Faculty Bibliography

BACKGROUND AND OBJECTIVES/OBJECTIVE:Sudden unexpected death in epilepsy (SUDEP) is the leading cause of seizure-related deaths in people with epilepsy. Despite evidence that SUDEP counseling does not cause stress, improves treatment adherence, and empowers people with epilepsy and their caregivers, it remains underdiscussed. This study aimed to explore the in-depth perspectives of parents who have lost a child to SUDEP, focusing on their experiences, grief, and coping strategies, while factoring in their demographics, the clinical features of their deceased children, and their previous awareness of SUDEP, all aspects that have not been systematically investigated before. METHODS:This qualitative phenomenological study involved in-depth semistructured interviews with 51 parents of 43 children who died of SUDEP. Transcripts were analyzed using immersion/crystallization qualitative methodology with Dedoose software, using an iterative consensus-building process. Thematic analysis revealed common perspectives, grief narratives, coping strategies, and perceived needs among parents after their child's SUDEP. RESULTS:Of the 51 participating parents (mean age 54.1 ± 9.4 years, 71% female), 27 reported being unaware of SUDEP before it occurred, whereas 24 reported previous awareness of it. These groups shared similar demographics and clinical characteristics. However, "unaware" parents expressed more intense trauma and prolonged maladaptive grief, characterized by guilt, extreme anger, and medical distrust. By contrast, "aware" parents described mitigated trauma, with less guilt- and anger-ridden grief, and reduced reliance on specialized support groups. Previous SUDEP awareness provided emotional preparation, buffering the devastating reality and fostering agency and acceptance. Another theme highlighted the struggles parents faced immediately after SUDEP, particularly with law enforcement and treating physicians. Unanimously, parents emphasized the paramount importance of counseling about the known relationship between epilepsy and SUDEP. DISCUSSION/CONCLUSIONS:Previous awareness of SUDEP (or lack thereof) has complex and far-reaching effects on the subsequent parental perceived trauma, grief, and coping processes. Furthermore, emergency responders, official personnel, and treating physicians may mishandle the aftermath of SUDEP. This study's findings strongly advocate for a paradigm shift in SUDEP-related practices across multiple disciplines, including legislation. Emphasis should be placed on increasing proactive SUDEP counseling to mitigate the traumatic effect and subsequent grieving process when SUDEP occurs.

OBJECTIVES/OBJECTIVE:To explore the evolution of the grief experience following Sudden Unexpected Death in Epilepsy (SUDEP) and identify factors that assist the bereaved in coping with their loss. METHODS:A survey formulated by a multidisciplinary team gathered information gathered information on decedent and respondent demographics, epilepsy details, circumstances surrounding death, postmortem experiences, descriptions of grief overtime (from 3 months to > 10 years post death), insights into coping strategies and recommendations for assisting the bereaved. RESULTS:A total of 206 participants completed the survey (predominantly middle-aged white females who were parents of the deceased). Most respondents (69.2 %) were unaware of SUDEP prior to the death and strongly desired to have had prior information. Negative impacts on relationships and mental health were highest at three months post-loss but gradually improved over time; feelings of sadness persisted while anger and guilt decreased, and acceptance increased. Interactions with understanding peers, supportive family or friends, and professional counseling were identified as most helpful, alongside clear communication and support from medical professionals and advocacy groups. CONCLUSIONS:This study highlights the profound and evolving nature of grief following SUDEP, describes the importance of SUDEP disclosure as part of comprehensive epilepsy care, and illustrates the need for ongoing and dynamic support for the bereaved. Interpretation of the findings is limited as the respondents were predominantly middle-aged white females who were parents of the deceased.

The ability to quickly learn and generalize is one of the brain's most impressive feats and recreating it remains a major challenge for modern artificial intelligence research. One of the most mysterious one-shot learning abilities displayed by humans is one-shot perceptual learning, whereby a single viewing experience drastically alters visual perception in a long-lasting manner. Where in the brain one-shot perceptual learning occurs and what mechanisms support it remain enigmatic. Combining psychophysics, 7 T fMRI, and intracranial recordings, we identify the high-level visual cortex as the most likely neural substrate wherein neural plasticity supports one-shot perceptual learning. We further develop a deep neural network model incorporating top-down feedback into a vision transformer, which recapitulates and predicts human behavior. The prior knowledge learnt by this model is highly similar to the neural code in the human high-level visual cortex. These results reveal the neurocomputational mechanisms underlying one-shot perceptual learning in humans.

Large Language Models (LLMs) offer a framework for understanding language processing in the human brain. Unlike traditional models, LLMs represent words and context through layered numerical embeddings. Here, we demonstrate that LLMs' layer hierarchy aligns with the temporal dynamics of language comprehension in the brain. Using electrocorticography (ECoG) data from participants listening to a 30-minute narrative, we show that deeper LLM layers correspond to later brain activity, particularly in Broca's area and other language-related regions. We extract contextual embeddings from GPT-2 XL and Llama-2 and use linear models to predict neural responses across time. Our results reveal a strong correlation between model depth and the brain's temporal receptive window during comprehension. We also compare LLM-based predictions with symbolic approaches, highlighting the advantages of deep learning models in capturing brain dynamics. We release our aligned neural and linguistic dataset as a public benchmark to test competing theories of language processing.

Recent research demonstrates that large language models can predict neural activity recorded via electrocorticography during natural language processing. To predict word-by-word neural activity, most prior work evaluates encoding models within individual electrodes and participants, limiting generalizability. Here we analyze electrocorticography data from eight participants listening to the same 30-min podcast. Using a shared response model, we estimate a common information space across participants. This shared space substantially enhances large language model-based encoding performance and enables denoising of individual brain responses by projecting back into participant-specific electrode spaces-yielding a 37% average improvement in encoding accuracy (from r = 0.188 to r = 0.257). The greatest gains occur in brain areas specialized for language comprehension, particularly the superior temporal gyrus and inferior frontal gyrus. Our findings highlight that estimating a shared space allows us to construct encoding models that better generalize across individuals.

BACKGROUND:Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Generalised-particularly nocturnal-convulsive seizures, longstanding epilepsy, and solitary living have been identified retrospectively as risk factors. No definitive electroclinical biomarkers have been prospectively ascertained. This study aimed to identify SUDEP risk markers using multimodality data with long-term follow-up. METHODS:This prospective, multicentre, observational cohort study, conducted at nine centres (eight in the USA and one in the UK), recruited children and adults with epilepsy who were undergoing prolonged video-electroencephalographic (EEG) monitoring. Inclusion criteria were diagnosis of epilepsy by an epilepsy specialist, with or without drug resistance; age older than 2 months; admission to the epilepsy monitoring unit of a participating centre, with video-EEG monitoring; and completion of at least one 6-month follow-up. Demographic, electroclinical, and cardiorespiratory data were collected at baseline. Participants were followed up long term through routine clinic visits, review of electronic health records, and telephone interviews to collect information about seizure frequency, medication status, and mortality. The primary endpoint was time to SUDEP. Cox proportional hazards models were used to assess significant risk factors. FINDINGS/RESULTS:Between Sept 17, 2011, and Dec, 30, 2021, 2632 children and adults with epilepsy were enrolled in this study; 164 were lost to follow-up. 38 (1·54%) of 2468 participants died from SUDEP (12 definite, 18 probable, and eight possible SUDEP cases) and two had near-SUDEP events. Incident SUDEP mortality rate was 4·76 (95% CI 3·37-6·53) cases per 1000 person-years, from a cohort of 7982 person-years. Living alone (hazard ratio 7·62, 95% CI 3·94-14·71), three or more generalised convulsive seizures in the previous year (3·1, 1·64-5·87]), longer ictal central apnoea (1·11, 1·05-1·18), and longer postictal central apnoea (1·32, 1·14-1·54]) were significant predictors of increased SUDEP risk. In a subanalysis excluding possible and near-SUDEP cases, longer ictal central apnoea was not significant. INTERPRETATION/CONCLUSIONS:This study shows an association between premortem peri-ictal apnoea and increased SUDEP risk. Cardiorespiratory monitoring during seizures might benefit assessments of epilepsy mortality risk. Together with solitary living and convulsive seizure frequency, peri-ictal apnoea (>14 s for postictal central apnoea and >17 s for ictal central apnoea) could inform the development of a validatable SUDEP risk index. FUNDING/BACKGROUND:US National Institutes of Health.

BACKGROUND:Sudden unexpected death in epilepsy (SUDEP) is the most common category of epilepsy-related mortality. Centrally mediated respiratory dysfunction has been observed to lead to death in the majority of cases of SUDEP. SUDEP also mainly occurs during nighttime sleep. This study seeks to identify sleep EEG and sleep-related respiratory biomarkers of SUDEP risk. METHODS:In this case-control study, we compared demographic, clinical, EEG, and respiratory data from people with epilepsy who later died of SUDEP (the SUDEP group) with data from age and sex-matched living people with epilepsy, classified as high risk of SUDEP (with ≥1 generalised tonic-clonic seizure [GTCS] per year), low risk of SUDEP (no history of GTCS), and non-epilepsy controls. These data were prospectively collected as part of a multicentre National Institutes of Health study. We analysed sleep macroarchitecture and microarchitecture features and measured sleep homoeostasis by calculating overnight change in slow wave activity (SWA; 0·5-4·0 Hz) in non-rapid eye movement (NREM) sleep during seizure-free nights using linear regression models. We also analysed sleep respiratory metrics, including inter-breath interval variability. We used receiver operating characteristic analysis to assess the individual discriminative performance of demographic, clinical, sleep EEG, and sleep-related respiratory features to predict the risk of SUDEP. FINDINGS/RESULTS:Between Sept 1, 2011, and Oct 15, 2022, 41 participants who later died of SUDEP and 123 matched controls (41 people living with epilepsy at hight risk of SUDEP, 41 people living with epilepsy at low-risk of SUDEP, and 41 non-epilepsy controls) were enrolled. The SUDEP group showed an abnormal lack of overnight decline and an increase in the slope of SWA power compared with the other groups (SUDEP group mean 0·005 standardised error of the mean [SEM] 0·003; high-SUDEP risk group -0·005, 0·002; low-SUDEP risk group -0·003, 0·002; non-epilepsy controls -0·007, 0·003; p=0·017). The overnight increase in the SWA slope was more pronounced in males compared with females (males mean 0·012, SEM 0·001; females 0·001, 0·002; p=0·005). The variability of the inter-breath interval was significantly higher in the SUDEP (coefficient of variation mean 0·15, SD 0·09; SD mean 0·54 s SD 0·35 s) and high-SUDEP risk groups (0·11, 0·03; 0·46 s, 0·19 s) compared with low-SUDEP risk group (0·08, 0·03; 0·30 s, 0·14 s) and non-epilepsy controls (0·08, 0·02; 0·31 s, 0·11 s; p<0·0001). The coefficient of variation of inter-breath interval had the greatest predictive power of SUDEP risk (between-group point estimate difference 0·30, AUC 0·80; 95% CI 0·70-0·90; p<0·0001). INTERPRETATION/CONCLUSIONS:This study identifies impaired sleep homoeostasis in the form of altered SWA progression during NREM sleep overnight in people with epilepsy who later died of SUDEP, and an increase in respiratory variability during NREM sleep in people with epilepsy who later died of SUDEP and in people with epilepsy at high risk of SUDEP. Multiday polysomnography studies are needed to validate sleep homoeostasis and respiratory variability during sleep as potential biomarkers of SUDEP risk. Further studies are required to explore possible sleep interventions that could mitigate SUDEP risk. FUNDING/BACKGROUND:National Institutes of Health-National Institute of Neurological Disorders and Stroke.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Impaired consciousness in epilepsy negatively affects quality of life. Previous work has focused on temporal lobe seizures, where cortical slow waves are associated with depressed subcortical arousal and impaired consciousness. However, it is unknown whether frontal lobe seizures also show cortical slow waves or a different activity pattern with impaired consciousness. METHODS:Intracranial EEG (icEEG) recordings from patients at 3 centers were retrospectively assessed to identify seizures originating in the frontal lobe. Seizures were classified as focal preserved consciousness (FPC), focal impaired consciousness (FIC), or focal to bilateral tonic-clonic (FBTC) based on video review. Changes in icEEG power from preictal baseline were calculated in different cortical regions and across frequency ranges in these 3 seizure categories. RESULTS:< 0.001, 95% CI 330.1-781.9 and 375.3-818.2, respectively). DISCUSSION/CONCLUSIONS:The widespread power increases across frequencies in frontal lobe FIC seizures contrast with those in focal temporal lobe epilepsy, where impaired consciousness is associated with cortical slow waves. These findings suggest that different focal seizure types produce impaired consciousness by affecting widespread cortical regions but through different physiologic mechanisms. Insights gained by studying the physiology of impaired consciousness may be the first step toward developing novel treatments to prevent this significant negative consequence of epilepsy and improve quality of life.