Assuntos
Área Postrema , Neuromielite Óptica , Espasmo , Humanos , Feminino , Neuromielite Óptica/complicações , Neuromielite Óptica/diagnóstico , Neuromielite Óptica/diagnóstico por imagem , Idoso , Espasmo/etiologia , Espasmo/diagnóstico , Área Postrema/diagnóstico por imagem , Imageamento por Ressonância Magnética , Dor/etiologia , Dor/diagnósticoRESUMO
Purpose of Review: Paroxysmal nonepileptic events (PNEEs) are a heterogenous group of time-limited events, characterized by changes in motor or behavioral activity beginning abruptly and ending in a short time. Owing to their manifestation, these conditions can clinically simulate seizures. Recent Findings: These episodes belong to different categories, including syncopal events, psychiatric disorders, and movement disorders. PNEEs are a common cause of diagnostic mistakes and families' concerns, and the risk of useless and sometimes even injurious treatment is considerable. The high frequency of these manifestations in clinical practice makes PNEEs a diagnostic challenge for clinicians. Summary: This review is focused on the distinctive clinical findings and treatment of PNEEs. Illustrative video recordings of the PNEEs and a video collection as a support tool for differential diagnosis are provided.
RESUMO
One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.
Assuntos
Epilepsias Parciais , Epilepsia , Malformações do Desenvolvimento Cortical , Humanos , Estudos Retrospectivos , Malformações do Desenvolvimento Cortical/complicações , Malformações do Desenvolvimento Cortical/diagnóstico por imagem , Epilepsia/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Aprendizado de Máquina , Epilepsias Parciais/diagnóstico por imagemRESUMO
OBJECTIVE: Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. METHODS: The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. RESULTS: FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. SIGNIFICANCE: FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy.
Assuntos
Epilepsia Resistente a Medicamentos , Epilepsia , Malformações do Desenvolvimento Cortical , Criança , Epilepsia Resistente a Medicamentos/complicações , Epilepsia Resistente a Medicamentos/diagnóstico por imagem , Epilepsia Resistente a Medicamentos/cirurgia , Epilepsia/diagnóstico por imagem , Epilepsia/etiologia , Epilepsia/cirurgia , Liberdade , Humanos , Imageamento por Ressonância Magnética , Malformações do Desenvolvimento Cortical/complicações , Malformações do Desenvolvimento Cortical/diagnóstico por imagem , Malformações do Desenvolvimento Cortical/cirurgia , Estudos Retrospectivos , Convulsões/diagnóstico por imagem , Convulsões/etiologia , Convulsões/cirurgia , Resultado do TratamentoRESUMO
Familial adult myoclonic epilepsy (FAME), also described with different acronyms (ADCME, BAFME, FEME, FCTE and others), is a high-penetrant autosomal dominant condition featuring cortical hand tremors, myoclonic jerks, and occasional/rare convulsive seizures. Prevalence is unknown since this condition is often under-recognized, but it is estimated to be less than 1/35,000. The disease usually starts in the second decade of life and has been genetically associated with at least 4 different loci (8q24, 2p11.1-q12.2, 5p15.31-p15 and 3q26.32-3q28). Recently, the expansion of non coding TTTTA and TTTCA repeats has been identified as the causative mutation in Japanese families linked to the 8q24. The diagnosis is supported by clinical features and electrophysiological investigations as jerk-locked back averaging, C-reflex, and somatosensory-evoked potential. Photic stimulation, emotional stress, and sleep deprivation may trigger both tonic-clonic and myoclonic seizures. FAME has a slow but progressive clinical course occurring with intellectual disability and worsening of both tremor and myoclonus although with a less severe decline compared to other progressive myoclonic epilepsies. Valproate, levetiracetam, and benzodiazepines are considered the first-line treatments.