Faculty Bibliography

Epilepsy surgery should be considered in all patients with drug-resistant focal epilepsy. The diagnostic presurgical evaluation aims to delineate the epileptogenic zone and its relationship to eloquent brain regions. Genetic testing is not yet routine in presurgical evaluations, despite many monogenic causes of severe epilepsies, including some focal epilepsies. This review highlights genomic data that may inform decisions regarding epilepsy surgery candidacy and strategy. Focal epilepsies due to pathogenic variants in mechanistic target of rapamycin pathway genes are amenable to surgery if clinical, electroencephalography and imaging data are concordant. Epilepsy surgery outcomes are less favourable in patients with pathogenic variants in ion channel genes such as SCN1A. However, genomic data should not be used in isolation to contraindicate epilepsy surgery and should be considered alongside other diagnostic modalities. The additional role of somatic mosaicism in the pathogenesis of focal epilepsies may have implications for surgical planning and prognostication. Here, we advocate for including genomic data in the presurgical evaluation and multidisciplinary discussion for many epilepsy surgery candidates. We encourage neurologists to perform genetic testing in patients with focal non-lesional epilepsy, epilepsy in the setting of intellectual disability and epilepsy due to specific malformations of cortical development. The integration of genomics into the presurgical evaluation assists selection of patients for resective surgery and fosters a personalised medicine approach, where precision or targeted therapies are considered alongside surgical procedures.

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.

Large international consortia examining the genomic architecture of the epilepsies focus on large diagnostic subgroupings such as "all focal epilepsy" and "all genetic generalized epilepsy". In addition, phenotypic data are generally entered into these large discovery databases in a unidirectional manner at one point in time only. However, there are many smaller phenotypic subgroupings in epilepsy, many of which may have unique genomic risk factors. Such a subgrouping or "microphenotype" may be defined as an uncommon or rare phenotype that is well recognized by epileptologists and the epilepsy community, and which may or may not be formally recognized within the International League Against Epilepsy classification system. Here we examine the genetic structure of a number of such microphenotypes and report in particular on two interesting clinical phenotypes, Jeavons syndrome and pediatric status epilepticus. Although no single gene reached exome-wide statistical significance to be associated with any of the diagnostic categories, we observe enrichment of rare damaging variants in established epilepsy genes among Landau-Kleffner patients (GRIN2A) and pediatric status epilepticus patients (MECP2, SCN1A, SCN2A, SCN8A).

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.

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.

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.

Interictal epileptiform discharges (IEDs) can impair memory. The properties of IEDs most detrimental to memory, however, are undefined. We studied the impact of temporal and spatial characteristics of IEDs on list learning. Subjects completed a memory task during intracranial EEG recordings including hippocampal depth and temporal neocortical subdural electrodes. Subjects viewed a series of objects, and after a distracting task, recalled the objects from the list. The impacts of IED presence, duration, and propagation to neocortex during encoding of individual stimuli were assessed. The effects of IED total number and duration during maintenance and recall periods on delayed recall performance were also determined. The influence of IEDs during recall was further investigated by comparing the likelihood of IEDs preceding correctly recalled items vs. periods of no verbal response. Across 6 subjects, we analyzed 28 hippocampal and 139 lateral temporal contacts. Recall performance was poor, with a median of 17.2% correct responses (range 10.4-21.9%). Interictal epileptiform discharges during encoding, maintenance, and recall did not significantly impact task performance, and there was no significant difference between the likelihood of IEDs during correct recall vs. periods of no response. No significant effects of discharge duration during encoding, maintenance, or recall were observed. Interictal epileptiform discharges with spread to lateral temporal cortex during encoding did not adversely impact recall. A post hoc analysis refining model assumptions indicated a negative impact of IED count during the maintenance period, but otherwise confirmed the above results. Our findings suggest no major effect of hippocampal IEDs on list learning, but study limitations, such as baseline hippocampal dysfunction, should be considered. The impact of IEDs during the maintenance period may be a focus of future research.

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.

The COVID-19 pandemic has caused global anguish unparalleled in recent times. As cases rise, increased pressure on health services, combined with severe disruption to people's everyday lives, can adversely affect individuals living with chronic illnesses, including people with epilepsy. Stressors related to disruption to healthcare, finances, mental well-being, relationships, schooling, physical activity, and increased isolation could increase seizures and impair epilepsy self-management. We aim to understand the impact that COVID-19 has had on the health and well-being of people with epilepsy focusing on exposure to increased risk of seizures, associated comorbidity, and mortality. We designed two online surveys with one addressing people with epilepsy directly and the second for caregivers to report on behalf of a person with epilepsy. The survey is ongoing and has yielded 463 UK-based responses by the end of September 2020. Forty percent of respondents reported health changes during the pandemic (n = 185). Respondents cited a change in seizures (19%, n = 88), mental health difficulties (34%, n = 161), and sleep disruption (26%, n = 121) as the main reasons. Thirteen percent found it difficult to take medication on time. A third had difficulty accessing medical services (n = 154), with 8% having had an appointment canceled (n = 39). Only a small proportion reported having had discussions about epilepsy-related risks, such as safety precautions (16%, n = 74); mental health (29%, n = 134); sleep (30%, n = 140); and Sudden Unexpected Death in Epilepsy (SUDEP; 15%, n = 69) in the previous 12 months. These findings suggest that people with epilepsy are currently experiencing health changes, coupled with inadequate access to services. Also, there seems to be a history of poor risk communication in the months preceding the pandemic. As the UK witnesses a second COVID-19 wave, those involved in healthcare delivery must ensure optimal care is provided for people with chronic conditions, such as epilepsy, to ensure that avoidable morbidity and mortality is prevented during the pandemic, and beyond.

OBJECTIVE:The combined spatiotemporal dynamics underlying sign language production remains largely unknown. To investigate these dynamics as compared to speech production we utilized intracranial electrocorticography during a battery of language tasks. METHODS:We report a unique case of direct cortical surface recordings obtained from a neurosurgical patient with intact hearing and bilingual in English and American Sign Language. We designed a battery of cognitive tasks to capture multiple modalities of language processing and production. RESULTS:We identified two spatially distinct cortical networks: ventral for speech and dorsal for sign production. Sign production recruited peri-rolandic, parietal and posterior temporal regions, while speech production recruited frontal, peri-sylvian and peri-rolandic regions. Electrical cortical stimulation confirmed this spatial segregation, identifying mouth areas for speech production and limb areas for sign production. The temporal dynamics revealed superior parietal cortex activity immediately before sign production, suggesting its role in planning and producing sign language. CONCLUSIONS:Our findings reveal a distinct network for sign language and detail the temporal propagation supporting sign production.