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BACKGROUND: Kleefstra syndrome (KS) or 9q34.3 microdeletion syndrome (OMIM #610253) is a rare genetic condition featuring intellectual disability, hypotonia, and dysmorphic facial features. Autism spectrum disorder, severe language impairment, and sleep disorders have also been described. The syndrome can be either caused by a microdeletion in 9q34.3 or by pathogenic variants in the euchromatin histone methyltransferase 1 gene (EHMT1, *607001). Although epilepsy has been reported in 20 to 30% of subjects, a detailed description of epileptic features and underlying etiology is still lacking. The purpose of this study is to investigate epilepsy features in a cohort of epileptic patients with KS. METHODS: This multicenter study investigated eight patients with KS and epilepsy. Our findings were compared with literature data. RESULTS: We included five patients with 9q or 9q34.33 deletions, a subject with a complex translocation involving EHMT1, and two with pathogenic EHMT1 variants. All patients presented with moderate to severe developmental delay, language impairment, microcephaly, and infantile hypotonia. Although the epileptic manifestations were heterogeneous, most patients experienced focal seizures. The seizure frequency differs according to the age of epilepsy onset, with patients with early-onset epilepsy (before 36 months of age) presenting more frequent seizures. An overtime reduction in seizure frequency, as well as in antiseizure drug number, was observed in all patients. Developmental delay degree did not correlate with seizure onset and frequency or drug resistance. CONCLUSION: Epilepsy is a frequent finding in KS, but the underlying pathogenetic mechanism and specific features remain elusive.
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Transtorno do Espectro Autista , Epilepsia , Deficiência Intelectual , Transtornos do Desenvolvimento da Linguagem , Humanos , Pré-Escolar , Deficiência Intelectual/complicações , Deficiência Intelectual/genética , Hipotonia Muscular/genética , Mutação , Epilepsia/genética , ConvulsõesRESUMO
The present work aims at validating a Bayesian multi-dipole modeling algorithm (SESAME) in the clinical scenario consisting of localizing the generators of single interictal epileptiform discharges from resting state magnetoencephalographic recordings. We use the results of Equivalent Current Dipole fitting, performed by an expert user, as a benchmark, and compare the results of SESAME with those of two widely used source localization methods, RAP-MUSIC and wMNE. In addition, we investigate the relation between post-surgical outcome and concordance of the surgical plan with the cerebral lobes singled out by the methods. Unlike dipole fitting, the tested algorithms do not rely on any subjective channel selection and thus contribute towards making source localization more unbiased and automatic. We show that the two dipolar methods, SESAME and RAP-MUSIC, generally agree with dipole fitting in terms of identified cerebral lobes and that the results of the former are closer to the fitted equivalent current dipoles than those of the latter. In addition, for all the tested methods and particularly for SESAME, concordance with surgical plan is a good predictor of seizure freedom while discordance is not a good predictor of poor post-surgical outcome. The results suggest that the dipolar methods, especially SESAME, represent a reliable and more objective alternative to manual dipole fitting for clinical applications in the field of epilepsy surgery.
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Eletroencefalografia , Epilepsia , Imageamento por Ressonância Magnética , Teorema de Bayes , Mapeamento Encefálico , Epilepsia/diagnóstico por imagem , Epilepsia/cirurgia , Humanos , MagnetoencefalografiaRESUMO
Source localization from M/EEG data is a fundamental step in many analysis pipelines, including those aiming at clinical applications such as the pre-surgical evaluation in epilepsy. Among the many available source localization algorithms, SESAME (SEquential SemiAnalytic Montecarlo Estimator) is a Bayesian method that distinguishes itself for several good reasons: it is highly accurate in localizing focal sources with comparably little sensitivity to input parameters; it allows the quantification of the uncertainty of the reconstructed source(s); it accepts user-defined a priori high- and low-probability search regions in input; it can localize the generators of neural oscillations in the frequency domain. Both a Python and a MATLAB implementation of SESAME are available as open-source packages under the name of SESAMEEG and are well integrated with the main software packages used by the M/EEG community; moreover, the algorithm is part of the commercial software BESA Research (from version 7.0 onwards). While SESAMEEG is arguably simpler to use than other source modeling methods, it has a much richer output that deserves to be described thoroughly. In this article, after a gentle mathematical introduction to the algorithm, we provide a complete description of the available output and show several use cases on experimental M/EEG data.
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To test the ability of different entropy measures to classify patients with different conditions of chronic disorder of consciousness, we applied the Lempel-Ziv complexity, the amplitude coalition entropy (ACE), and the synchrony coalition entropy (SCE) to the EEG signals recorded in 32 patients, clinically evaluated using the coma recovery scale revised (CRS-R). All the entropy measures indicated that differences found in the theta and alpha bands can distinguish patients in a minimal consciousness state (MCS) with respect to those in a vegetative state/unresponsive wakefulness state (VS/UWS). These differences were significant comparing the entropy measure performed on the anterior region of the left hemisphere and midline region. The values of theta-alpha entropy positively correlated with those of the CRS-R scores. Among the entropy measures, ACE most often highlighted significant differences. The higher values found in MCS were for the less impaired patients, according to their CRS-R, suggest that the preservation of signal entropy on the anterior region of the dominant hemisphere correlates with better preservation of consciousness, even in chronic conditions.
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OBJECTIVE: Non-Rapid Eye Movement (NREM) sleep promotes the spread and propagation of Interictal Epileptiform Discharges (IEDs), while IEDs are suppressed during REM. Recently, it has been shown that the inhibitory effect on epileptic activity is mostly exerted by the phasic REM (PREM) microstate. This study aims at assessing if this holds true even in the extreme condition of IEDs activation during sleep represented by Electrical Status Epilepticus during Sleep (ESES). METHODS: eight patients affected by ESES, who underwent long-term EEG were included. REM was subdivided into phasic and tonic microstates along with the sleep scoring. IEDs count was carried out using a semi-automatic method and a Spike Index (SI) was calculated. RESULTS: The SI was significantly higher in NREM sleep than in REM. Within REM, the SI was significantly lower in PREM than in tonic REM (TREM). The SI was reduced by 84% in TREM with respect to NREM and by 97% in PREM with respect to NREM. Moreover, the SI was reduced by 87% in PREM with respect to TREM. CONCLUSIONS: PREM has a greater suppressive effect on epileptic activity even in the extreme IEDs activation during sleep typical of ESES. SIGNIFICANCE: Understanding the protective effect of PREM sleep on epileptic activity might be relevant for future therapeutic approaches.
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Epilepsia , Estado Epiléptico , Eletroencefalografia/métodos , Humanos , Sono , Sono REM/fisiologiaRESUMO
BACKGROUND: Pathogenic variants in the dynamin 1 like gene are related to abnormal mitochondrial dynamics and distributions and are associated to variable clinical phenotypes. A few patients harboring the p.Arg403Cys missense variant appears to be different from the classical, more severe phenotypes, showing sudden onset of drug resistant seizures after a previously normal or slightly delayed development. CASE REPORT: We report on a boy with abrupt onset of focal status and coma at the age of 13, initially treated as autoimmune encephalitis, with final diagnosis of de novo missense p.Arg403Cys variant in the DNM1L gene. DISCUSSION: We compare his clinical, electrophysiological, biochemical, neuroradiological and histopathological picture to the rare cases reported to date and provide diagnostic clues that can help clinicians in differentiate p.Arg403Cys-related phenotype from that of immune-mediated encephalopathies. CONCLUSION: The clinical picture related to p.Arg403Cys mutations should be considered alongside acquired pathologies in the differential diagnosis of young patients with focal refractory epilepsy and encephalopathy, also occurring during late childhood or adolescence. Prompt genetic testing allows to avoid unnecessary treatments and procedures and to better define the prognosis and management strategies.
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Encefalopatias/diagnóstico , Dinaminas/genética , Doenças Mitocondriais/diagnóstico , Adolescente , Encefalopatias/genética , Encefalopatias/fisiopatologia , Diagnóstico Diferencial , Humanos , Masculino , Doenças Mitocondriais/genética , Doenças Mitocondriais/fisiopatologia , Mutação de Sentido IncorretoRESUMO
BACKGROUND: Magneto- and Electro-encephalography record the electromagnetic field generated by neural currents with high temporal frequency and good spatial resolution, and are therefore well suited for source localization in the time and in the frequency domain. In particular, localization of the generators of neural oscillations is very important in the study of cognitive processes in the healthy and in the pathological brain. NEW METHOD: We introduce the use of a Bayesian multi-dipole localization method in the frequency domain. Given the Fourier Transform of the data at one or multiple frequencies and/or trials, the algorithm approximates numerically the posterior distribution with Monte Carlo techniques. RESULTS: We use synthetic data to show that the proposed method behaves well under a wide range of experimental conditions, including low signal-to-noise ratios and correlated sources. We use dipole clusters to mimic the effect of extended sources. In addition, we test the algorithm on real MEG data to confirm its feasibility. COMPARISON WITH EXISTING METHOD(S): Throughout the whole study, DICS (Dynamic Imaging of Coherent Sources) is used systematically as a benchmark. The two methods provide similar general pictures; the posterior distributions of the Bayesian approach contain much richer information at the price of a higher computational cost. CONCLUSIONS: The Bayesian method described in this paper represents a reliable approach for localization of multiple dipoles in the frequency domain.