Atrophy of the pedunculopontine nucleus region in patients with sleep-predominant seizures: A voxel-based morphometry study
Non–rapid eye movement (NREM) sleep increases interictal epileptiform discharges and frequency of seizures, whereas REM sleep suppresses them. The pedunculopontine nucleus (PPN), one of the REM sleep–modulating structures, is postulated to have a potent antiepileptogenic role. We asked if patients with sleep-predominant seizures (SPS) show volume changes in the region of the PPN compared to those with seizures occurring during awake state only (nSPS). The volume of the PPN region was assessed in patients with SPS, those with nSPS, and healthy volunteers, through voxel-based morphometry and automated, nonbiased region of interest (ROI) analysis of T1 magnetic resonance (MR) images. The volume of PPN region was statistically smaller in patients with SPS (n = 33) than in those with nSPS (n = 40) and healthy controls (n = 30) after controlling for covariates. These results suggest that a structural change in the PPN may be associated with sleep-predominant timing of seizure occurrence. Our findings might help understand the intervening pathomechanism that lies between the human sleep–wake cycle and epilepsy.
Seizure-onset zone localization by statistical parametric mapping in visually normal 18F-FDG PET studies
Neuroimaging is crucial in the presurgical evaluation of patients with medically refractory epilepsy. To improve the moderate sensitivity of [18F]fluorodeoxyglucose–positron emission tomography (18F-FDG-PET), our aim was to evaluate the usefulness of statistical parametric mapping (SPM) to localize the seizure-onset zone (SOZ) in PET studies deemed normal by visual assessment.Methods
Fifty-five patients with medically refractory epilepsy whose 18F-FDG-PET was visually evaluated as normal were retrospectively included. Twenty of these patients had undergone surgical intervention. PET images were analyzed by SPM8 using a corrected p-value of p < 0.05 and three uncorrected p-values of p < 0.0001, p < 0.001, and p < 0.005, matched with minimum cluster sizes of k > 0, k > 20, k > 100, and k > 200, respectively. The SPM-identified potential seizure zone (SZ) was compared to the SOZ, which was determined by consensus during patient management meetings in the epilepsy unit, taking into account presurgical tests. Studies in which the SPM-identified potential SZ was concordant with the SOZ were considered “correctly localizing.”Results
The SPM threshold combination with the least restrictive p-value and greatest minimum cluster size achieved the highest rate of correctly localizing studies. When p < 0.005/k > 200 was used, 40% (22/55) of studies were correctly localizing, and the concordance obtained in the surgically intervened subgroup was substantial (к = 0.607, 95% confidence interval [CI] 0.258–0.957), which was comparable to the concordance obtained by magnetic resonance imaging (MRI) (к = 0.783, 95% CI 0.509–1.000).Significance
SPM offers improved SOZ localization in 18F-FDG-PET studies that are negative on visual assessment. For this purpose, statistical parametric maps could be thresholded with liberal p-values and restrictive cluster sizes.
Children with rolandic spikes may or may not have seizures, ranging from benign rolandic epilepsy to severe atypical rolandic epilepsy. We investigated whether ripples (80–250 Hz), superimposed on rolandic spikes in surface electroencephalography (EEG), can differentiate between different entities.Methods
In this cohort study we analyzed the EEG studies of children with rolandic spikes without other EEG or magnetic resonance imaging (MRI) abnormalities. They were divided into the following three groups: (1) rolandic spikes but no epilepsy, (2) typical rolandic epilepsy, and (3) atypical and symptomatic rolandic epilepsy. Ripples superimposed on rolandic spikes were marked in 10 minutes of EEG, and compared to the number of seizures before the EEG. Receiver operating characteristic (ROC) curves were constructed to determine the predictive value of ripples and spikes for having epilepsy (groups 2 and 3) and for differentiating benign courses (groups 1 or 2) from atypical and symptomatic epilepsy (group 3). Ripples were also marked in the time frequency spectrum of averaged rolandic spikes.Results
Ripples were found in 13 of 22 children. Children without epilepsy showed no ripples, except for a single child with only one ripple. The number of ripples showed a significant positive correlation with the number of seizures (ρ = 0.70, p = 0.001), whereas spikes had a borderline significant correlation (ρ = 0.43, p = 0.05). Presence of more than two ripples was a predictor for having seizures (area under the curve [AUC] 0.84), whereas spikes could not predict having seizures (AUC 0.53). More than five ripples predicted the difference between benign courses and atypical and symptomatic epilepsy (AUC 0.91, sensitivity 63%, specificity 100%). Ripples in the time frequency spectra appeared in all children and were not related to seizures.Significance
Absence of ripples on top of rolandic spikes predicts a relative benign clinical entity, whereas in the presence of several ripples, the child is likely to have more seizures than classical rolandic epilepsy, and pharmacologic treatment might be needed.
On April 21, 2015, the first SCN8A Encephalopathy Research Group convened in Washington, DC, to assess current research into clinical and pathogenic features of the disorder and prepare an agenda for future research collaborations. The group comprised clinical and basic scientists and representatives of patient advocacy groups. SCN8A encephalopathy is a rare disorder caused by de novo missense mutations of the sodium channel gene SCN8A, which encodes the neuronal sodium channel Nav1.6. Since the initial description in 2012, approximately 140 affected individuals have been reported in publications or by SCN8A family groups. As a result, an understanding of the severe impact of SCN8A mutations is beginning to emerge. Defining a genetic epilepsy syndrome goes beyond identification of molecular etiology. Topics discussed at this meeting included (1) comparison between mutations of SCN8A and the SCN1A mutations in Dravet syndrome, (2) biophysical properties of the Nav1.6 channel, (3) electrophysiologic effects of patient mutations on channel properties, (4) cell and animal models of SCN8A encephalopathy, (5) drug screening strategies, (6) the phenotypic spectrum of SCN8A encephalopathy, and (7) efforts to develop a bioregistry. A panel discussion of gaps in bioregistry, biobanking, and clinical outcomes data was followed by a planning session for improved integration of clinical and basic science research. Although SCN8A encephalopathy was identified only recently, there has been rapid progress in functional analysis and phenotypic classification. The focus is now shifting from identification of the underlying molecular cause to the development of strategies for drug screening and prioritized patient care.