Abstract
Struck AF, Garcia-Ramos C, Gjini K, Jones JE, Prabhakaran V, Adluru N, Hermann BP. Hum Brain Mapp. 2025 May;46(7):e70226. doi: 10.1002/hbm.70226 Structural neuroimaging studies of patients with Juvenile Myoclonic Epilepsy (JME) typically present two findings: (1) volume reduction of subcortical gray matter structures, and (2) abnormalities of cortical thickness. The general trend has been to observe increased cortical thickness primarily in medial frontal regions, but heterogeneity across studies is common, including reports of decreased cortical thickness. These differences have not been explained. The cohort of patients investigated here originates from the Juvenile Myoclonic Epilepsy Connectome Project, which included comprehensive neuropsychological testing, 3T MRI, and high-density 256-channel EEG. 64 JME patients aged 12–25 and 41 age and sex-matched healthy controls were included. Data-driven approaches were used to compare cortical thickness and subcortical volumes between the JME and control participants. After differences were identified, supervised machine learning was used to confirm their classification power. K-means clustering was used to generate imaging endophenotypes, which were then correlated with cognition, EEG frequency band lagged coherence from resting state high-density EEG, and white and grey matter based spatial statistics from diffusion imaging. The volumes of subcortical gray matter structures, particularly the thalamus and the motor-associated thalamic nuclei (ventral anterior), were found to be smaller in JME. In addition, the right hemisphere (primarily) sulcal pre-motor cortex was abnormally thicker in an age-dependent manner in JME with an asymmetry in the pre-motor cortical findings. These results suggested that for some patients JME may be an asymmetric disease, at least at the cortical level. Cluster analysis revealed three discrete imaging endophenotypes (left, right, symmetric). Clinically, the groups were not substantially different except for cognition, where left hemisphere disease was linked with a lower performance on a general cognitive factor (“g”). HD-EEG demonstrated statistically significant differences between imaging endophenotypes. Tract-based spatial statistics showed significant changes between endophenotypes as well. The left dominant disease group exhibited diffuse white matter changes. JME patients present with heterogeneity in underlying imaging endophenotypes that are defined by the presence and laterality of asymmetric abnormality at the level of the pre-motor sulcal cortex; these endophenotypes are linked to orderly relationships with cognition, EEG, and white matter pathology. The relationship of JME’s adolescent onset, age-dependent cortical thickness loss, and seizure upon awakening all suggest that synaptic pruning may be a key element in the pathogenesis of JME. Individualized treatment approaches for neuromodulation are needed to target the most relevant cortical and subcortical structures as well as develop disease-modifying and neuroprotective strategies.
Commentary
Juvenile myoclonic epilepsy (JME) is the most common idiopathic generalized epilepsy syndrome. It is characterized by myoclonic seizures, onset during adolescence or early adulthood, cognitive comorbidities involving fronto-subcortical networks, and a marked susceptibility to seizures upon awakening. 1 The epilepsy is often attributed to an underlying hyperexcitability due to dysfunction in ion channels. The current approach of viewing JME as a “generalized” homogeneous disorder glosses over the heterogeneity in the patient population and their clinical phenotypes. A prerequisite to move toward individualized care is to understand the diverse phenotypes of this condition, which would allow tailored treatments for the epilepsy, cognitive, and behavioral outcomes of JME.
In this study, Struck et al 2 tried to identify the different phenotypes of JME by leveraging the multimodal data from the JME Connectome project. The cohort consisted of 64 individuals with JME, aged 12–25, who underwent a magnetic resonance imaging (MRI), cognitive testing, and a 45-min EEG session using a high-density 256 electrode setup. The JME cohort was matched to age and sex-matched controls from other studies and the community.
Given prior imaging studies showing increased cortical volumes in the premotor region in JME, 3 and conversely, diminished volumes in subcortical structures, a premotor/thalamic ratio was generated by incorporating these 2 measures. The team also evaluated asymmetric differences in these regions of interest. Volumetric analysis showed higher right premotor cortical volumes and lower thalamic volumes as expected. The biggest volumetric differences were noted in younger, early adolescent individuals, raising the possibility of impaired synaptic pruning in this age group. When evaluating thalamic nuclei, the ventral anterior nucleus had the most decreased volume in JME versus controls. Additionally, there was an asymmetry noted in JME in the premotor/thalamic ratio driven by the large cortical volumes on the right in JME versus controls. Individuals exposed to valproic acid (16%) were analyzed to exclude medication effects by comparing them to those who were not on the drug. They were found to have increased right premotor volumes, but the interaction of valproic acid with age was not significant.
Using supervised machine learning methods, models incorporating these thalamic and premotor volumetric measures were able to accurately distinguish between JME and controls with an area under the curve ranging between 0.79 and 0.92. The left premotor/thalamic ratio also correlated with a general cognitive factor derived from the neuropsychological battery (ρ = −0.67, p = 0.02). Following this, the authors used a cluster-based method, which identified 3 JME imaging phenotypes largely based on the asymmetry index; these were labelled as left-lateralized, symmetric, and right-lateralized. The EEGs of the 3 newly identified groups were then compared using lagged coherence of the traditional frequency bands as a measure of connectivity. Differences arose when comparing left versus symmetric (theta, beta), symmetric versus right (alpha and beta), and left versus right (beta). Finally, the groups were also compared using diffusion-weighted imaging measures of white matter integrity and grey matter microstructure. No differences were noted in grey matter microstructure; however, the left-lateralized group had the most disorganized white matter architecture when compared to controls and the other 2 JME groups.
The study has several limitations, including a relatively small sample size, with two of the clusters comprising only 14 individuals each. The wide age range of participants is both a strength and a limitation: on the one hand, it enabled the authors to demonstrate a meaningful correlation between age and prefrontal volumes. On the other hand, because each individual may be at a different point along their neurodevelopmental trajectory, it becomes more difficult to disentangle effects specific to early adolescence versus early adulthood in JME. As expected, there was a correlation between the cognitive factor and the left premotor-to-thalamic ratio, likely reflecting the fact that most cognitive evaluations are better suited to detecting deficits associated with the language-dominant hemisphere. In contrast, nonverbal impairments—typically linked to right-hemisphere function—are often more difficult to quantify. It would be interesting to explore whether the right premotor-to-thalamic ratio correlates with neurobehavioral outcomes, such as impulse control disorders, which appear to be more prevalent in this patient population. The study was also understandably focused on frontal–subcortical structures, although other cortical areas may be of interest, especially since temporal lobe structural changes in JME have been correlated with drug-resistance and cognitive outcomes. 4
One of the most compelling aspects of this study is that it reframes JME not merely as a disorder of hyperexcitability, but as a neurodevelopmental condition involving impaired synaptic pruning. There is accumulating evidence that deficits in neuronal migration may be contributing to JME, with brain autopsy studies showing microdysgenesis 5 and genetic studies implicating genes involved in neuronal migration, such as intestinal-cell kinase and EF-Hand Domain (C-Terminal)-Containing Protein 1. 6 However, this is likely not enough to explain the age-dependent changes in cortical thickness in JME, and the early morning susceptibility to myoclonus, which is where synaptic pruning may come to play. To substantiate this hypothesis, future studies should include longitudinal imaging and direct measures of synaptic density, such as positron emission tomography ligands targeting synaptic proteins. If confirmed, this model could inform novel therapeutic approaches during critical developmental windows. For instance, given the role of sleep in synaptic upscaling and downscaling, sleep modulation may serve as a promising intervention. It would also be important to investigate whether siblings of individuals with JME may also have a synaptic pruning disorder, given that they exhibit comparable frontal–subcortical dysfunction despite being seizure-free. 7
Additionally, this study identifies three neuroimaging phenotypes within JME, including 2 lateralized profiles. These profiles were associated with different connectivity patterns on EEG, reflecting differences in thalamo-cortical circuitry. These 3 disease clusters did not differ in terms of epilepsy-related variables (medications, seizure frequency, and pharmacoresponsiveness). However, when the asymmetry index is treated as a continuous variable, there is a clear association with cognition, as the more left-skewed the asymmetry, the worse the cognitive performance. The left-lateralized cohort was also the one with the most disorganized white matter architecture. These findings show that JME is not always a perfectly symmetric disorder, which would explain a recent study showing that around 22% of generalized discharges on EEG were asymmetric, and that the asymmetry varied more across rather than within individuals. 8 In addition, focal features on EEG 9 and clinical semiology are quite common in this disorder, and it is unclear if these also contribute to the phenotype.
What does the future of JME care look like? It would ideally involve the JME connectome protocol; an EEG, MRI, and cognitive testing, leading to a clear phenotype. Neuroimaging is not currently considered relevant in management. This would then be followed by medication selection, cognitive and behavioral support, and, if the synaptic pruning hypothesis is true, interventions to address this. We are certainly not there yet, but one of the first steps is to appreciate the many faces of JME.
