Faculty Bibliography

The COVID-19 pandemic has had an unprecedented impact on people and healthcare services. The disruption to chronic illnesses, such as epilepsy, may relate to several factors ranging from direct infection to secondary effects from healthcare reorganization and social distancing measures.

Models of reading emphasize that visual (orthographic) processing provides input to phonological as well as lexical-semantic processing. Neurobiological models of reading have mapped these processes to distributed regions across occipital-temporal, temporal-parietal, and frontal cortices. However, the role of the precentral gyrus in these models is ambiguous. Articulatory phonemic representations in the precentral gyrus are obviously involved in reading aloud, but it is unclear if the precentral gyrus is recruited during reading silently in a time window consistent with participation in phonological processing contributions. Here, we recorded intracranial electrophysiology during a speeded semantic decision task from 24 patients to map the spatio-temporal flow of information across the cortex during silent reading. Patients selected animate nouns from a stream of nonanimate words, letter strings, and false-font stimuli. We characterized the distribution and timing of evoked high-gamma power (70-170 Hz) as well as phase-locking between electrodes. The precentral gyrus showed a proportion of electrodes responsive to linguistic stimuli (27%) that was at least as high as those of surrounding peri-sylvian regions. These precentral gyrus electrodes had significantly greater high-gamma power for words compared to both false-font and letter-string stimuli. In a patient with word-selective effects in the fusiform, superior temporal, and precentral gyri, there was significant phase-locking between the fusiform and precentral gyri starting at ∼180 msec and between the precentral and superior temporal gyri starting at ∼220 msec. Finally, our large patient cohort allowed exploratory analyses of the spatio-temporal reading network underlying silent reading. The distribution, timing, and connectivity results place the precentral gyrus as an important hub in the silent reading network.

Background and Objectives/UNASSIGNED:syndrome by collecting data, including EEG, from a large cohort of affected patients. Methods/UNASSIGNED:Syndrome Foundation and the literature. Data on clinical, genetic, neuroimaging, and neurophysiologic features were obtained. Results/UNASSIGNED:without any clear genotype-phenotype associations. Discussion/UNASSIGNED:syndrome presents with a developmental and epileptic encephalopathy with characteristics recognizable from neonatal age, which should prompt genetic screening. Sixty percent have drug-resistant epilepsy with focal or generalized seizures. We collected more than 90 pathogenic variants without observing overt genotype-phenotype associations.

PURPOSE/OBJECTIVE:To assess the impact of fenfluramine (FFA) on the expected mortality incidence, including sudden unexpected death in epilepsy (SUDEP), in persons with Dravet syndrome (DS). METHODS:In this pooled analysis, total time of exposure for persons with DS who were treated with FFA in phase 3 clinical trials, in United States and European Early Access Programs, and in two long-term open-label observational studies in Belgium was calculated. Literature was searched for reports of SUDEP mortality in DS, which were utilized as a comparison. Mortality rates were expressed per 1000 person-years. RESULTS:A total of 732 persons with DS were treated with FFA, representing a total of 1185.3 person-years of exposure. Three deaths occurred, all in the phase 3 program: one during placebo treatment (probable SUDEP) and two during treatment with FFA (one probable SUDEP and one definite SUDEP). The all-cause and SUDEP mortality rates during treatment with FFA was 1.7 per 1000 person-years (95% CI, 0.4 to 6.7), a value lower than the all-cause estimate of 15.8 per 1000 person-years (95% CI, 9.9 to 25.4) and SUDEP estimate of 9.3 (95% CI, 5.0 to 17.3) reported by Cooper et al. (Epilepsy Res 2016;128:43-7) for persons with DS receiving standard-of-care. CONCLUSION/CONCLUSIONS:All-cause and SUDEP mortality rates in DS patients treated with FFA were substantially lower than in literature reports. Further studies are warranted to confirm that FFA reduces SUDEP risk in DS patients and to better understand the potential mechanism(s) by which FFA lowers SUDEP risk. CLINICAL TRIAL REGISTRATION/BACKGROUND:NCT02926898, NCT02682927, NCT02826863, NCT02823145, NCT03780127.

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.

Perception results from the interplay of sensory input and prior knowledge. Despite behavioral evidence that long-term priors powerfully shape perception, the neural mechanisms underlying these interactions remain poorly understood. We obtained direct cortical recordings in neurosurgical patients as they viewed ambiguous images that elicit constant perceptual switching. We observe top-down influences from the temporal to occipital cortex, during the preferred percept that is congruent with the long-term prior. By contrast, stronger feedforward drive is observed during the non-preferred percept, consistent with a prediction error signal. A computational model based on hierarchical predictive coding and attractor networks reproduces all key experimental findings. These results suggest a pattern of large-scale information flow change underlying long-term priors' influence on perception and provide constraints on theories about long-term priors' influence on perception.

Background and aims: Epilepsy encompasses a group of neurological disorders characterized by an imbalance of electrical activity in the central nervous system (CNS) and recurrent seizures representing the principal clinical manifestation. Acetylcholine (ACh), serotonin, and norepinephrine (NE) may modulate neural activity via several mechanisms, mainly through its receptors/transporter activity and alterations in the extracellular potassium (K⁺) concentration via inwardly rectifying K⁺ (Kir) channels. Therefore, the aim of this study was to investigate the immunoreactivity pattern of these neurotransmitter, receptors/transporters and Kir channels in Pentylenetetrazol (PTZ)-kindling rat model, a well-established tool for studying chronic epilepsy.
Method(s): Kindling was chemically induced by intraperitoneally injections of PTZ for one month. Changes in the immunoreactivity of epileptogenesis-related neurotransmitter receptors/transporters (M2, 5-HT2B, and NE transporter) as well as Kir3.1 and Kir6.2 channels were determined in the cortex, hippocampus and medulla of adult Wistar rats by immunohistochemistry analyses.
Result(s): Increased immunoreactivity of the NE transporter, M2, and 5-HT2B receptors was witnessed in the cortex and medulla. While the immunoreactivity of the 5-HT2B receptor was found increased in the cortex and medulla, it was decreased in the hippocampus, with no changes observed in the M2 receptor in this region. Kir3.1 and Kir6.2 staining showed increase immunoreactivity in the cerebral cortex, but contrasting findings were found in the hippocampus and medulla.
Conclusion(s): Our data suggested significant changes in the neurotransmitter, receptors/transporters and ion channels, that may regulate neurotransmitter levels such as ACh, serotonin, and NE in the cortex, hippocampus, and medulla, thus contributing to epileptogenesis.
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Humans form lasting memories of stimuli that were only encountered once. This naturally occurs when listening to a story, however it remains unclear how and when memories are stored and retrieved during story-listening. Here, we first confirm in behavioral experiments that participants can learn about the structure of a story after a single exposure and are able to recall upcoming words when the story is presented again. We then track mnemonic information in high frequency activity (70-200 Hz) as patients undergoing electrocorticographic recordings listen twice to the same story. We demonstrate predictive recall of upcoming information through neural responses in auditory processing regions. This neural measure correlates with behavioral measures of event segmentation and learning. Event boundaries are linked to information flow from cortex to hippocampus. When listening for a second time, information flow from hippocampus to cortex precedes moments of predictive recall. These results provide insight on a fine-grained temporal scale into how episodic memory encoding and retrieval work under naturalistic conditions.

Research has shown that sleep is beneficial for the long-term retention of memories. According to theories of memory consolidation, memories are gradually reorganized, becoming supported by widespread, distributed cortical networks, particularly during postencoding periods of sleep. However, the effects of sleep on the organization of memories in the hippocampus itself remains less clear. In a 3-d study, participants encoded separate lists of word-image pairs differing in their opportunity for sleep-dependent consolidation. Pairs were initially studied either before or after an overnight sleep period, and were then restudied in a functional magnetic resonance imaging (fMRI) scan session. We used multivariate pattern similarity analyses to examine fine-grained effects of consolidation on memory representations in the hippocampus. We provide evidence for a dissociation along the long axis of the hippocampus that emerges with consolidation, such that representational patterns for object-word memories initially formed prior to sleep become differentiated in anterior hippocampus and more similar, or overlapping, in posterior hippocampus. Differentiation in anterior hippocampal representations correlated with subsequent behavioral performance. Furthermore, representational overlap in posterior hippocampus correlated with the duration of intervening slow wave sleep. Together, these results demonstrate that sleep-dependent consolidation promotes the reorganization of memory traces along the long axis of the hippocampus.