.Copyright © 2016 by the American Clinical Neurophysiology Society.

Background: We investigated the association between epileptiform EEG abnormalities and the preceding duration of seizure freedom in genetic generalized epilepsies (GGE). Method(s): We analyzed 24-hour ambulatory EEG recordings of patients with GGE diagnosed and classified according to the International League Against Epilepsy criteria. We quantified epileptiform EEG abnormalities into density scores (total duration of epileptiform discharges per hour) and estimated the preceding seizure-free duration at the time of EEG recording based on the last self-reported seizure. We then employed regression analysis to quantitate the relationship between the duration of seizure freedom and EEG variables. Result(s): We analyzed 6,923 epileptiform discharges from 105 patients with abnormal 24-hour EEGs. In the regression analysis exploring the crude associations, we found significant correlations between 6 EEG variables and the duration of seizure freedom indicating that shorter duration of seizure freedom was associated with higher spike densities and longer paroxysms. These associations were not affected by confounders such as syndrome, age at EEG, age at epilepsy onset, sex, duration of epilepsy, or number of antiepileptic drugs. Conclusion(s): Higher densities and longer durations of epileptiform discharges may be retrospectively associated with a shorter duration of self-reported seizure freedom. Hence, EEG can potentially be used as a biomarker of prognosis in GGE. These findings need to be validated in a prospective study in order to define EEG markers of future seizure freedom.Copyright © 2016 American Academy of Neurology.

Objective We sought to investigate the temporal patterns and sleep-wake cycle-related epileptiform discharges (EDs) in genetic generalized epilepsies (GGEs). Methods We studied 24-hour ambulatory electroencephalography (EEG) recordings of patients with GGE, diagnosed and classified according to the International League against Epilepsy criteria. We manually coded the type of discharge, time of occurrence, duration, and arousal state of each ED. We employed mixed effects Poisson regression modeling to study the temporal distribution of epileptiform discharges. Additionally, we used multinomial regression analysis to explore the significance of the relationship between different states of arousal and types of epileptiform discharges. Results We analyzed 6923 EDs from 105 abnormal 24-hour EEGs. Mixed effects Poisson regression analysis demonstrated significant changes in ED counts across time blocks. This distribution was largely influenced by the state of arousal. Generalized fragments (duration < 2 s) and focal discharges were more frequent during non-REM sleep while paroxysms (duration >= 2 s) were more frequent in wakefulness. Overall, 67% of epileptiform discharges occurred in non-REM sleep and only 33% occurred in wakefulness. Twenty-four patients (23%) had ED exclusively in sleep. Epileptiform discharges peaked from 23:00 through 07:00 h. Significance There is a time-of-day dependency of ED with a significant influence exerted by the state of arousal. Our observations suggest that the generation of epileptiform discharges is not a random process but is the result of complex interactions among biological rhythms such as the sleep-wake cycle and the intrinsic circadian pacemaker. High density of ED in sleep suggests that 24-hour EEG recording with the capture of natural sleep may be more useful than routine EEG to diagnose GGE.Copyright © 2016 Elsevier Inc.

Objective We sought to investigate the temporal patterns and sleep-wake cycle-related epileptiform discharges (EDs) in genetic generalized epilepsies (GGEs). Methods We studied 24-hour ambulatory electroencephalography (EEG) recordings of patients with GGE, diagnosed and classified according to the International League against Epilepsy criteria. We manually coded the type of discharge, time of occurrence, duration, and arousal state of each ED. We employed mixed effects Poisson regression modeling to study the temporal distribution of epileptiform discharges. Additionally, we used multinomial regression analysis to explore the significance of the relationship between different states of arousal and types of epileptiform discharges. Results We analyzed 6923 EDs from 105 abnormal 24-hour EEGs. Mixed effects Poisson regression analysis demonstrated significant changes in ED counts across time blocks. This distribution was largely influenced by the state of arousal. Generalized fragments (duration < 2 s) and focal discharges were more frequent during non-REM sleep while paroxysms (duration >= 2 s) were more frequent in wakefulness. Overall, 67% of epileptiform discharges occurred in non-REM sleep and only 33% occurred in wakefulness. Twenty-four patients (23%) had ED exclusively in sleep. Epileptiform discharges peaked from 23:00 through 07:00 h. Significance There is a time-of-day dependency of ED with a significant influence exerted by the state of arousal. Our observations suggest that the generation of epileptiform discharges is not a random process but is the result of complex interactions among biological rhythms such as the sleep-wake cycle and the intrinsic circadian pacemaker. High density of ED in sleep suggests that 24-hour EEG recording with the capture of natural sleep may be more useful than routine EEG to diagnose GGE.Copyright © 2016 Elsevier Inc.

Summary Conventionally, epilepsy is dichotomized into distinct "focal" and "generalized" categories. However, many studies have reported so-called focal features among patients with idiopathic generalized epilepsy (IGE) in the domains of semiology, electroencephalography, neuropsychology, neuropathology, and neuroimaging. We sought to review such features and clinical implications. A Web of Science database search was conducted to identify relevant publications. Our search yielded 145 papers describing focal features involving different domains in IGE, with 117 papers analyzed after excluding abstracts and case reports. Focal semiologic features are commonly seen in IGE. There are conflicting data from studies in the domains of electroencephalography, neuroimaging, and neuropathology. Studies on neuropsychology are suggestive of frontal lobe functional deficits in juvenile myoclonic epilepsy. Most advanced neuroimaging studies demonstrate the involvement of both the thalamus and the cortex during generalized spike-wave discharges (GSWDs). A few electroencephalographic and neuroimaging studies indicate that the cortex precedes the thalamus at the onset of GSWD. Focal features may contribute to misdiagnosis of IGE as focal epilepsy. However there are methodologic limitations in the studies that affect the results. © 2014 International League Against Epilepsy.

We examined the phenotypic variation and clinical genetics in nine families with generalized epilepsy with febrile seizures plus (GEFS+). This genetic epilepsy syndrome with heterogeneous phenotypes was hitherto described in only one family. We obtained genealogical information on 799 individuals and conducted detailed evaluation of 272 individuals. Ninety-one individuals had a history of seizures and 63 had epilepsy consistent with the GEFS+ syndrome. Epilepsy phenotypes were febrile seizures (FS) in 31, febrile seizures plus (FS+) in 15, FS+ with other seizure types (atonic, myoclonic, absence, or complex partial) in 8, and myoclonic-astatic epilepsy in 9 individuals. Inheritance was autosomal dominant with approximately 60% penetrance. This study confirms and expands the spectrum of GEFS+ and provides new insights into the phenotypic relationships and genetics of FS and the generalized epilepsies of childhood. Moreover, the ability to identify large families with this newly recognized common, childhood-onset, generalized genetic epilepsy syndrome suggests that it should be a prime target for attempts to identify genes relevant to FS and generalized epilepsy.

We examined the phenotypic variation and clinical genetics in nine families with generalized epilepsy with febrile seizures plus (GEFS+). This genetic epilepsy syndrome with heterogeneous phenotypes was hitherto described in only one family. We obtained genealogical information on 799 individuals and conducted detailed evaluation of 272 individuals. Ninety-one individuals had a history of seizures and 63 had epilepsy consistent with the GEFS+ syndrome. Epilepsy phenotypes were febrile seizures (FS) in 31, febrile seizures plus (FS+) in 15, FS+ with other seizure types (atonic, myoclonic, absence, or complex partial) in 8, and myoclonic-astatic epilepsy in 9 individuals. Inheritance was autosomal dominant with approximately 60% penetrance. This study confirms and expands the spectrum of GEFS+ and provides new insights into the phenotypic relationships and genetics of FS and the generalized epilepsies of childhood. Moreover, the ability to identify large families with this newly recognized common, childhood-onset, generalized genetic epilepsy syndrome suggests that it should be a prime target for attempts to identify genes relevant to FS and generalized epilepsy.