A generalized epilepsy network derived from brain abnormalities and deep brain stimulation

Idiopathic generalized epilepsy (IGE) is a brain network disease, but the location of this network and its relevance for treatment remain unclear. We combine the locations of brain abnormalities in IGE (131 coordinates from 21 studies) with the human connectome to identify an IGE network. We validate this network by showing alignment with structural brain abnormalities previously identified in IGE and brain areas activated by generalized epileptiform discharges in simultaneous electroencephalogram-functional magnetic resonance imaging. The topography of the IGE network aligns with brain networks involved in motor control and loss of consciousness consistent with generalized seizure semiology. To investigate therapeutic relevance, we analyze data from 21 patients with IGE treated with deep brain stimulation (DBS) for generalized seizures. Seizure frequency reduced a median 90% after DBS and stimulation sites intersect an IGE network peak in the centromedian nucleus of the thalamus. Together, this study helps unify prior findings in IGE and identify a brain network target that can be tested in clinical trials of brain stimulation to control generalized seizures.

Diffusion tensor imaging for detecting biomarkers of idiopathic epilepsy in dogs

Idiopathic epilepsy (IE) is the most common neurological disease in dogs. Approximately 1/3 of dogs with IE are resistant to anti-seizure medications (ASMs). Because the diagnosis of IE is largely based on the exclusion of other diseases, it would be beneficial to indicate an IE biomarker to better understand, diagnose, and treat this disease. Diffusion tensor imaging (DTI), a magnetic resonance imaging (MRI) sequence, is used in human medicine to detect microstructural biomarkers of epilepsy. Based on the translational model between people and dogs, the use of DTI should be investigated in a veterinary context to determine if it is a viable resource for detecting microstructural white matter abnormalities in the brains of dogs with IE. As well, to determine if there are differences in white matter microstructure between dogs who are responsive to ASMs and dogs who are resistant to ASMs. Using DTI to better understand neurostructural abnormalities associated with IE and ASM resistance might help refine diagnostic approaches and treatment processes in veterinary medicine.

Latent cognitive phenotypes in juvenile myoclonic epilepsy: Clinical, sociodemographic, and neuroimaging associations

OBJECTIVE

Application of cluster analytic procedures has advanced understanding of the cognitive heterogeneity inherent in diverse epilepsy syndromes and the associated clinical and neuroimaging features. Application of this unsupervised machine learning approach to the neuropsychological performance of persons with juvenile myoclonic epilepsy (JME) has yet to be attempted, which is the intent of this investigation.

METHODS

A total of 77 JME participants, 19 unaffected siblings, and 44 unrelated controls, 12 to 25 years of age, were administered a comprehensive neuropsychological battery (intelligence, language, memory, executive function, and processing speed), which was subjected to factor analysis followed by K-means clustering of the resultant factor scores. Identified cognitive phenotypes were characterized and related to clinical, family, sociodemographic, and cortical and subcortical imaging features.

RESULTS

Factor analysis revealed three underlying cognitive dimensions (general ability, speed/response inhibition, and learning/memory), with JME participants performing worse than unrelated controls across all factor scores, and unaffected siblings performing worse than unrelated controls on the general mental ability and learning/memory factors, with no JME vs sibling differences. K-means clustering of the factor scores revealed three latent groups including above average (31.4% of participants), average (52.1%), and abnormal performance (16.4%). Participant groups differed in their distributions across the latent groups (p < 0.001), with 23% JME, 22% siblings, and 2% unrelated controls in the abnormal performance group; and 18% JME, 21% siblings, and 59% unrelated controls in the above average group. Clinical epilepsy variables were unassociated with cluster membership, whereas family factors (lower parental education) and abnormally increased thickness and/or volume in the frontal, parietal, and temporal-occipital regions were associated with the abnormal cognition group.

SIGNIFICANCE

Distinct cognitive phenotypes characterize the spectrum of neuropsychological performance of patients with JME for which there is familial (sibling) aggregation. Phenotypic membership was associated with parental (education) and imaging characteristics (increased cortical thickness and volume) but not basic clinical seizure features.

Importance of Genetic Testing in Children With Generalized Epilepsy

INTRODUCTION

Epilepsy is a neurological disorder characterized by the predisposition for recurrent unprovoked seizures. It can broadly be classified as focal, generalized, unclassified, and unknown in its onset. Focal epilepsy originates in and involves networks localized to one region of the brain. Generalized epilepsy engages broader, more diffuse networks. The etiology of epilepsy can be structural, genetic, infectious, metabolic, immune, or unknown. Many generalized epilepsies have presumed genetic etiologies. The aim of this study is to compare the role of genetic testing to brain MRI as diagnostic tools for identifying the underlying causes of idiopathic (genetic) generalized epilepsy (IGE).

METHODS

 We evaluated the diagnostic yield of these two categories in children diagnosed with IGE. Data collection was completed using ICD10 codes filtered by TriNetX to select 982 individual electronic medical records (EMRs) of children in the Penn State Children's Hospital who received a diagnosis of IGE. The diagnosis was confirmed after reviewing the clinical history and electroencephalogram (EEG) data for each patient.

RESULTS

From this dataset, neuroimaging and genetic testing results were gathered. A retrospective chart review was done on 982 children with epilepsy, of which 143 (14.5%) met the criteria for IGE. Only 18 patients underwent genetic testing. Abnormalities that could be a potential cause for epilepsy were seen in 72.2% (13/18) of patients with IGE and abnormal genetic testing, compared to 30% (37/123) for patients who had a brain MRI with genetic testing.

CONCLUSION

This study suggests that genetic testing may be more useful than neuroimaging for identifying an etiological diagnosis of pediatric patients with IGE.

MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer's disease, and healthy controls

BACKGROUND

Radiological identification of temporal lobe epilepsy (TLE) is crucial for diagnosis and treatment planning. TLE neuroimaging abnormalities are pervasive at the group level, but they can be subtle and difficult to identify by visual inspection of individual scans, prompting applications of artificial intelligence (AI) assisted technologies.

METHOD

We assessed the ability of a convolutional neural network (CNN) algorithm to classify TLE vs. patients with AD vs. healthy controls using T1-weighted magnetic resonance imaging (MRI) scans. We used feature visualization techniques to identify regions the CNN employed to differentiate disease types.

RESULTS

We show the following classification results: healthy control accuracy = 81.54% (SD = 1.77%), precision = 0.81 (SD = 0.02), recall = 0.85 (SD = 0.03), and F1-score = 0.83 (SD = 0.02); TLE accuracy = 90.45% (SD = 1.59%), precision = 0.86 (SD = 0.03), recall = 0.86 (SD = 0.04), and F1-score = 0.85 (SD = 0.04); and AD accuracy = 88.52% (SD = 1.27%), precision = 0.64 (SD = 0.05), recall = 0.53 (SD = 0.07), and F1 score = 0.58 (0.05). The high accuracy in identification of TLE was remarkable, considering that only 47% of the cohort had deemed to be lesional based on MRI alone. Model predictions were also considerably better than random permutation classifications (p < 0.01) and were independent of age effects.

CONCLUSIONS

AI (CNN deep learning) can classify and distinguish TLE, underscoring its potential utility for future computer-aided radiological assessments of epilepsy, especially for patients who do not exhibit easily identifiable TLE associated MRI features (e.g., hippocampal sclerosis).

Cortical morphometric vulnerability to generalised epilepsy reflects chromosome- and cell type-specific transcriptomic signatures

AIMS

Generalised epilepsy is thought to involve distributed brain networks. However, the molecular and cellular factors that render different brain regions more vulnerable to epileptogenesis remain largely unknown. We aimed to investigate epilepsy-related morphometric similarity network (MSN) abnormalities at the macroscale level and their relationships with microscale gene expressions at the microscale level.

METHODS

We compared the MSN of genetic generalised epilepsy with generalised tonic-clonic seizure patients (GGE-GTCS, n = 101) to demographically matched healthy controls (HC, n = 150). Cortical MSNs were estimated by combining seven morphometric features derived from structural magnetic resonance imaging for each individual. Regional gene expression profiles were derived from brain-wide microarray measurements provided by the Allen Human Brain Atlas.

RESULTS

GGE-GTCS patients exhibited decreased regional MSNs in primary motor, prefrontal and temporal regions and increases in occipital, insular and posterior cingulate cortices, when compared with the HC. These case-control neuroimaging differences were validated using split-half analyses and were not affected by medication or drug response effects. When assessing associations with gene expression, genes associated with GGE-GTCS-related MSN differences were enriched in several biological processes, including 'synapse organisation', 'neurotransmitter transport' pathways and excitatory/inhibitory neuronal cell types. Collectively, the GGE-GTCS-related cortical vulnerabilities were associated with chromosomes 4, 5, 11 and 16 and were dispersed bottom-up at the cellular, pathway and disease levels, which contributed to epileptogenesis, suggesting diverse neurobiologically relevant enrichments in GGE-GTCS.

CONCLUSIONS

By bridging the gaps between transcriptional signatures and in vivo neuroimaging, we highlighted the importance of using MSN abnormalities of the human brain in GGE-GTCS patients to investigate disease-relevant genes and biological processes.

Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI

BACKGROUND

Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive.

OBJECTIVES

Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures.

METHODS

A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes.

RESULTS

Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described.

CONCLUSION

Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes.

Correlations Between Structural Brain Abnormalities, Cognition and Electroclinical Characteristics in Patients With Juvenile Myoclonic Epilepsy

Objective

To explore the structural brain abnormality and its relationship with neuropsychological disorders and electroclinical characteristics in juvenile myoclonic epilepsy (JME) patients.

Methods

Sixty-seven patients diagnosed with JME and 56 healthy controls were enrolled. All subjects underwent MRI using T1-weighted 3D brain structural images with 1 mm thickness. Voxel-based morphometry (VBM) and surface-based morphometry (SBM) analyses were performed. They also underwent a series of neuropsychological tests to assess cognitive function. The correlation analyses were conducted between structural changes, neuropsychological outcomes, and electroclinical features.

Results

The gray matter concentration (GMC) was decreased in the bilateral pre-central and post-central gyrus, right anterior cingulate gyrus, left posterior orbital region, bilateral occipital regions, bilateral hippocampus and bilateral caudate nucleus in the JME groups (corrected P < 0.05). The evaluation of gray matter volume (GMV) showed significant decrease respectively in bilateral pre-central and post-central gyrus, left paracentral lobule, left orbital gyrus, left amygdala, left basal ganglia and left thalamus of JME patients (P < 0.05). The cortex thicknesses of the right inferior temporal gyrus, right insular gyrus, and right cingulate gyrus had negative correlations with the disease duration significantly. At the same time, the whole-brain white matter volume was positively associated with the course of the disease (P < 0.05). Patients with persistent abnormal EEG discharges had significantly less whole-brain gray matter volume than JME patients with normal EEG (P = 0.03). Correlation analyses and linear regression analyses showed that, in addition to the gray matter volumes of frontal and parietal lobe, the temporal lobe, as well as the basal ganglia and thalamus, were also significantly correlated with neuropsychological tests' results (P < 0.05).

Conclusion

The JME patients showed subtle structural abnormalities in multiple brain regions that were not only limited to the frontal lobe but also included the thalamus, basal ganglia, parietal lobe, temporal lobe and some occipital cortex, with significant involvement of the primary somatosensory cortex and primary motor cortex. And we significantly demonstrated a correlation between structural abnormalities and cognitive impairment. In addition, the course of disease and abnormal discharges had a specific negative correlation with the structural changes.

Neuroimaging and thalamic connectomics in epilepsy neuromodulation

Neuromodulation is an increasingly utilized therapy for the treatment of people with drug-resistant epilepsy. To date, the most common and effective target has been the thalamus, which is known to play a key role in multiple forms of epilepsy. Neuroimaging has facilitated rapid developments in the understanding of functional targets, surgical and programming techniques, and the effects of thalamic stimulation. In this review, the role of neuroimaging in neuromodulation is explored. First, the structural and functional changes of the thalamus in common epilepsy syndromes are discussed as the rationale for neuromodulation of the thalamus. Next, methods for imaging different thalamic nuclei are presented, as well as rationale for the need of direct surgical targeting rather than reliance on traditional stereotactic coordinates. Lastly, we discuss the potential role of neuroimaging in assessing the effects of thalamic stimulation and as a potential biomarker for neuromodulation outcomes.

Neurobehavioural comorbidities of epilepsy: towards a network-based precision taxonomy

Cognitive and behavioural comorbidities are prevalent in childhood and adult epilepsies and impose a substantial human and economic burden. Over the past century, the classic approach to understanding the aetiology and course of these comorbidities has been through the prism of the medical taxonomy of epilepsy, including its causes, course, characteristics and syndromes. Although this 'lesion model' has long served as the organizing paradigm for the field, substantial challenges to this model have accumulated from diverse sources, including neuroimaging, neuropathology, neuropsychology and network science. Advances in patient stratification and phenotyping point towards a new taxonomy for the cognitive and behavioural comorbidities of epilepsy, which reflects the heterogeneity of their clinical presentation and raises the possibility of a precision medicine approach. As we discuss in this Review, these advances are informing the development of a revised aetiological paradigm that incorporates sophisticated neurobiological measures, genomics, comorbid disease, diversity and adversity, and resilience factors. We describe modifiable risk factors that could guide early identification, treatment and, ultimately, prevention of cognitive and broader neurobehavioural comorbidities in epilepsy and propose a road map to guide future research.

Altered gray matter volume in MRI-negative focal to bilateral tonic-clonic seizures

To investigate cortical changes in MRI-negative patients with focal to bilateral tonic-clonic seizures (FBTCS). High-resolution three-dimensional T1-weighted MRI were collected with a GE 3.0-T MRI scanner from 26 patients with FBTCS and 21 healthy volunteers at Nanjing Brain Hospital. Voxel-based morphometry was performed on T1-weighted MRI of all subjects. A two-sample t test was performed to compare the GMV of two groups. Age and gender were taken as covariables, so that brain regions with significant differences, as compared by two-sample t test, between the two group were obtained. These regions were extracted as the regions of interest (ROIs) used for correlation analysis between ROIs and clinical variables. There is no significant difference in GMF between two groups. In FBTCS, regions with decreased GMV are bilateral thalamus, bilateral orbitofrontal cortex, left medical cingulate gyrus, and right supplementary motor area. GMV is increased within the bilateral para-hippocampal regions (voxel-wise FDR-corrected, P < 0.05). The GMVs are significantly negatively correlated with disease duration in the left thalamus and the left para-hippocampal region (P < 0.05). Seizures may lead to the loss of neurons and the decrease of GMV in FBTCS. The increase of GMV in some regions might be due to inflammatory responses in the early stages of epileptic seizures.

Probabilistic mapping of thalamic nuclei and thalamocortical functional connectivity in idiopathic generalised epilepsy

It is well established that abnormal thalamocortical systems play an important role in the generation and maintenance of primary generalised seizures. However, it is currently unknown which thalamic nuclei and how nuclear‐specific thalamocortical functional connectivity are differentially impacted in patients with medically refractory and non‐refractory idiopathic generalised epilepsy (IGE). In the present study, we performed structural and resting‐state functional magnetic resonance imaging (MRI) in patients with refractory and non‐refractory IGE, segmented the thalamus into constituent nuclear regions using a probabilistic MRI segmentation method and determined thalamocortical functional connectivity using seed‐to‐voxel connectivity analyses. We report significant volume reduction of the left and right anterior thalamic nuclei only in patients with refractory IGE. Compared to healthy controls, patients with refractory and non‐refractory IGE had significant alterations of functional connectivity between the centromedian nucleus and cortex, but only patients with refractory IGE had altered cortical connectivity with the ventral lateral nuclear group. Patients with refractory IGE had significantly increased functional connectivity between the left and right ventral lateral posterior nuclei and cortical regions compared to patients with non‐refractory IGE. Cortical effects were predominantly located in the frontal lobe. Atrophy of the anterior thalamic nuclei and resting‐state functional hyperconnectivity between ventral lateral nuclei and cerebral cortex may be imaging markers of pharmacoresistance in patients with IGE. These structural and functional abnormalities fit well with the known importance of thalamocortical systems in the generation and maintenance of primary generalised seizures, and the increasing recognition of the importance of limbic pathways in IGE.

Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials

Purpose

Most techniques used for automatic segmentation of subcortical brain regions are developed for three-dimensional (3D) MR images. MRIs obtained in non-specialist hospitals may be non-isotropic and two-dimensional (2D). Automatic segmentation of 2D images may be challenging and represents a lost opportunity to perform quantitative image analysis. We determine the performance of a modified subcortical segmentation technique applied to 2D images in patients with idiopathic generalised epilepsy (IGE).

Methods

Volume estimates were derived from 2D (0.4 × 0.4 × 3 mm) and 3D (1 × 1x1mm) T1-weighted acquisitions in 31 patients with IGE and 39 healthy controls. 2D image segmentation was performed using a modified FSL FIRST (FMRIB Integrated Registration and Segmentation Tool) pipeline requiring additional image reorientation, cropping, interpolation and brain extraction prior to conventional FIRST segmentation. Consistency between segmentations was assessed using Dice coefficients and volumes across both approaches were compared between patients and controls. The influence of slice thickness on consistency was further assessed using 2D images with slice thickness increased to 6 mm.

Results

All average Dice coefficients showed excellent agreement between 2 and 3D images across subcortical structures (0.86–0.96). Most 2D volumes were consistently slightly lower compared to 3D volumes. 2D images with increased slice thickness showed lower agreement with 3D images with lower Dice coefficients (0.55–0.83). Significant volume reduction of the left and right thalamus and putamen was observed in patients relative to controls across 2D and 3D images.

Conclusion

Automated subcortical volume estimation of 2D images with a resolution of 0.4 × 0.4x3mm using a modified FIRST pipeline is consistent with volumes derived from 3D images, although this consistency decreases with an increased slice thickness. Thalamic and putamen atrophy has previously been reported in patients with IGE. Automated subcortical volume estimation from 2D images is feasible and most reliable at using in-plane acquisitions greater than 1 mm x 1 mm and provides an opportunity to perform quantitative image analysis studies in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00234-021-02811-x.

Antisaccades and memory-guided saccades in genetic generalized epilepsy and temporal lobe epilepsy

OBJECTIVE

Oculomotor tasks can be used to measure volitional control of behavior sensitive to frontal dysfunction. This study aimed to examine the saccadic eye movement in Genetic Generalized Epilepsy (GGE) which could correlate with the abnormality of the frontal lobe or the thalamo-frontal network.

METHODS

Twenty-one patients with GGE were compared with 22 patients with Temporal Lobe Epilepsy (TLE) and 39 healthy controls. Visual-guided saccades, Antisaccades, and Memory-guided saccades as oculomotor tasks were performed using a novel gaze-tracker designed for clinical practice use.

RESULTS

Patients with epilepsy (either GEE or TLE) had similar latency, accuracy, and velocity in visual-guided saccades and memory-guided saccades. Patients with epilepsy had similar latencies and correct antisaccade number. However, healthy volunteers, matched by age, had faster responses and more accurate results than patients with epilepsy.

CONCLUSIONS

Our investigations did not reveal differences between TLE and GGE patients' groups in visually guided saccades, antisaccades, and memory-guided saccades, thus suggesting that the frontal cortical mechanisms responsible for them are not explicitly impaired in patients with GGE.

Structural brain abnormalities in epilepsy with myoclonic atonic seizures

OBJECTIVE

Epilepsy with myoclonic atonic seizure (EMAS) occurs in young children with previously normal to subnormal development. The outcome ranges from seizure freedom with preserved cognitive abilities to refractory epilepsy with intellectual disability (ID). Routine brain imaging typically shows no abnormalities. We aimed to compare the brain morphometry of EMAS patients with healthy subjects several years after epilepsy onset, and to correlate it to epilepsy severity and cognitive findings.

METHODS

Fourteen EMAS patients (4 females, 5-14 years) and 14 matched healthy controls were included. Patients were classified into three outcome groups (good, intermediate, poor) according to seizure control and cognitive and behavioral functioning. Individual anatomical data (T1-weighted sequence) were processed using the FreeSurfer pipeline. Cortical volume (CV), cortical thickness (CT), local gyrification index (LGI), and subcortical volumes were used for group-comparison and linear regression analyses.

RESULTS

Morphometric comparison between EMAS patients and healthy controls revealed that patients have 1) reduced CV in frontal, temporal and parietal lobes (p = <.001; 0.009 and 0.024 respectively); 2) reduced CT and LGI in frontal lobes (p = 0.036 and 0.032 respectively); and 3) a neat cerebellar volume reduction (p = 0.011). Neither the number of anti-seizure medication nor the duration of epilepsy was related to cerebellar volume (both p > 0.62). Poor outcome group was associated with lower LGI. Patients in good and intermediate outcome groups had a comparable LGI to their matched healthy controls (p > 0.27 for all lobes).

CONCLUSIONS

Structural brain differences were detectable in our sample of children with EMAS, mainly located in the frontal lobes and cerebellum. These findings are similar to those found in patients with genetic/idiopathic generalized epilepsies. Outcome groups correlated best with LGI. Whether these anatomical changes reflect genetically determined abnormal neuronal networks or a consequence of sustained epilepsy remains to be solved with prospective longitudinal studies.

Network Substrates of Centromedian Nucleus Deep Brain Stimulation in Generalized Pharmacoresistant Epilepsy

Deep brain stimulation (DBS), specifically thalamic DBS, has achieved promising results to reduce seizure severity and frequency in pharmacoresistant epilepsies, thereby establishing it for clinical use. The mechanisms of action are, however, still unknown. We evidenced the brain networks directly modulated by centromedian (CM) nucleus-DBS and responsible for clinical outcomes in a cohort of patients uniquely diagnosed with generalized pharmacoresistant epilepsy. Preoperative imaging and long-term (2-11 years) clinical data from ten generalized pharmacoresistant epilepsy patients (mean age at surgery = 30.8 ± 5.9 years, 4 female) were evaluated. Volume of tissue activated (VTA) was included as seeds to reconstruct the targeted network to thalamic DBS from diffusion and functional imaging data. CM-DBS clinical outcome improvement (> 50%) appeared in 80% of patients and was tightly related to VTAs interconnected with a reticular system network encompassing sensorimotor and supplementary motor cortices, together with cerebellum/brainstem. Despite methodological differences, both structural and functional connectomes revealed the same targeted network. Our results demonstrate that CM-DBS outcome in generalized pharmacoresistant epilepsy is highly dependent on the individual connectivity profile, involving the cerebello-thalamo-cortical circuits. The proposed framework could be implemented in future studies to refine stereotactic implantation or the parameters for individualized neuromodulation.

Eyelid myoclonia with absences, intellectual disability and attention deficit hyperactivity disorder: a clinical phenotype of the RORB gene mutation

Eyelid myoclonia with absences is recently included in the category of childhood epileptic syndromes. It is clinically characterized by brief seizures of eyelid myoclonia, sometimes followed by absences, and it is associated to EEG generalized discharges of polyspikes or polyspike-waves, which are triggered by eyes closure in a well-lit room. This epileptic syndrome probably has a genetic origin, as well as other genetic generalized epilepsies, in particular photosensitive epilepsies. We describe the case of a patient affected by eyelid myoclonia with absences, intellectual disability, and attention deficit hyperactivity disorder (ADHD), with a de novo mutation of the RORB gene (retinoid-related orphan receptor β); this gene is involved in vivo in different neuronal processes among which are migration and differentiation. We suggest that its mutation in our patient can be considered the cause of the aberrant functioning of the cerebral cortex, which is clinically expressed by epilepsy and neurodevelopment disorders.

Microstructural features of the cerebral cortex: Implications for predicting epilepsy relapse after drug withdrawal

A considerable portion of patients with well-controlled seizures and visually normal brain structures experience seizure recurrence after anti-seizure medication is withdrawn. Microstructural abnormalities of the cortex may play an essential role in epilepsy relapse. Patients with idiopathic/cryptogenic epilepsy were registered. At the follow-up endpoint, 18 patients with relapse (PR+ group), 20 patients without relapse (PR- group), and 30 healthy controls were included. High-resolution T1-weighted images were obtained at the time of drug withdrawal. Microstructural features including cortical thickness, surface area, cortical volume and mean curvature in 68 cortical areas were calculated. A general linear model was applied to investigate intergroup differences, and then post hoc analysis was performed. Additionally, factor analysis was conducted to extract components from imaging measures showing a difference between PR- and PR+ groups, and independent associations between components and epilepsy relapse were assessed using a logistic regression model. Cortical thickness of the left paracentral lobule, left temporal pole and right superior frontal gyrus; surface area of the bilateral lingual gyrus and bilateral pericalcarine cortex; and cortical volume of the bilateral pericalcarine cortex had significant intergroup differences (false discovery rate correction, P < 0.05). All measures, except for cortical thickness of the left temporal pole, showed differences between PR- and PR+ groups. Two dominant components were extracted from these measures, and both were independently associated with epilepsy relapse. In conclusion, epilepsy patients with relapse presented distinct microstructural features of cortex at the time of drug withdrawal, which may serve as a potential biomarker for predicting seizure recurrence.

Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy

This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis. The resulting cognitive subgroups were compared in regard to sociodemographic and clinical epilepsy characteristics as well as variations in brain structure and functional connectivity. Three cognitive subgroups were identified (intact, language/memory/executive function impairment, generalized impairment) which differed significantly, in a systematic fashion, across multiple features. The generalized impairment group was characterized by an earlier age at medication initiation (P < 0.05), fewer patient (P < 0.001) and parental years of education (P < 0.05), greater racial diversity (P < 0.05), and greater number of lifetime generalized seizures (P < 0.001). The three groups also differed in an orderly manner across total intracranial (P < 0.001) and bilateral cerebellar cortex volumes (P < 0.01), and rate of bilateral hippocampal atrophy (P < 0.014), but minimally in regional measures of cortical volume or thickness. In contrast, large-scale patterns of cortical-subcortical covariance networks revealed significant differences across groups in global and local measures of community structure and distribution of hubs. Resting-state fMRI revealed stepwise anomalies as a function of cluster membership, with the most abnormal patterns of connectivity evident in the generalized impairment group and no significant differences from controls in the cognitively intact group. Overall, the distinct underlying cognitive phenotypes of temporal lobe epilepsy harbor systematic relationships with clinical, sociodemographic and neuroimaging correlates. Cognitive phenotype variations in patient and familial education and ethnicity, with linked variations in total intracranial volume, raise the question of an early and persisting socioeconomic-status related neurodevelopmental impact, with additional contributions of clinical epilepsy factors (e.g., lifetime generalized seizures). The neuroimaging features of cognitive phenotype membership are most notable for disrupted large scale cortical-subcortical networks and patterns of functional connectivity with bilateral hippocampal and cerebellar atrophy. The cognitive taxonomy of temporal lobe epilepsy appears influenced by features that reflect the combined influence of socioeconomic, neurodevelopmental and neurobiological risk factors.

Macroscale and microcircuit dissociation of focal and generalized human epilepsies

Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at the level of whole-brain mechanisms. Using multimodal imaging, connectomics, and computational simulations, we examined thalamo-cortical and cortico-cortical signatures and underlying microcircuits in 96 genetic generalized (GE) and 107 temporal lobe epilepsy (TLE) patients, along with 65 healthy controls. Structural and functional network profiling highlighted extensive atrophy, microstructural disruptions and decreased thalamo-cortical connectivity in TLE, while GE showed only subtle structural anomalies paralleled by enhanced thalamo-cortical connectivity. Connectome-informed biophysical simulations indicated modest increases in subcortical drive contributing to cortical dynamics in GE, while TLE presented with reduced subcortical drive and imbalanced excitation-inhibition within limbic and somatomotor microcircuits. Multiple sensitivity analyses supported robustness. Our multiscale analyses differentiate human focal and generalized epilepsy at the systems-level, showing paradoxically more severe microcircuit and macroscale imbalances in the former.

Impaired Topological Properties of Gray Matter Structural Covariance Network in Epilepsy Children With Generalized Tonic-Clonic Seizures: A Graph Theoretical Analysis

Modern network science has provided exciting new opportunities for understanding the human brain as a complex network of interacting regions. The improved knowledge of human brain network architecture has made it possible for clinicians to detect the network changes in neurological diseases. Generalized tonic–clonic seizure (GTCS) is a subtype of epilepsy characterized by generalized spike-wave discharge involving the bilateral hemispheres during seizure. Network researches in adults with GTCS exhibited that GTCS can be conceptualized as a network disorder. However, the overall organization of the brain structural covariance network in children with GTCS remains largely unclear. Here, we used a graph theory method to assess the gray matter structural covariance network organization of 14 pediatric patients diagnosed with GTCS and 29 healthy control children. The group differences in regional and global topological properties were investigated. Results revealed significant changes in nodal betweenness locating in brain regions known to be abnormal in GTCS (the right thalamus, bilateral temporal pole, and some regions of default mode network). The network hub analysis results were in accordance with the regional betweenness, which presented a disrupted regional topology of structural covariance network in children with GTCS. To our knowledge, the present study is the first work reporting the changes of structural topological properties in children with GTCS. The findings contribute new insights into the understanding of the neural mechanisms underlying GTCS and highlight critical regions for future neuroimaging research in children with GTCS.

Cognitive Function in Genetic Generalized Epilepsies: Insights From Neuropsychology and Neuroimaging

Genetic generalized epilepsies (GGE), previously called idiopathic generalized epilepsies, constitute about 20% of all epilepsies, and include childhood absence epilepsy, juvenile absence epilepsy, juvenile myoclonic epilepsy, and epilepsy with generalized tonic-clonic seizures alone (CAE, JAE, JME, and GGE-GTCS, respectively). GGE are characterized by high heritability, likely underlain by polygenetic mechanisms, which may relate to atypical neurodevelopmental trajectories. Age of onset ranges from pre-school years, for CAE, to early adulthood for GGE-GTCS. Traditionally, GGE have been considered benign, a belief contrary to evidence from neuropsychology studies conducted over the last two decades. In JME, deficits in executive and social functioning are common findings and relate to impaired frontal lobe function. Studies using neuropsychological measures and cognitive imaging paradigms provide evidence for hyperconnectivity between prefrontal and motor cortices, aberrant fronto-thalamo-cortical connectivity, and reduced fronto-cortical and subcortical gray matter volumes, which are associated with altered cognitive performance. Recent research has also identified associations between abnormal hippocampal morphometry and fronto-temporal activation during episodic memory. Longitudinal studies on individuals with newly diagnosed JME have observed cortical dysmaturation, which is paralleled by delayed cognitive development compared to the patients' peers. Comorbidities and cognitive deficits observed in other GGE subtypes, such as visuo-spatial and language deficits in both CAE and JAE, have also been correlated with atypical neurodevelopment. Although it remains unclear whether cognitive impairment profiles differ amongst GGE subtypes, effects may become more pronounced with disease duration, particularly in absence epilepsies. Finally, there is substantial evidence that patients with JME and their unaffected siblings share patterns of cognitive deficits, which is indicative of an underlying genetic etiology (endophenotype), independent of seizures and anti-epileptic medication.

Children with epilepsy demonstrate macro- and microstructural changes in the thalamus, putamen, and amygdala

PURPOSE

Despite evidence for macrostructural alteration in epilepsy patients later in life, little is known about the underlying pathological or compensatory mechanisms at younger ages causing these alterations. The aim of this work was to investigate the impact of pediatric epilepsy on the central nervous system, including gray matter volume, cerebral blood flow, and water diffusion, compared with neurologically normal children.

METHODS

Inter-ictal magnetic resonance imaging data was obtained from 30 children with epilepsy ages 1-16 (73% F, 27% M). An atlas-based approach was used to determine values for volume, cerebral blood flow, and apparent diffusion coefficient in the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. These values were then compared with previously published values from 100 neurologically normal children using a MANCOVA analysis.

RESULTS

Most brain volumes of children with epilepsy followed a pattern similar to typically developing children, except for significantly larger putamen and amygdala. Cerebral blood flow was also comparable between the groups, except for the putamen, which demonstrated decreased blood flow in children with epilepsy. Diffusion (apparent diffusion coefficient) showed a trend towards higher values in children with epilepsy, with significantly elevated diffusion within the thalamus in children with epilepsy compared with neurologically normal children.

CONCLUSION

Children with epilepsy show statistically significant differences in volume, diffusion, and cerebral blood flow within their thalamus, putamen, and amygdala, suggesting that epilepsy is associated with structural changes of the central nervous system influencing brain development and potentially leading to poorer neurocognitive outcomes.

Prognostic patterns and predictors in epilepsy: a multicentre study (PRO-LONG)

OBJECTIVES

To describe the long-term prognosis of epilepsy and prognostic patterns in a large cohort of newly diagnosed patients and identify prognostic factors.

METHODS

Study participants were 13 Italian epilepsy centres with accessible records dating back to 2005 or earlier, complete data on seizure outcome and treatments, precise epilepsy diagnosis, and follow-up of at least 10 years. Records were examined by trained neurology residents for demographics, seizure characteristics, neurological signs, psychiatric comorbidity, first electroencephalogram (EEG) and MRI/CT, epilepsy type and aetiology, antiepileptic drugs (AEDs), and 1-year, 2-year, 5-year and 10-year seizure remissions. Five predefined prognostic patterns were identified: early remission, late remission, relapsing-remitting course, worsening course and no remission. Prognostic factors were assessed using multinomial logistic regression models.

RESULTS

1006 children and adults were followed for 17 892 person-years (median 16 years; range 10-57). During follow-up, 923 patients (91.7%) experienced 1-year remission. 2-year, 5-year and 10-year remissions were present in 89.5%, 77.1% and 44.4% of cases. 5-year remission was associated with one to two seizures at diagnosis, generalised epilepsy, no psychiatric comorbidity, and treatment with one or two AEDs during follow-up. 10-year remission was associated with one or two AEDs. The most common prognostic pattern was relapsing-remitting (52.2%), followed by early remission (24.5%). 8.3% of cases experienced no remission. Predictors of a relapsing-remitting course were <6 seizures at diagnosis, (presumed) genetic aetiology and no psychiatric comorbidity.

CONCLUSIONS

Few seizures at diagnosis, generalised epilepsy and no psychiatric comorbidity predict early or late seizure freedom in epilepsy. Achieving remission at any time after the diagnosis does not exclude further relapses.

Social cognition in idiopathic generalized epilepsies and potential neuroanatomical correlates

Social cognition allows us to elaborate mental representations of social relationships and use them appropriately in a social environment. One of its main attributes is the so-called Theory of Mind (ToM), which consists of the ability to attribute beliefs, intentions, emotions, and feelings to self and others. Investigating social cognition may help understand the poor social outcome often experienced by persons with Idiopathic Generalized Epilepsies (IGE), who otherwise present with normal intelligence. In recent years, several studies have addressed social cognition in subjects with focal epilepsies, while literature on social cognition in IGE is scarce, and findings are often conflicting. Some studies on samples of patients with mixed IGE showed difficulties in emotion attribution tasks, which were not replicated in a homogeneous population of patients with Juvenile Myoclonic Epilepsy alone. Impairment of higher order social skills, such as those assessed by Strange Stories Test and Faux Pas Tasks, were consistently found by different studies on mixed IGE, suggesting that this may be a more distinctive IGE-associated trait, irrespective of the specific syndrome subtype. Though an interplay between social cognition and executive functions (EF) was suggested by several authors, and their simultaneous impairment was shown in several epilepsy syndromes including IGE, no formal correlations among the two domains were identified in most studies. People with IGE exhibit subtle brain structural alterations in areas potentially involved in sociocognitive functional networks, including mesial prefrontal and temporoparietal cortices, which may relate to impairment in social cognition. Heterogeneity in patient samples, mostly consisting of groups with mixed IGE, and lack of analyses in specific IGE subsyndromes, represent evident limitations of the current literature. Larger studies, focusing on specific subsyndromes and implementing standardized test batteries, will improve our understanding of sociocognitive processing in IGE. Concomitant high-resolution structural and functional neuroimaging may aid the identification of its neural correlates. This article is part of the Special Issue "Epilepsy and social cognition across the lifespan".

Peripapillary retinal nerve fibre layer thinning in genetic generalized epilepsy

PURPOSE

The purpose of this study was to compare the peripapillary retinal nerve fibre layer (RNFL) between patients with genetic generalized epilepsy (GGE) and healthy controls.

METHODS

This prospective observational study was conducted on adults aged 18-60 years. The study group comprised 26 consecutive patients who met the inclusion criteria and 26 healthy age- and sex-matched healthy adults. Peripapillary RNFL thickness was measured by spectral domain optical coherence tomography.

RESULTS

The average peripapillary RNFL thickness was significantly thinner for GGE patients (98.61 μm) than for healthy controls (104.77 μm) (p = 0.016). Similar results were obtained for the left eye. The peripapillary RFNL thickness of all quadrants was lower for GGE patients than for healthy controls, but it was significant only in the superior (p = 0.009) and inferior (p = 0.024) quadrants for both eyes.

CONCLUSIONS

Our results suggest that the peripapillary RNFL is significantly thinner in GGE patients than in healthy participants. We concluded that this microstructural feature might be an intrinsic feature of GGE.

Reduced resilience of brain gray matter networks in idiopathic generalized epilepsy: A graph-theoretical analysis

Purpose

The pathophysiology of idiopathic generalized epilepsy (IGE) is still unclear, but graph theory may help to understand it. Here, we examined the graph-theoretical findings of the gray matter network in IGE using anatomical covariance methods.

Materials and methods

We recruited 33 patients with IGE and 35 age- and sex-matched healthy controls. Gray matter images were obtained by 3.0-T 3D T1-weighted MRI and were normalized using the voxel-based morphometry tools of Statistical Parametric Mapping 12. The normalized images were subjected to graph-theoretical group comparison using the Graph Analysis Toolbox with two different parcellation schemes. Initially, we used the Automated Anatomical Labeling template, whereas the Hammers Adult atlas was used for the second analysis.

Results

The resilience analyses revealed significantly reduced resilience of the IGE gray matter networks to both random failure and targeted attack. No significant between-group differences were found in global network measures, including the clustering coefficient and characteristic path length. The IGE group showed several changes in regional clustering, including an increase mainly in wide areas of the bilateral frontal lobes. The second analysis with another region of interest (ROI) parcellation generated the same results in resilience and global network measures, but the regional clustering results differed between the two parcellation schemes.

Conclusion

These results may reflect the potentially weak network organization in IGE. Our findings contribute to the accumulation of knowledge on IGE.