Faculty Bibliography

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.

OBJECTIVE:There is evidence for central nervous system complications of coronavirus disease 2019 (COVID-19) infection, including encephalopathy. Encephalopathy caused by or arising from seizures, especially nonconvulsive seizures (NCS), often requires electroencephalography (EEG) monitoring for diagnosis. The prevalence of seizures and other EEG abnormalities among COVID-19-infected patients is unknown. METHODS:Medical records and EEG studies of patients hospitalized with confirmed COVID-19 infections over a 2-month period at a single US academic health system (four hospitals) were reviewed to describe the distribution of EEG findings including epileptiform abnormalities (seizures, periodic discharges, or nonperiodic epileptiform discharges). Factors including demographics, remote and acute brain injury, prior history of epilepsy, preceding seizures, critical illness severity scores, and interleukin 6 (IL-6) levels were compared to EEG findings to identify predictors of epileptiform EEG abnormalities. RESULTS:Of 111 patients monitored, most were male (71%), middle-aged or older (median age 64 years), admitted to an intensive care unit (ICU; 77%), and comatose (70%). Excluding 11 patients monitored after cardiac arrest, the most frequent EEG finding was moderate generalized slowing (57%), but epileptiform findings were observed in 30% and seizures in 7% (4% with NCS). Three patients with EEG seizures did not have epilepsy or evidence of acute or remote brain injury, although all had clinical seizures prior to EEG. Only having epilepsy (odds ratio [OR] 5.4, 95% confidence interval [CI] 1.4-21) or seizure(s) prior to EEG (OR 4.8, 95% CI 1.7-13) was independently associated with epileptiform EEG findings. SIGNIFICANCE/CONCLUSIONS:Our study supports growing evidence that COVID-19 can affect the central nervous system, although seizures are unlikely a common cause of encephalopathy. Seizures and epileptiform activity on EEG occurred infrequently, and having a history of epilepsy or seizure(s) prior to EEG testing was predictive of epileptiform findings. This has important implications for triaging EEG testing in this population.

PURPOSE/OBJECTIVE:Surgical planning for people with drug resistant non-lesional focal epilepsy can be challenging. Prior studies focus on cases that are only MRI-negative or MRI-negative with PET-positive imaging, but little is known about outcomes in patients with non-lesional findings on both MRI and PET imaging. In this study, we investigate 5-year surgical outcomes in patients who underwent epilepsy surgery for drug resistant MRI/PET-negative focal epilepsy. METHODS:We collected clinical and testing data on 131 consecutive patients with drug resistant non-lesional epilepsy who were presented at a multidisciplinary epilepsy surgery conference at the New York University Comprehensive Epilepsy Center between 2010 and 2014, and identified those who underwent epilepsy surgery in order to review 5-year surgical outcomes. RESULTS:There were 103 with non-lesional MRI studies, and of these, 22 had corresponding non-lesional PET imaging. 14 MRI/PET-negative patients pursued a surgical treatment option and 9 underwent resections after intracranial EEG. At 5 years, 77.8 % of patients had favorable (ILAE class 1 and 2) outcomes. Most (77.8 %) had focal cortical dysplasia type Ia (FCDIa) on pathology. CONCLUSION/CONCLUSIONS:These findings suggest that with careful planning and patient selection, surgery for patients with drug resistant MRI/PET-negative focal epilepsy can be successful.

Drug-resistant focal epilepsy (DRFE) in children can impair cognition and behavior, and lead to premature death. Increased pediatric epilepsy surgery numbers reflect the improvements in seizure control and long-term developmental outcomes. Yet, many children with DRFE are not candidates for surgical resection due to overlap of the seizure network with eloquent cortex or multiple seizure-onset zones, making surgery dangerous or ineffective. In adults, responsive neurostimulation (RNS System) therapy is safe and effective treatment for DRFE with one or two seizure foci, especially when the seizure focus is in eloquent cortex. We present six pediatric patients with DRFE who underwent RNS implantation. Our outcomes demonstrate safety, decreased clinical seizure frequency, as well as improved functional status and quality of life. Changes in the clinical seizure semiology and frequency occurred in conjunction with adjustments to the stimulation parameters, supporting the efficacy of responsive neuromodulation in children.

PURPOSE/OBJECTIVE:A phase I feasibility study to determine the accuracy of identifying seizures based on audio recordings. METHODS:We systematically generated 166 audio clips of 30 s duration from 83 patients admitted to an epilepsy monitoring unit between 1/2015 and 12/2016, with one clip during a seizure period and one clip during a non-seizure control period for each patient. Five epileptologists performed a blinded review of the audio clips and rated whether a seizure occurred or not, and indicated the confidence level (low or high) of their rating. The accuracy of individual and consensus ratings were calculated. RESULTS:The overall performance of the consensus rating between the five epileptologists showed a positive predictive value (PPV) of 0.91 and a negative predictive value (NPV) of 0.66. The performance improved when confidence was high (PPV of 0.96, NPV of 0.70). The agreement between the epileptologists was moderate with a kappa of 0.584. Hyperkinetic (PPV 0.92, NPV 0.86) and tonic-clonic (PPV and NPV 1.00) seizures were most accurately identified. Seizures with automatisms only and non-motor seizures could not be accurately identified. Specific seizure-related sounds associated with accurate identification included disordered breathing (PPV and NPV 1.00), rhythmic sounds (PPV 0.93, NPV 0.80), and ictal vocalizations (PPV 1.00, NPV 0.97). CONCLUSION/CONCLUSIONS:This phase I feasibility study shows that epileptologists are able to accurately identify certain seizure types from audio recordings when the seizures produce sounds. This provides guidance for the development of audio-based seizure detection devices and demonstrate which seizure types could potentially be detected.