Faculty Bibliography

Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain's ability to discover regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words), while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.

Epilepsy is a heterogenous group of disorders defined by recurrent seizure activity due to abnormal synchronized activity of neurons. A growing number of epilepsy cases are believed to be caused by genetic factors and copy number variants (CNV) contribute to up to 5% of epilepsy cases. However, CNVs in epilepsy are usually large deletions or duplications involving multiple neurodevelopmental genes. In patients who underwent seizure focus resection for treatment-resistant epilepsy, whole genome DNA methylation profiling identified 3 main clusters of which one showed strong association with receptor tyrosine kinase (RTK) genes. We identified focal copy number gains involving epidermal growth factor receptor (EGFR) and PDGFRA loci. The dysplastic neurons of cases with amplifications showed marked overexpression of EGFR and PDGFRA, while glial and endothelial cells were negative. Targeted sequencing of regulatory regions and DNA methylation analysis revealed that only enhancer regions of EGFR and gene promoter of PDGFRA were amplified, while coding regions did not show copy number abnormalities or somatic mutations. Somatic focal copy number gains of noncoding regulatory represent a previously unrecognized genetic driver in epilepsy and a mechanism of abnormal activation of RTK genes. Upregulated RTKs provide a potential avenue for therapy in seizure disorders.

OBJECTIVE:To analyze the association between peri-ictal brainstem posturing semiologies with post-ictal generalized electroencephalographic suppression (PGES) and breathing dysfunction in generalized convulsive seizures (GCS). METHODS:Prospective, multicenter analysis of GCS. Ictal brainstem semiology was classified as (1) decerebration: bilateral symmetric tonic arm extension, (2) decortication: bilateral symmetric tonic arm flexion only, (3) hemi-decerebration: unilateral tonic arm extension with contralateral flexion and (4) absence of ictal tonic phase. Post-ictal posturing was also assessed. Respiration was monitored using thoraco-abdominal belts, video and pulse oximetry. RESULTS:= 0.035). CONCLUSIONS:recovery. Peri-ictal brainstem posturing may be surrogate biomarkers for GCS severity identifiable without in-hospital monitoring. CLASSIFICATION OF EVIDENCE/METHODS:This study provides Class III evidence that peri-ictal brainstem posturing is associated with the GCS with more prolonged PGES and more severe breathing dysfunction.

Objective: A recent clinical trial with 0.7mg/kg/day of fenfluramine (FFA) showed 62.3% (IC 95%: -47.7%; -72.8%; p<0.001) reduction in convulsive seizure frequency (CSF) compared to placebo. However, the impact of the disease on the patient and their caregivers may depend on other variables. This alternative analysis value the impact of other results.
Method(s): After a baseline period of 6 weeks patients with DS ages 2 to 18 years, was randomized to FFA 0.7 or 0.2mg/kg/day or placebo added. Time to new event (time required to experience the same number of crisis as in the reference period [TTE]) was analyzed. Intervals without crisis and number of days without crisis was analyzed too.
Result(s): 119 patients with DS receiving FFA 0.7mg/kg/day; FFA0.2mg/kg/day; or placebo. TTE was significantly longer in active groups. Placebo: 6 weeks, FFA 0.2mg/kg/day:8 weeks and FFA 0.7mg/kg/day: >12 weeks (p<0.001; ~60% of patients in the FFA 0.7mg/kg/day group never reached their baseline seizure count and were censored). The number of days without crisis was higher in groups treated with FFA: 33 and 20 days without additional crisis counted in the active groups. The longest average without crisis was higher with FFA 0.7mg/kg/day (25 days; p<0.001) and FFA 0.2mg/kg/day (15 days; Px0.035) than with placebo (9.5 days).
Conclusion(s): FFA extended TTE and provided significantly more days without crisis and longer periods without crisis than placebo. Our analysis can help assess the ability of a treatment to reduce the burden of seizures in patients with SD and their caregivers

We reviewed data on the American diet from 1800 to 2019. Methods: We examined food availability and estimated consumption data from 1800 to 2019 using historical sources from the federal government and additional public data sources. Results: Processed and ultra-processed foods increased from <5 to >60% of foods. Large increases occurred for sugar, white and whole wheat flour, rice, poultry, eggs, vegetable oils, dairy products, and fresh vegetables. Saturated fats from animal sources declined while polyunsaturated fats from vegetable oils rose. Non-communicable diseases (NCDs) rose over the twentieth century in parallel with increased consumption of processed foods, including sugar, refined flour and rice, and vegetable oils. Saturated fats from animal sources were inversely correlated with the prevalence of NCDs. Conclusions: As observed from the food availability data, processed and ultra-processed foods dramatically increased over the past two centuries, especially sugar, white flour, white rice, vegetable oils, and ready-to-eat meals. These changes paralleled the rising incidence of NCDs, while animal fat consumption was inversely correlated.

Seizure clusters, an intermediate between single seizure and status epilepticus, are associated with morbidity, impaired quality of life, and premature mortality. The relationship between seizure clusters and sudden unexplained death in epilepsy (SUDEP) is poorly understood. Here, we define seizure clusters; review comorbid psychiatric disorders and memory deficits associated with seizure clusters; and review cases of witnessed SUDEP for which seizure frequency prior to death is available. Patients with a history of seizure clusters have a 2.5 fold increased risk for SUDEP, and one third of patients with monitored in hospital SUDEP experienced a cluster of generalized tonic clonic seizures prior to death. Understanding the effects of seizure frequency and duration on SUDEP risk could yield new insights in SUDEP pathophysiology and new targets for intervention.

Epilepsy is a common neurological disorder affecting over 70 million people worldwide, with a high rate of pharmaco-resistance, diverse comorbidities including progressive cognitive and behavioural disorders, and increased mortality from direct (e.g. sudden unexpected death in epilepsy, accidents, drowning) or indirect effects of seizures and therapies. Extensive research with animal models and human studies provides limited insights into the mechanisms underlying seizures and epileptogenesis, and these have not translated into significant reductions in pharmaco-resistance, morbidities or mortality. To help define changes in molecular signalling networks associated with seizures in epilepsy with a broad range of aetiologies, we examined the proteome of brain samples from epilepsy and control cases. Label-free quantitative mass spectrometry was performed on the hippocampal cornu ammonis 1-3 region (CA1-3), frontal cortex and dentate gyrus microdissected from epilepsy and control cases (n = 14/group). Epilepsy cases had significant differences in the expression of 777 proteins in the hippocampal CA1 - 3 region, 296 proteins in the frontal cortex and 49 proteins in the dentate gyrus in comparison to control cases. Network analysis showed that proteins involved in protein synthesis, mitochondrial function, G-protein signalling and synaptic plasticity were particularly altered in epilepsy. While protein differences were most pronounced in the hippocampus, similar changes were observed in other brain regions indicating broad proteomic abnormalities in epilepsy. Among the most significantly altered proteins, G-protein subunit beta 1 (GNB1) was one of the most significantly decreased proteins in epilepsy in all regions studied, highlighting the importance of G-protein subunit signalling and G-protein-coupled receptors in epilepsy. Our results provide insights into common molecular mechanisms underlying epilepsy across various aetiologies, which may allow for novel targeted therapeutic strategies.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Rationale: Currently, there is some ambiguity over the role of postictal generalized electro-encephalographic suppression (PGES) as a biomarker in sudden unexpected death in epilepsy (SUDEP). Visual analysis of PGES, known to be subjective, may account for this. In this study, we set out to perform an analysis of PGES presence and duration using a validated signal processing tool, specifically to examine the association between PGES and seizure features previously reported to be associated with visually analyzed PGES. Methods: This is a prospective, multicenter epilepsy monitoring study of autonomic and breathing biomarkers of SUDEP in adult patients with intractable epilepsy. We studied videoelectroencephalogram (vEEG) recordings of generalized convulsive seizures (GCS) in a cohort of patients in whom respiratory and vEEG recording were carried out during the evaluation in the epilepsy monitoring unit. A validated automated EEG suppression detection tool was used to determine presence and duration of PGES. Results: We studied 148 GCS in 87 patients. PGES occurred in 106/148 (71.6%) seizures in 70/87 (80.5%) of patients. PGES mean duration was 38.7 ± 23.7 (37; 1-169) seconds. Presence of tonic phase during GCS, including decerebration, decortication and hemi-decerebration, were 8.29 (CI 2.6-26.39, p = 0.0003), 7.17 (CI 1.29-39.76, p = 0.02), and 4.77 (CI 1.25-18.20, p = 0.02) times more likely to have PGES, respectively. In addition, presence of decerebration (p = 0.004) and decortication (p = 0.02), older age (p = 0.009), and hypoxemia duration (p = 0.03) were associated with longer PGES durations. Conclusions: In this study, we confirmed observations made with visual analysis, that presence of tonic phase during GCS, longer hypoxemia, and older age are reliably associated with PGES. We found that of the different types of tonic phase posturing, decerebration has the strongest association with PGES, followed by decortication, followed by hemi-decerebration. This suggests that these factors are likely indicative of seizure severity and may or may not be associated with SUDEP. An automated signal processing tool enables objective metrics, and may resolve apparent ambiguities in the role of PGES in SUDEP and seizure severity studies.

Rationale: Seizure clusters may be related to Sudden Unexpected Death in Epilepsy (SUDEP). Two or more generalized convulsive seizures (GCS) were captured during video electroencephalography in 7/11 (64%) patients with monitored SUDEP in the MORTEMUS study. It follows that seizure clusters may be associated with epilepsy severity and possibly with SUDEP risk. We aimed to determine if electroclinical seizure features worsen from seizure to seizure within a cluster and possible associations between GCS clusters, markers of seizure severity, and SUDEP risk. Methods: Patients were consecutive, prospectively consented participants with drug-resistant epilepsy from a multi-center study. Seizure clusters were defined as two or more GCS in a 24-h period during the recording of prolonged video-electroencephalography in the Epilepsy monitoring unit (EMU). We measured heart rate variability (HRV), pulse oximetry, plethysmography, postictal generalized electroencephalographic suppression (PGES), and electroencephalography (EEG) recovery duration. A linear mixed effects model was used to study the difference between the first and subsequent seizures, with a level of significance set at p < 0.05. Results: We identified 112 GCS clusters in 105 patients with 285 seizures. GCS lasted on average 48.7 ± 19 s (mean 49, range 2-137). PGES emerged in 184 (64.6%) seizures and postconvulsive central apnea (PCCA) was present in 38 (13.3%) seizures. Changes in seizure features from seizure to seizure such as seizure and convulsive phase durations appeared random. In grouped analysis, some seizure features underwent significant deterioration, whereas others improved. Clonic phase and postconvulsive central apnea (PCCA) were significantly shorter in the fourth seizure compared to the first. By contrast, duration of decerebrate posturing and ictal central apnea were longer. Four SUDEP cases in the cluster cohort were reported on follow-up. Conclusion: Seizure clusters show variable changes from seizure to seizure. Although clusters may reflect epilepsy severity, they alone may be unrelated to SUDEP risk. We suggest a stochastic nature to SUDEP occurrence, where seizure clusters may be more likely to contribute to SUDEP if an underlying progressive tendency toward SUDEP has matured toward a critical SUDEP threshold.