Epilepsia最新文献

This study evaluated food preferences and eating behaviors of individuals with Dravet syndrome. Patients diagnosed with Dravet syndrome were recruited, as well as a control group composed of siblings of patients with epilepsy (any form). The Food Preference Questionnaire and the Child Eating Behavior Questionnaire were completed by caregivers along with two open-ended questions regarding eating challenges. Seventy-eight participants (45 with Dravet syndrome and 33 controls) were included. Compared to controls, mean scores for food preference were lower for fruits (p = .000099), meats and fish (p = .00094), and snacks (p = .000027) in Dravet syndrome. People with Dravet syndrome also had less emotional overeating (p = .0085) and food enjoyment (p = .0012), but more slowness in eating (p = .00021) and food fussiness (p = .0064). In a subgroup analysis of only pediatric (age <18 years) patients, similar results were observed for both food preferences and eating habits. In qualitative data, caregivers most commonly reported difficulties with fixation on specific foods. This study demonstrates specific food preferences and challenging eating behaviors in individuals with Dravet syndrome. These data provide potential avenues for nutritional interventions and behavioral therapies to increase the quality of life of patients and their families.

Objective: This study was undertaken to analyze the histology underlying increased T2 signal intensity (iT2SI) in anterior temporal lobe white matter (aTLWM) epilepsy due to hippocampal sclerosis (TLE/HS).

Methods: Twenty-three patients were included: 16 with increased T2 signal in the aTLWM and seven with HS only. Magnetic resonance imaging (MRI) findings were consistent across two neuroradiologists (kappa = .89, p < .001). Quantification of neuronal cells, astrocytes, oligodendrocytes, and vacuolization in the white matter of temporal lobe specimens was performed by immunohistochemistry (neuronal nuclear antigen, glial fibrillary acidic protein, oligodendrocyte transcription factor, and basic myelin protein, respectively). Surgical specimens from TLE/HS patients with and without iT2SI in the aTLWM were compared. Samples of aTLWM were divided into three groups, according to MRI features: G1 = samples of iT2SI, G2 = samples with normal T2 signal intensity from patients without white matter imaging abnormalities, and G3 = samples with normal T2 signal intensity adjacent to areas with iT2SI.

Results: Patients with increased T2 signal had a significantly younger age at epilepsy onset (p < .035). Histological analysis revealed a higher percentage of vacuolar area in these patients (p < .004) along with a lower number of ectopic neurons (p = .042). No significant differences were found in astrocyte or oligodendrocyte counts among groups.

Significance: A higher proportion of vacuoles in regions with iT2SI may be the histopathologic substrate of this signal alteration in the white matter of the temporal lobe in patients with TLE/HS. This method of quantifying vacuoles using digital image analysis proved reliable and cost-effective.

Objective: Blood-brain barrier dysfunction (BBBD) has been linked to various neurological disorders, including epilepsy. This study aims to utilize dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify and compare brain regions with BBBD in patients with epilepsy (PWE) and healthy individuals.

Methods: We scanned 50 drug-resistant epilepsy (DRE) patients and 58 control participants from four global specialized epilepsy centers using DCE-MRI. The presence and extent of BBBD were analyzed and compared between PWE and healthy controls.

Results: Both greater brain volume and higher number of brain regions with BBBD were significantly present in PWE compared to healthy controls (p < 10^-7). No differences in total brain volume with BBBD were observed in patients diagnosed with either focal seizures or generalized epilepsy, despite variations in the affected regions. Overall brain volume with BBBD did not differ in PWE with MRI-visible lesions compared with non-lesional cases. BBBD was observed in brain regions suspected to be related to the onset of seizures in 82% of patients (n = 39) and was typically identified in, adjacent to, and/or in the same hemisphere as the suspected epileptogenic lesion (n = 10).

Significance: These findings are consistent with pre-clinical studies that highlight the role of BBBD in the development of DRE and identify microvascular stabilization as a potential therapeutic strategy.

Objective: Anterior temporal lobe resection (ATLR) is an effective treatment for drug-resistant temporal lobe epilepsy (TLE), although language deficits may occur after both left and right ATLR. Functional reorganization of the language network has been observed in the ipsilateral and contralateral hemispheres within 12 months after ATLR, but little is known of longer-term plasticity effects. Our aim was to examine the plasticity of language functions up to a decade after ATLR, in relation to cognitive profiles.

Methods: We examined 24 TLE patients (12 left [LTLE]) and 10 controls across four time points: pre-surgery, 4 months, 12 months, and ~9 years post-ATLR. Participants underwent standard neuropsychological assessments (naming, phonemic, and categorical fluency tests) and a verbal fluency functional magnetic resonance imaging (fMRI) task. Using a flexible factorial design, we analyzed longitudinal fMRI activations from 12 months to ~9 years post-ATLR, relative to controls, with separate analyses for people with hippocampal sclerosis (HS). Change in cognitive profiles was correlated with the long-term change in fMRI activations to determine the "efficiency" of reorganized networks.

Results: LTLE patients had increased long-term engagement of the left extra-temporal and contralateral temporal regions, with better language performance linked to bilateral activation. Those with HS exhibited more widespread bilateral activations. RTLE patients showed plasticity in the left extra-temporal regions, with better language outcomes associated with these areas. Both groups of patients achieved cognitive stability over 9 years, with more than 50% of LTLE patients improving. Older age, longer epilepsy duration, and lower pre-operative cognitive reserve negatively affected long-term language performance.

Significance: Neuroplasticity continues for up to ~9 years post-epilepsy surgery in LTLE and RTLE, with effective language recovery linked to bilateral engagement of temporal and extra-temporal regions. This adaptive reorganization is associated with improved cognitive outcomes, challenging the traditional view of localized surgery effects. These findings emphasize the need for early intervention, tailored pre-operative counseling, and the potential for continued cognitive gains with extended post-ATLR rehabilitation.

Objective: This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features.

Methods: We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight. QEEG features included statistical moments, spectral power, and recurrence quantification analysis (RQA) features. We trained and evaluated Neo-GB on a University of California, San Francisco (UCSF) neonatal HIE dataset, augmenting training with publicly available neonatal electroencephalogram (EEG) datasets from Cork University and Helsinki University Hospitals. We assessed the performance of Neo-GB at providing dynamic and static forecasts with diagnostic performance metrics and incident/dynamic area under the receiver operating characteristic curve (iAUC) analyses. Model explanations were performed to assess contributions of QEEG features and channels to model predictions.

Results: The UCSF dataset included 60 neonates with HIE (30 with seizures). In subject-level static forecasting at 30 min after EEG initiation, baseline Neo-GB without time-dependent features had an area under the receiver operating characteristic curve (AUROC) of .76 and Neo-GB with time-dependent features had an AUROC of .89. In time-dependent evaluation of the initial seizure onset within a 24-h seizure occurrence period, dynamic forecast with Neo-GB demonstrated median iAUC = .79 (interquartile range [IQR] .75-.82) and concordance index (C-index) = .82, whereas baseline static forecast at 30 min demonstrated median iAUC = .75 (IQR .72-.76) and C-index = .69. Model explanation analysis revealed that spectral power, PMA, RQA, and cord blood gas values made the strongest contributions in driving Neo-GB predictions. Within the most influential EEG channels, as the preictal period advanced toward eventual seizure, there was an upward trend in broadband spectral power.

Significance: This study demonstrates an ML model that combines QEEG with clinical features to forecast time-dependent risk of initial seizure onset in neonatal HIE. Spectral power evolution is an early EEG marker of seizure risk in neonatal HIE.

Objective: To investigate phenotypes of comorbidity before and after an epilepsy diagnosis in a national cohort of post-9/11 Service Members and Veterans and explore phenotypic associations with mortality.

Methods: Among a longitudinal cohort of Service Members and Veterans receiving care in the Veterans Health Administration (VHA) from 2002 to 2018, annual diagnoses for 26 conditions associated with epilepsy were collected over 5 years, ranging from 2 years prior to 2 years after the year of first epilepsy diagnosis. Latent class analysis (LCA) was used to identify probabilistic comorbidity phenotypes with distinct health trajectories. Descriptive statistics were used to describe the characteristics of each phenotype. Fine and Gray cause-specific survival models were used to measure mortality outcomes for each phenotype up to 2021.

Results: Six distinct phenotypes were identified: (1) relatively healthy, (2) post-traumatic stress disorder, (3) anxiety and depression, (4) chronic disease, (5) bipolar/substance use disorder, and (6) polytrauma. Accidents were the most common cause of death overall, followed by suicide/mental health and cancer, respectively. Each phenotype exhibited unique associations with mortality and cause of death, highlighting the differential impact of comorbidity patterns on patient outcomes.

Significance: By delineating clinically meaningful epilepsy comorbidity phenotypes, this study offers a framework for clinicians to tailor interventions. Moreover, these data support systems of care that facilitate treatment of epilepsy and comorbidities within an interdisciplinary health team that allows continuity of care. Targeting treatment toward patients with epilepsy who present with specific heightened risks could help mitigate adverse outcomes and enhance overall patient care.

Cholesterol is a critical molecule in the central nervous system, and imbalances in the synthesis and metabolism of brain cholesterol can result in a range of pathologies, including those related to hyperexcitability. The impact of cholesterol on disorders of epilepsy and developmental and epileptic encephalopathies is an area of growing interest. Cholesterol cannot cross the blood-brain barrier, and thus the brain synthesizes and metabolizes its own pool of cholesterol. The primary metabolic enzyme for brain cholesterol is cholesterol 24-hydroxylase (CH24H), which metabolizes cholesterol into 24S-hydroxycholesterol (24HC). Dysregulation of CH24H and 24HC can affect neuronal excitability through a range of mechanisms. 24HC is a positive allosteric modulator of N-methyl-D-aspartate (NMDA) receptors and can increase glutamate release via tumor necrosis factor-α-dependent pathways. Increasing cholesterol metabolism can lead to dysfunction of excitatory amino acid transporter 2 and impair glutamate reuptake. Finally, overstimulation of NMDA receptors can further activate metabolism of cholesterol, leading to a vicious cycle of overactivation. All of these mechanisms increase extracellular glutamate and can lead to hyperexcitability. For these reasons, the cholesterol pathway represents a new potential mechanistic target for antiseizure medications. CH24H inhibition has been shown to decrease seizure behavior and improve survival in multiple animal models of epilepsy and could be a promising new mechanism of action for the treatment of neuronal hyperexcitability and developmental and epileptic encephalopathies.

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.

Objective: For the pre-surgical evaluation of patients with drug-resistant focal epilepsy, stereo-electroencephalographic (SEEG) signals are routinely recorded to identify the epileptogenic zone network (EZN). This network consists of remote brain regions involved in seizure initiation. However, the pathophysiological mechanisms underlying typical SEEG patterns that occur during the transition from interictal to ictal activity in distant brain nodes of the EZN remain poorly understood. The primary aim is to identify and explain these mechanisms using a novel physiologically-plausible model of the EZN.

Methods: We analyzed SEEG signals recorded from the EZN in 10 patients during the transition from interictal to ictal activity. This transition consisted of a sequence of periods during which SEEG signals from distant neocortical regions showed stereotypical patterns of activity: sustained preictal spiking activity preceding a fast activity occurring at seizure onset, followed by the ictal activity. Spectral content and non-linear correlation of SEEG signals were analyzed. In addition, we developed a novel neuro-inspired computational model consisting of bidirectionally coupled neuronal populations.

Results: The proposed model captured the essential characteristics of the patient signals, including the quasi-synchronous onset of rapid discharges in distant interconnected epileptogenic zones. Statistical analysis confirmed the dynamic correlation/de-decorrelation pattern observed in the patient signals and accurately reproduced in the simulated signals.

Significance: This study provides insight into the abnormal dynamic changes in glutamatergic and γ-aminobutyric acid (GABA)ergic synaptic transmission that occur during the transition to seizures. The results strongly support the hypothesis that bidirectional connections between distant neuronal populations of the EZN (from pyramidal cells to vaso-intestinal peptide-positive interneurons) play a key role in this transition, while parvalbumin-positive interneurons intervene in the emergence of rapid discharges at seizure onset.

Objective: Epilepsy and intellectual disability are common in tuberous sclerosis complex (TSC). Although early life seizures and intellectual disability are known to be correlated in TSC, the differential effects of age at seizure onset and accumulated seizure burden on development remain unclear.

Methods: Daily seizure diaries, serial neurodevelopmental testing, and brain magnetic resonance imaging were analyzed for 129 TSC patients followed from 0 to 36 months. We used machine learning to identify subgroups of patients based on neurodevelopmental test scores at 36 months of age and assessed the stability of those subgroups at 12 months. We tested the ability of candidate biomarkers to predict 36-month neurodevelopmental subgroup using univariable and multivariable logistic regression. Candidate biomarkers included age at seizure onset, accumulated seizure burden, tuber volume, sex, and earlier neurodevelopmental test scores.

Results: Patients clustered into two neurodevelopmental subgroups at 36 months of age, higher and lower scoring. Subgroup was mostly (75%) the same at 12 months. Significant univariable effects on subgroup were seen only for accumulated seizure burden (largest effect), earlier test scores, and tuber volume. Neither age at seizure onset nor sex significantly distinguished 36-month subgroups, although for girls but not boys there was a significant effect of age at seizure onset. In the multivariable model, accumulated seizure burden and earlier test scores together predicted 36-month neurodevelopmental group with 82% accuracy and an area under the curve of .86.

Significance: These results untangle the contributions of age at seizure onset and accumulated seizure burden to neurodevelopmental outcomes in young children with TSC. Accumulated seizure burden, rather than the age at seizure onset, most accurately predicts neurodevelopmental outcome at 36 months of age. These results emphasize the need to manage seizures aggressively during the first 3 years of life for patients with TSC, not only to promote seizure control but to optimize cognitive function.