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Background: Juvenile myoclonic epilepsy (JME) is a common adolescent epilepsy characterized by myoclonic, generalized tonic-clonic, and sometimes absence seizures. Prognosis varies, with many patients experiencing relapse despite pharmacological treatment. Recent advances in imaging and artificial intelligence suggest that combining microstructural brain changes with traditional clinical variables can enhance potential prognostic biomarkers identification. Methods: A retrospective study was conducted on patients with JME at the Severance Hospital, analyzing clinical variables and magnetic resonance imaging (MRI) data. Machine learning models were developed to predict prognosis using clinical and radiological features. Results: The study utilized six machine learning models, with the XGBoost model demonstrating the highest predictive accuracy (AUROC 0.700). Combining clinical and MRI data outperformed models using either type of data alone. The key features identified through a Shapley additive explanation analysis included the volumes of the left cerebellum white matter, right thalamus, and left globus pallidus. Conclusions: This study demonstrated that integrating clinical and radiological data enhances the predictive accuracy of JME prognosis. Combining these neuroanatomical features with clinical variables provided a robust prediction of JME prognosis, highlighting the importance of integrating multimodal data for accurate prognosis.

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Juvenile myoclonic epilepsy (JME) is associated with brain dysconnectivity in the default mode network (DMN). Most previous studies of patients with JME have assessed static functional connectivity in terms of the temporal correlation of signal intensity among different brain regions. However, more recent studies have shown that the directionality of brain information flow has a more significant regional impact on patients' brains than previously assumed in the present study. Here, we introduced an empirical approach incorporating independent component analysis (ICA) and spectral dynamic causal modeling (spDCM) analysis to study the variation in effective connectivity in DMN in JME patients. We began by collecting resting-state functional magnetic resonance imaging (rs-fMRI) data from 37 patients and 37 matched controls. Then, we selected 8 key nodes within the DMN using ICA; finally, the key nodes were analyzed for effective connectivity using spDCM to explore the information flow and detect patient abnormalities. This study found that compared with normal subjects, patients with JME showed significant changes in the effective connectivity among the precuneus, hippocampus, and lingual gyrus (p < 0.05 with false discovery rate (FDR) correction) with most of the effective connections being strengthened. In addition, previous studies have found that the self-connection of normal subjects' nodes showed strong inhibition, but the self-connection inhibition of the anterior cingulate cortex and lingual gyrus of the patient was decreased in this experiment (p < 0.05 with FDR correction); as the activity in these areas decreased, the nodes connected to them all appeared abnormal. We believe that the changes in the effective connectivity of nodes within the DMN are accompanied by changes in information transmission that lead to changes in brain function and impaired cognitive and executive function in patients with JME. Overall, our findings extended the dysconnectivity hypothesis in JME from static to dynamic causal and demonstrated that aberrant effective connectivity may underlie abnormal brain function in JME patients at early phase of illness, contributing to the understanding of the pathogenesis of JME. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09994-4.

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Sleep disturbances significantly impact the lives of individuals with Juvenile Myoclonic Epilepsy (JME). This study aimed to investigate sleep studies, disturbances, and the impact of anti-seizure drugs on sleep in JME patients. Relevant studies were retrieved from the National Library of Medicine (Pubmed) database and the Cochrane Library utilizing the search terms "Juvenile Myoclonic Epilepsy" and "sleep". A total of 160 papers' review, data extraction, and resolution of discrepancies were performed independently by two reviewers according to the PRISMA protocol and were registered in PROSPERO (CRD42023472439). A systematic review of 31 studies was conducted, encompassing various methodologies, including sleep questionnaires (Pittsburgh Sleep Quality Index (n = 13), Epworth Sleepiness Scale (n = 10)), polysomnography (n = 8), EEG (n = 9), actigraphy (n = 1), and transcranial magnetic stimulation (n = 1). Most studies were hospital-based (n = 31), cross-sectional (n = 11), and prospective (n = 25). Patients with JME exhibit a higher prevalence of sleep disturbances, worse quality of sleep (n = 4), daytime sleepiness (n = 2), sleep efficiency (n = 7), and increased sleep latency (n = 1) compared to controls. These disruptions are characterized by increased wakefulness (n = 3), frequent arousals (n = 3), decreased REM sleep (n = 2), and conflicting NREM sleep findings (n = 3). Additional sleep-related issues observed in JME patients include insomnia (n = 1) and increased prevalence of parasomnias such as nightmares and sleep talking. Periodic limb movement and obstructive sleep apnea are similar or less frequent (3/28). REM behavioral disorders and sleepwalking were not seen. Valproate showed conflicting effects on sleep (n = 7), while levetiracetam did not impact sleep (n = 1). These findings underlined the need for more sufficient evidence of sleep studies in JME. Future research should prioritize understanding the nature of sleep in JME and its impact on management.

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Objective: To investigate whether changes occur in the dynamic functional connectivity (dFC) of motor cerebellum with cerebral cortex in juvenile myoclonic epilepsy (JME). Methods: We adopted resting-state electroencephalography-functional magnetic resonance imaging (EEG-fMRI) and a sliding-window approach to explore the dFC of motor cerebellum with cortex in 36 JME patients compared with 30 and age-matched health controls (HCs). The motor cerebellum was divided into five lobules (I-V, VI, VIIb, VIIIa, and VIIIb). Additionally, correlation analyses were conducted between the variability of dFC and clinical variables in the Juvenile Myoclonic Epilepsy (JME) group, such as disease duration, age at disease onset, and frequency score of myoclonic seizures. Results: Compared to HCs, the JME group presented increased dFC between the motor cerebellum with SMN and DMN. Specifically, connectivity between lobule VIIb and left precentral gyrus and right inferior parietal lobule (IPL); between lobule VIIIa and right inferior frontal gyrus (IFG) and left IPL; and between lobule VIIIb and left middle frontal gyrus (MFG), bilateral superior parietal gyrus (SPG), and left precuneus. In addition, within the JME group, the strength of dFC between lobule VIIIb and left precuneus was negatively (r = -0.424, p = 0.025, Bonferroni correction) related with the frequency score of myoclonic seizures. Conclusion: In patients with JME, there is a functional dysregulation between the motor cerebellum with DMN and SMN, and the variability of dynamic functional connectivity may be closely associated with the occurrence of motor symptoms in JME.

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Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

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BACKGROUND: To explore the time-frequency structure and cross-scale coupling of electroencephalography (EEG) signals during seizure in juvenile myoclonic epilepsy (JME), correlations between different leads, as well as dynamic evolution in epileptic discharge, progression and end of seizure were examined. METHODS: EEG data were obtained for 10 subjects with JME and 10 normal controls and were decomposed using gauss continuous wavelet transform (CWT). The phase amplitude coupling (PAC) relationship between the 11th (4.57 Hz) and 17th (0.4 Hz) scale was investigated. Correlations were examined between the 11th and 17th scale EEG signals in different leads during seizure, using multi-scale cross correlation analysis. RESULTS: The time-frequency structure of JME subjects showed strong rhythmic activity in the 11th and 17th scales and a close PAC was identified. Correlation analysis revealed that the ictal JME correlation first increased in the anterior head early in seizure and gradually expanded to the posterior head. CONCLUSION: PAC was exhibited between the 11th and 17th scales during JME seizure. The results revealed that the correlation in the anterior leads was higher than the posterior leads. In the perictal period, the 17th scale EEG signal preceded the 11th scale signal and remained for some time after a seizure. This suggests that the 17th scale signal may play an important role in JME seizure.

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BACKGROUND: Drug-resistant juvenile myoclonic epilepsy (DR-JME) remains a significant challenge in neurology. Traditional management strategies often fail to achieve satisfactory control, necessitating innovative treatments. OBJECTIVE: This case report aims to evaluate the efficacy and safety of deep brain stimulation (DBS) targeting the subthalamic nucleus (STN-DBS) in a patient with DR-JME. METHODS: We describe the treatment of a patient with DR-JME using STN-DBS. The patient underwent implantation and received high-frequency stimulation (HFS) at the STN. RESULTS: One year post-implantation, the patient demonstrated a substantial reduction in motor seizure frequency by 87.5%, with improvements in quality of life and seizure severity by 52.0% and 46.7%, respectively. No adverse events were reported during the follow-up period. CONCLUSIONS: This case represents the first report of favorable outcomes with STN-DBS in a patient with DR-JME, suggesting that long-term HFS of the STN may be a promising treatment option for patients suffering from this condition.

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Juvenile myoclonic epilepsy (JME) as an idiopathic generalized epilepsy has been studied by many advanced neuroimaging techniques to elucidate its neuroanatomical basis and pathophysiological mechanisms. In this paper, we used co-activation patterns (CAPs) to explore the differences of dynamic brain activity changes in resting state between JME patients and healthy controls. 27 cases JME patients and 27 cases healthy of fMRI data were collected. The structural image data of the subjects were analyzed by voxel-based morphological analysis, and the regions with gray matter volume atrophy and high voxel were selected as the regions of interest. Further, the mean disease duration was used as boundary to divide the patients' data into the below-average time and the above-average time groups, which were defined as patient disease duration groups. And these data were used to construct CAPs and to compare changes in brain dynamics. It was found that the number of patterns occurrences and the possibility of switching between patterns were smaller than those in the healthy control, which indicated patients with damage to brain regions. For the patient time control group, the number of patterns occurrences and the possibility of switching between patterns were similar, while there was linear regression between the three values and disease duration. Collectively, this study provides important evidence for revealing the key brain regions of JME by studying the transformation between CAPs. Future studies could investigate the effects of receiving treatment on patient dynamic brain activity.

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BACKGROUND: Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels. RESULTS: Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN. CONCLUSIONS: Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.

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Epileptologists and psychiatrists have long observed a correlation between epilepsy and personality disorders (PDs) in their clinical practice. We conducted a comprehensive PubMed search looking for evidence on PDs in people with epilepsy (PwE). Out of over 600 results obtained without applying any time restriction, we selected only relevant studies (both analytical and descriptive) limited to English, Italian, French and Spanish languages, with a specific focus on PDs, rather than traits or symptoms, thus narrowing our search down to 23 eligible studies. PDs have been investigated in focal epilepsy (predominantly temporal lobe epilepsy - TLE), juvenile myoclonic epilepsy (JME) and psychogenic non-epileptic seizures (PNES), with heterogeneous methodology. Prevalence rates of PDs in focal epilepsy ranged from 18 to 42% in surgical candidates or post-surgical individuals, with Cluster C personality disorders or related traits and symptoms being most common. In JME, prevalence rates ranged from 8 to 23%, with no strong correlation with any specific PDs subtype. In PNES, prevalence rates ranged from 30 to 60%, with a notable association with Cluster B personality disorders, particularly borderline personality disorder. The presence of a PD in PwE, irrespective of subtype, complicates treatment management. However, substantial gaps of knowledge exist concerning the neurobiological substrate, effects of antiseizure medications and epilepsy surgery on concomitant PDs, all of which are indeed potential paths for future research.

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Objective: A third of patients with epilepsy continue to have seizures despite receiving adequate antiseizure medication. Transcranial direct current stimulation (tDCS) might be a viable adjunct treatment option, having been shown to reduce epileptic seizures in patients with focal epilepsy. Evidence for the use of tDCS in genetic generalized epilepsy (GGE) is scarce. We aimed to establish the feasibility of applying tDCS during fMRI in patients with GGE to study the acute neuromodulatory effects of tDCS, particularly on sensorimotor network activity. Methods: Seven healthy controls and three patients with GGE received tDCS with simultaneous fMRI acquisition while watching a movie. Three tDCS conditions were applied: anodal, cathodal and sham. Periods of 60 s without stimulation were applied between each stimulation condition. Changes in sensorimotor cortex connectivity were evaluated by calculating the mean degree centrality across eight nodes of the sensorimotor cortex defined by the Automated Anatomical Labeling atlas (primary motor cortex (precentral left and right), supplementary motor area (left and right), mid-cingulum (left and right), postcentral gyrus (left and right)), across each of the conditions, for each participant. Results: Simultaneous tDCS-fMRI was well tolerated in both healthy controls and patients without adverse effects. Anodal and cathodal stimulation reduced mean degree centrality of the sensorimotor network (Friedman's ANOVA with Dunn's multiple comparisons test; adjusted p = 0.02 and p = 0.03 respectively). Mean degree connectivity of the sensorimotor network during the sham condition was not different to the rest condition (adjusted p = 0.94). Conclusion: Applying tDCS during fMRI was shown to be feasible and safe in a small group of patients with GGE. Anodal and cathodal stimulation caused a significant reduction in network connectivity of the sensorimotor cortex across participants. This initial research supports the feasibility of using fMRI to guide and understand network modulation by tDCS that might facilitate its clinical application in GGE in the future.

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INTRODUCTION: There are rising evidences that subcortical structures, including the basal ganglia, are affected in patients with epilepsy. These structures are thought to influence the modulation and phenotypic expression of epileptic seizures. Our study aimed to evaluate the presence of structural abnormalities in subcortical structures in patients with juvenile myoclonic epilepsy (JME). METHODS: This cross-sectional study included 51 patients who were diagnosed with JME and who were monitored on an outpatient basis at the Clinic for Neurology and Psychiatry for Children and Youth in Belgrade from January 1985 to October 2017. All patients underwent transcranial parenchymal sonography (TCS) from October 2015 to October 2017. Relation of clinical parameters (seizure control andcognitive functioning,) with TCS results was assessed. RESULTS: Hyperechogenicity of the substantia nigra (SN) was detected in 37.2% of JME subjects and it was significantly more common in patients with JME than in the control group. The marked echogenicity of the red nucleus (RN) was detected in 17.6% of cases, while 11.8% of subjects had hyperechogenic RN. The presence of hyperechogenic RN (both right and left) was significantly more frequent in the group of patients with JME compared to the control group. The third ventricle diameter was larger in patients with JME than in controls. CONCLUSION: Structural changes of certain subcortical structures, primarily SN and RN, detected in JME patients indicate additional non-lesional abnormalities of the basal ganglia and midbrain structures in these patients.

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A key aspect of management of genetic generalized epilepsy involves assessing seizure control and deciding suitability for driving motor vehicles. We surveyed child neurologists and pediatric epileptologists on key questions that practitioners should ask prior to providing clearance for driving. The results showed a wide variability of practice among responders. We propose a likely appropriate process necessary to determine seizure control.

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The ILAE's Task Force on Nosology and Definitions revised in 2022 its definition of juvenile myoclonic epilepsy (JME), the most common idiopathic generalized epilepsy disorder, but this definition may well change again in the future. Although good drug response could almost be a diagnostic criterion for JME, drug resistance (DR) is observed in up to a third of patients. It is important to distinguish this from pseudoresistance, which is often linked to psychosocial problems or psychiatric comorbidities. After summarizing these aspects and the various definitions applied to JME, the present review lists the risk factors for DR-JME that have been identified in numerous studies and meta-analyses. The factors most often cited are absence seizures, young age at onset, and catamenial seizures. By contrast, photosensitivity seems to favor good treatment response, at least in female patients. Current hypotheses on DR mechanisms in JME are based on studies of either simple (e.g., cortical excitability) or more complex (e.g., anatomical and functional connectivity) neurophysiological markers, bearing in mind that JME is regarded as a neural network disease. This research has revealed correlations between the intensity of some markers and DR, and above all shed light on the role of these markers in associated neurocognitive and neuropsychiatric disorders in both patients and their siblings. Studies of neurotransmission have mainly pointed to impaired GABAergic inhibition. Genetic studies have generally been inconclusive. Increasing restrictions have been placed on the use of valproate, the standard antiseizure medication for this syndrome, owing to its teratogenic and developmental risks. Levetiracetam and lamotrigine are prescribed as alternatives, as is vagal nerve stimulation, and there are several other promising antiseizure drugs and neuromodulation methods. The development of better alternative treatments is continuing to take place alongside advances in our knowledge of JME, as we still have much to learn and understand.

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OBJECTIVES: Myotonia is a clinical sign typical of a group of skeletal muscle channelopathies, the non-dystrophic myotonias. These disorders are electrophysiologically characterized by altered membrane excitability, due to specific genetic variants in known causative genes (CLCN1 and SCN4A). Juvenile Myoclonic Epilepsy (JME) is an epileptic syndrome identified as idiopathic generalized epilepsy, its genetics is complex and still unclarified. The co-occurrence of these two phenotypes is rare and the causes likely have a genetic background. In this study, we have genetically investigated an Italian family in which co-segregates myotonia, JME, or abnormal EEG without seizures was observed. METHODS: All six individuals of the family, 4 affected and 2 unaffected, were clinically evaluated; EMG and EEG examinations were performed. For genetic testing, Exome Sequencing was performed for the six family members and Sanger sequencing was used to confirm the candidate variant. RESULTS: Four family members, the mother and three siblings, were affected by myotonia. Moreover, EEG recordings revealed interictal generalized sharp-wave discharges in all affected individuals, and two siblings were affected by JME. All four affected members share the same identified variant, c.644 T > C, p.Ile215Thr, in SCN4A gene. Variants that could account for the epileptic phenotype alone, separately from the myotonic one, were not identified. SIGNIFICANCE: These results provide supporting evidence that both myotonic and epileptic phenotypes could share a common genetic background, due to variants in SCN4A gene. SCN4A pathogenic variants, already known to be causative of myotonia, likely increase the susceptibility to epilepsy in our family. PLAIN LANGUAGE SUMMARY: This study analyzed all members of an Italian family, in which the mother and three siblings had myotonia and epilepsy. Genetic analysis allowed to identify a variant in the SCN4A gene, which appears to be the cause of both clinical signs in this family.

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INTRODUCTION/BACKGROUND: Juvenile myoclonic epilepsy (JME) syndrome is known to cause alterations in brain structure and white matter integrity. The study aimed to determine structural white matter changes in patients with JME and to reveal the differences between the photosensitive (PS) and nonphotosensitive (NPS) subgroups by diffusion tensor imaging (DTI) using the tract-based spatial statistics (TBSS) method. METHODS: This study included data from 16 PS, 15 NPS patients with JME, and 41 healthy participants. The mean fractional anisotropy (FA) values of these groups were calculated, and comparisons were made via the TBSS method over FA values in the whole-brain and 81 regions of interest (ROI) obtained from the John Hopkins University White Matter Atlas. RESULTS: In the whole-brain TBSS analysis, no significant differences in FA values were observed in pairwise comparisons of JME patient group and subgroups with healthy controls (HCs) and in comparison between JME subgroups. In ROI-based TBSS analysis, an increase in FA values of right anterior corona radiata and left corticospinal pathways was found in JME patient group compared with HC group. When comparing JME-PS patients with HCs, an FA increase was observed in the bilateral anterior corona radiata region, whereas when comparing JME-NPS patients with HCs, an FA increase was observed in bilateral corticospinal pathway. Moreover, in subgroup comparison, an increase in FA values was noted in corpus callosum genu region in JME-PS compared with JME-NPS. CONCLUSIONS: Our results support the disruption in thalamofrontal white matter integrity in JME, and subgroups and highlight the importance of using different analysis methods to show the underlying microstructural changes.

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Juvenile myoclonic epilepsy (JME) is the most common of the generalized genetic epilepsies, with multiple causal and susceptibility genes; however, its etiopathogenesis is mainly unknown. The toxic effects caused by xenobiotics in cells occur during their metabolic transformation, mainly by enzymes belonging to cytochrome P450. The elimination of these compounds by transporters of the ABC type protects the central nervous system, but their accumulation causes neuronal damage, resulting in neurological diseases. The present study has sought the association between single nucleotide genetic variants of the CYP2C9, CYP2C19, and ABCB1 genes and the development of JME in patients compared to healthy controls. The CC1236 and GG2677 genotypes of ABCB1 in women; allele G 2677, genotypes GG 2677 and CC 3435 in men; the CYP2C19*2A allele, and the CYP2C19*3G/A genotype in both sexes were found to be risk factors for JME. Furthermore, carriers of the TTGGCC genotype combination of the ABCB1 gene (1236/2677/3435) have a 10.5 times higher risk of developing JME than non-carriers. Using the STRING database, we found an interaction between the proteins encoded by these genes and other possible proteins. These findings indicate that the CYP450 system and ABC transporters could interact with other genes in the JME.

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INTRODUCTION: Juvenile myoclonic epilepsy (JME) is an epileptic syndrome with onset in childhood and adolescence with myoclonus, absences, and generalized tonic-clonic seizures. Reflex stimuli such as sensitivity to light or photosensitivity, eyelid opening and closing, and praxis induction produce epileptiform discharges and seizures. These reflex triggers are not all systematically studied. OBJECTIVE: Examine reflex features in patients with JME. METHODS: One hundred adolescents and adults with JME who received different anti-seizure treatments were evaluated consecutively. A standard electroencephalogram was performed with an intermittent light stimulation (SLI) protocol and another for the evaluation of praxias through neurocognitive activity (CNA). The statistical analysis was descriptive and of correlation with a p > 0.05. RESULTS: Current age was 28±11 (14-67). The seizure began at 15 years ±3 (Range 8-25 years). They presented myoclonus and generalized tonic-clonic seizures in 58%. 50% received valproic acid and 31% continued with seizures. Epileptiform discharges at rest 20%; hyperventilation 30%; eyelid opening and closing 12%; photoparoxysmal response in SLI 40%; CNA 23%. Higher percentage of discharges and delay in performing CNA in those who presented seizures. Valproic acid compared to other drugs did not demonstrate superiority in seizure control. CONCLUSIONS: These findings confirm the importance of studying reflex traits for diagnosis, follow-up, and therapeutic control.

Introducción: La epilepsia mioclónica juvenil (EMJ) es un síndrome epiléptico de inicio en la infancia y adolescencia con mioclonías, convulsiones tónico-clónicas generalizadas y ausencias. Los estímulos reflejos como la sensibilidad a la luz o fotosensibilidad, la apertura y cierre palpebral y la inducción por praxias producen descargas epileptiformes y crisis. Estos desencadenantes reflejos no son todos sistemáticamente estudiados. OBJETIVO: Examinar los rasgos reflejos en pacientes con EMJ. Métodos: Se evaluaron en forma consecutiva 100 adolescentes y adultos con EMJ que recibían diferentes tratamientos anticrisis. Se realizó un electroencefalograma standard con un protocolo de estimulación luminosa intermitente (ELI) y otro para la evaluación de las praxias a través de una actividad neurocognitiva (ANC). El análisis estadístico fue descriptivo y de correlación. Se consideró significativa una p > 0.05. RESULTADOS: La edad actual fue de 28±11 (14-67). Las crisis comenzaron a los 15 años ±3 (Rango 8-25 años). EL 58% presentaron mioclonías y convulsiones tónico clónicas generalizadas. El 50% recibían ácido valproico y el 31% continuaban con crisis. Descargas epileptiformes en reposo 20%; hiperventilación 30%; apertura y cierre palpebral 12%; respuesta fotoparoxística en la ELI 40%; ANC 23%. Mayor porcentaje de descargas y demora en la realización de la ANC en los que presentaban crisis. El ácido valproico comparado con los otros fármacos no demostró superioridad en el control de las crisis. CONCLUSIONES: Estos hallazgos confirman la importancia del estudio de los rasgos reflejos para el diagnóstico, seguimiento y el control terapéutico.

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BACKGROUND: The clinical presentation of juvenile myoclonic epilepsy (JME) and epilepsy with generalized tonic-clonic seizures alone (GTCA) is similar, and MRI scans are often perceptually normal in both conditions making them challenging to differentiate. PURPOSE: To develop and validate an MRI-based radiomics model to accurately diagnose JME and GTCA, as well as to classify prognostic groups. STUDY TYPE: Retrospective. POPULATION: 164 patients (127 with JME and 37 with GTCA) patients (age 24.0 ± 9.6; 50% male), divided into training (n = 114) and test (n = 50) sets in a 7:3 ratio with the same proportion of JME and GTCA patients kept in both sets. FIELD STRENGTH/SEQUENCE: 3T; 3D T1-weighted spoiled gradient-echo. ASSESSMENT: A total of 17 region-of-interest in the brain were identified as having clinical evidence of association with JME and GTCA, from where 1581 radiomics features were extracted for each subject. Forty-eight machine-learning combinations of oversampling, feature selection, and classification algorithms were explored to develop an optimal radiomics model. The performance of the best radiomics models for diagnosis and for classification of the favorable outcome group were evaluated in the test set. STATISTICAL TESTS: Model performance measured using area under the curve (AUC) of receiver operating characteristic (ROC) curve. Shapley additive explanations (SHAP) analysis to estimate the contribution of each radiomics feature. RESULTS: The AUC (95% confidence interval) of the best radiomics models for diagnosis and for classification of favorable outcome group were 0.767 (0.591-0.943) and 0.717 (0.563-0.871), respectively. SHAP analysis revealed that the first-order and textural features of the caudate, cerebral white matter, thalamus proper, and putamen had the highest importance in the best radiomics model. CONCLUSION: The proposed MRI-based radiomics model demonstrated the potential to diagnose JME and GTCA, as well as to classify prognostic groups. MRI regions associated with JME, such as the basal ganglia, thalamus, and cerebral white matter, appeared to be important for constructing radiomics models. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.