Faculty Bibliography

BACKGROUND AND OBJECTIVES/OBJECTIVE:Severe hypoxemia after generalized convulsive seizures (GCSs) can trigger neural injury and is a potential biomarker for sudden unexpected death in epilepsy (SUDEP). Some degree of variability in interbreath interval is normal, but increased variability may suggest dysfunctional breathing control and may be associated with severe postictal hypoxemia. We evaluated the relationship between interictal breathing variability and severity and duration of hypoxemia after GCS. METHODS:nadir), and secondary outcome: occurrence of combined prolonged and pronounced hypoxemia. Univariable and multivariable models were created for primary outcomes, but only univariable analyses were performed for the secondary outcome. RESULTS:= 0.002) was the only variable significantly associated with hypoxemia severity after controlling for duration of postictal generalized EEG suppression, SD-2 of the awake interbreath interval, and body mass index. Univariable analyses for combined prolonged and pronounced hypoxemia showed SD-2 of the awake interbreath interval, temporal lobe epilepsy, ictal central apnea, and a shorter tonic phase duration were significantly associated. DISCUSSION/CONCLUSIONS:Measures of interictal respiratory variability are associated with severe and prolonged hypoxemia after GCS. Increased interictal respiratory variability suggests baseline respiratory dysregulation in some PWE and may be a surrogate for SUDEP risk.

PURPOSE OF REVIEW/UNASSIGNED:Alzheimer disease (AD) and epilepsy are major causes of neurologic disability and are reciprocally related: epileptiform discharges, subclinical seizures, and epilepsy are more prevalent in patients with AD compared with controls; progressive cognitive impairment commonly afflicts epilepsy patients; and late-onset epilepsy patients have higher rates of new-onset dementia. RECENT FINDINGS/UNASSIGNED:Epidemiologic studies support shared risk factors (e.g., genetic variants, vascular disease, sleep disorders, microbiome) with notable divergences. AD and epilepsy have some overlapping anatomic (e.g., hippocampus, entorhinal, and association cortex), clinical (e.g., memory, attentional, and executive) impairments, and neuropathologic (e.g., amyloid, tau, neurofibrillary tangles) features. Shared clinical and translational challenges include underlying mechanisms (e.g., genetic variants, neuroinflammation, metabolic and mitochondrial dysfunction, excitatory/inhibitory imbalance, microbiome, and sociodemographic factors) and identifying valid and reliable biomarkers (e.g., total tau and phosphorylated tau (p-tau), amyloid deposition, Aβ42/Aβ40 ratio) to assess disease progression, predict outcomes, and assess potentially disease-modifying interventions. SUMMARY/UNASSIGNED:Identifying convergences and divergences between epilepsy and AD may inform our understanding. The clinical, neurophysiologic, neuropathologic, and molecular pathologic changes in AD and epilepsy may reveal pathophysiologic insights and therapeutic opportunities.

Extensive neuroimaging research in temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) has identified brain atrophy as a disease phenotype. While it is also related to a complex genetic architecture, the transition from genetic risk factors to brain vulnerabilities remains unclear. Using a population-based approach, we examined the associations between epilepsy-related polygenic risk for HS (PRS-HS) and brain structure in healthy developing children, assessed their relation to brain network architecture, and evaluated its correspondence with case-control findings in TLE-HS diagnosed patients relative to healthy individuals We used genome-wide genotyping and structural T1-weighted magnetic resonance imaging (MRI) of 3,826 neurotypical children from the Adolescent Brain Cognitive Development (ABCD) study. Surface-based linear models related PRS-HS to cortical thickness measures, and subsequently contextualized findings with structural and functional network architecture based on epicentre mapping approaches. Imaging-genetic associations were then correlated to atrophy and disease epicentres in 785 patients with TLE-HS relative to 1,512 healthy controls aggregated across multiple sites. Higher PRS-HS was associated with decreases in cortical thickness across temporo-parietal as well as fronto-central regions of neurotypical children. These imaging-genetic effects were anchored to the connectivity profiles of distinct functional and structural epicentres. Compared with disease-related alterations from a separate epilepsy cohort, regional and network correlates of PRS-HS strongly mirrored cortical atrophy and disease epicentres observed in patients with TLE-HS, and highly replicable across different studies. Findings were consistent when using statistical models controlling for spatial autocorrelations and robust to variations in analytic methods. Capitalizing on recent imaging-genetic initiatives, our study provides novel insights into the genetic underpinnings of structural alterations in TLE-HS, revealing common morphological and network pathways between genetic vulnerability and disease mechanisms. These signatures offer a foundation for early risk stratification and personalized interventions targeting genetic profiles in epilepsy.

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.

OBJECTIVE:CDKL5 deficiency disorder (CDD) is a rare X-linked developmental and epileptic encephalopathy caused by loss-of-function variants in the CDKL5 gene. Preclinical experiments using enzyme replacement or gene therapies show promise and could be transformative therapies. This precompetitive consortium sought to harmonize nonseizure clinical endpoint selection for efficacy trials. Clinical Assessment of Neurodevelopmental Measures in CDD (CANDID) is an ongoing study evaluating the feasibility and suitability of neurocognitive tests and functioning scales in CDD patients. METHODS:CANDID is a 3-year, longitudinal, noninterventional global study involving children and adults with CDD. On-site and remote visits include clinical, behavioral, developmental, and quality of life assessments. RESULTS:We enrolled 112 patients (111 included in analyses); mean age = 8.3 years (range <1-28); 93% female; 10 participants were ≥18 years old. In the first 28 days, 82% had >16 seizures; six were seizure-free. Median seizure onset was at 1.5 months (range = 0-66). Patients used an average of 2.6 antiseizure medications at baseline. The most frequent comorbidities included gastrointestinal hypomotility, muscle tone abnormalities, and sleep disorders. Gross Motor Function Measure-88 (GMFM-88) scores indicated a floor effect in crawling, standing, and walking across all ages. Vineland-3 and Bayley-4 scores could be derived in most, with receptive language, interpersonal relationships, and fine and gross motor scores increasing with age. Bruni sleep questionnaire identified sleep initiation, sleep-awake transition, and excessive somnolence as the most disrupted components across all age groups. The mean Quality of Life Inventory-Disability total scores ranged from 53% to 64%, the independence domain being the most impacted. SIGNIFICANCE/CONCLUSIONS:The scales in the CANDID study capture disease-related deficits and phenotype variability in CDD. Floor effects in subdomains aligned with disease severity. The GMFM-88 lacks granularity, and its operational limitations make it unsuitable for CDD trials. Baseline analyses demonstrate the feasibility and potential value of most selected scales, supporting their use in optimizing trial design and endpoint selection for future CDD clinical trials.

Polyaminopathies are a recently described family of rare genetic neurodevelopmental disorders. Polyaminopathies disrupt the biosynthesis of the primary polyamines: putrescine, spermidine, and spermine. Snyder-Robinson syndrome results from hemizygous loss-of-function variants in the spermine synthase (SMS) gene, resulting in decreased or complete loss of spermine synthase enzyme activity. Bachmann-Bupp syndrome results from heterozygous gain-of-function variants in the ornithine decarboxylase 1 (ODC1) gene, resulting in increased ornithine decarboxylase enzyme activity. Faundes-Banka syndrome results from heterozygous loss-of-function variants in the eukaryotic translation initiation factor 5A (EIF5A) gene, impairing eIF5A protein function. DHPS (deoxyhypusine synthase) deficiency is an autosomal recessive disease and results from bi-allelic hypomorphic variants in the deoxyhypusine synthase (DHPS) gene, which results in reduced deoxyhypusine synthase enzyme activity. Finally, DOHH (deoxyhypusine hydroxylase) disorder is an autosomal recessive disorder caused by bi-allelic loss-of-function variants in the deoxyhypusine hydroxylase (DOHH) gene, which causes decreased deoxyhypusine hydroxylase enzyme activity. Snyder-Robinson syndrome was first described in 1969, while the other four syndromes have only been identified in the past 7 years. A comprehensive phenotypic and genotypic description of these five syndromes is needed. We review the clinical and genetic features of these five polyaminopathies to create an inclusive clinical resource. A systematic keyword search strategy was used to identify all published cases in PubMed, Web of Science, and Scopus databases. The five known syndromes associated with the polyamine pathway share many similar clinical phenotypes, and yet patients with each syndrome present with distinctive syndromic features. This review will serve as a valuable resource for clinicians diagnosing and caring for patients with these rare polyaminopathies.

OBJECTIVE:Sudden unexpected death in epilepsy (SUDEP) often follows generalized tonic-clonic seizures during sleep, likely resulting from impaired brainstem cardiorespiratory function. We used ictal electrocardiogram (ECG)-based cross-frequency phase-amplitude coupling (PAC) to detect cardiorespiratory disruptions, comparing SUDEP to non-SUDEP cohorts. Leveraging respiratory modulation of ECG signals can provide a robust indirect proxy of respiratory monitoring despite high-amplitude noise. METHODS:We analyzed ictal ECG and electroencephalographic recordings in 21 SUDEP cases and 21 non-SUDEP epilepsy controls. Ictal ECG segments from 76 seizures (38 SUDEP, 38 non-SUDEP) were processed using continuous wavelet transformation to compute PAC between respiratory (.1-.55 Hz, 6-33 breaths per minute) and cardiac (.7-3.7 Hz, 42-222 beats per minute) frequencies. Relative PAC coupling strength was evaluated for respiratory frequencies > .25 Hz (15 breaths per minute) and cardiac frequencies > 1.7 Hz (102 beats per minute). Furthermore, a 3 × 3 grid of PAC ranges was derived for each 20-s window, yielding 18 features (mean and SD) as inputs to a logistic regression model. RESULTS:Elevated ictal PAC at higher respiratory (>.25 Hz, p < .0001) and cardiac (>1.7 Hz, p < .0142) frequencies in SUDEP patients suggests ictal respiration modulates ictal tachycardia, leading to cardiorespiratory dysfunction, probably brainstem-mediated. The logistic model accurately distinguished 38 seizures in SUDEP cases from 38 seizures in non-SUDEP cases (receiver operating characteristic area under the curve = 91%). Seizures in SUDEP patients had higher propensity scores (p < .001) both per seizure and per patient. All six test seizures (three SUDEP, three non-SUDEP) were correctly classified using the optimal threshold. SIGNIFICANCE/CONCLUSIONS:Ictal ECG-based PAC analysis is a potential noninvasive biomarker for SUDEP risk, capturing cardiorespiratory dysregulation during seizures. Its integration into wearable ECG devices could enable real-time risk assessment, informing clinical interventions such as rescue medications, antiseizure medication adjustments, or surgical evaluations.

DNA methylation is a crucial epigenetic mechanism that regulates gene expression. Precise editing of DNA methylation has emerged as a promising tool for dissecting its biological function. However, challenges in delivery have limited most applications of DNA methylation editing to in vitro systems. Here, we develop two transgenic mouse lines harboring an inducible dCas9-DNMT3A or dCas9-TET1 editor to enable tissue-specific DNA methylation editing in vivo. We demonstrate that targeted methylation of the Psck9 promoter in the liver of dCas9-DNMT3A mice results in decreased Pcsk9 expression and a subsequent reduction in serum low-density lipoprotein cholesterol level. Targeted demethylation of the Mecp2 promoter in dCas9-TET1 mice reactivates Mecp2 expression from the inactive X chromosome and rescues neuronal nuclear size in Mecp2^+/- mice. Genome-wide sequencing analyses reveal minimal transcriptional off-targets, demonstrating the specificity of the system. These results demonstrate the feasibility and versatility of methylation editing, to functionally interrogate DNA methylation in vivo.

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.