Faculty Bibliography

Sudden unexplained death in childhood (SUDC) is death of a child ≥ 12 months old that is unexplained after autopsy and detailed analyses. Among SUDC cases, ~ 30% have febrile seizure (FS) history, versus 2-5% in the general population. SUDC cases share features with sudden unexpected death in epilepsy (SUDEP) and sudden infant death syndrome (SIDS), in which brainstem autonomic dysfunction is implicated. To understand whether brainstem protein changes are associated with FS history in SUDC, we performed label-free quantitative mass spectrometry on microdissected midbrain dorsal raphe, medullary raphe, and the ventrolateral medulla (n = 8 SUDC-noFS, n = 11 SUDC-FS). Differential expression analysis between SUDC-FS and SUDC-noFS at p < 0.05 identified 178 altered proteins in dorsal raphe, 344 in medullary raphe, and 100 in the ventrolateral medulla. These proteins were most significantly associated with increased eukaryotic translation initiation (p = 3.09 × 10^-7, z = 1.00), eukaryotic translation elongation (p = 6.31 × 10^-49, z = 6.01), and coagulation system (p = 1.32 × 10^-5, z = 1.00). The medullary raphe had the strongest enrichment for altered signaling pathways, including with comparisons to three other brain regions previously analyzed (frontal cortex, hippocampal dentate gyrus, cornu ammonus). Immunofluorescent tissue analysis of serotonin receptors identified 2.1-fold increased 5HT2A in the medullary raphe of SUDC-FS (p = 0.025). Weighted gene correlation network analysis (WGCNA) of case history indicated that longer FS history duration significantly correlated with protein levels in the medullary raphe and ventrolateral medulla; the most significant gene ontology biological processes were decreased cellular respiration (p = 9.8 × 10^-5, corr = - 0.80) in medullary raphe and decreased synaptic vesicle cycle (p = 1.60 × 10^-7, corr = - 0.90) in the ventrolateral medulla. Overall, FS in SUDC was associated with more protein differences in the medullary raphe and was related with increased translation-related signaling pathways. Future studies should assess whether these changes result from FS or may in some way predispose to FS or SUDC.

OBJECTIVES/OBJECTIVE:Late-onset epilepsy has the highest incidence among all age groups affected by epilepsy and often occurs in the absence of known clinical risk factors such as stroke and dementia. There is increasing evidence that brain changes contributing to epileptogenesis likely start years before disease onset, and we aim to relate cognitive and imaging correlates of subclinical brain injury to incident late-onset epilepsy in a large, community-based cohort. METHODS:We studied Offspring Cohort of the Framingham Heart Study participants 45 years or older, who were free of prevalent stroke, dementia, or epilepsy, and had neuropsychological (NP) evaluation and brain magnetic resonance imaging (MRI). Cognitive measures included Visual Reproduction Delayed Recall, Logical Memory Delayed Recall, Similarities, Trail Making Test B minus A (TrTB-TrTA; attention and executive function), and a global measure of cognition derived from principal component analysis. MRI measures included total cerebral brain volume, cortical gray matter volume (CGMV), white matter hyperintensity volume (WMHV), and hippocampal volume. Incident epilepsy was identified through a review of administrative data and medical records. Cox proportional hazards regression models were used for the analyses. All analyses were adjusted for age, sex, and educational level (cognition only). RESULTS:Among participants who underwent NP testing (n = 2349, 45.81% male), 31 incident epilepsy cases were identified during follow-up. Better performance on the TrTB-TrTA was associated with a lower risk of developing epilepsy (hazard ratio [HR] .25, 95% confidence interval [CI] .08-.73; p = .011). In the subgroup of participants with MRI (n = 2056, 46.01% male), 27 developed epilepsy. Higher WMHV was associated with higher epilepsy risk (HR 1.5, 95%CI 1.01-2.20; p = .042), but higher CGMV (HR .73, 95% CI .57-.93; p = .001) was associated with lower incidence of epilepsy. SIGNIFICANCE/CONCLUSIONS:Better performance on the (TrTB-TrTA), a measure of executive function and attention, and higher cortical volumes are associated with lower risk of developing epilepsy. Conversely, higher WMHV, a measure of occult vascular injury, increases the risk. Our study shows that non-invasive tests performed in mid-life may help identify people at risk for developing epilepsy later in life.

Syntax, the abstract structure of language, is a hallmark of human cognition. Despite its importance, its neural underpinnings remain obscured by inherent limitations of non-invasive brain measures and a near total focus on comprehension paradigms. Here, we address these limitations with high-resolution neurosurgical recordings (electrocorticography) and a controlled sentence production experiment. We uncover three syntactic networks that are broadly distributed across traditional language regions, but with focal concentrations in middle and inferior frontal gyri. In contrast to previous findings from comprehension studies, these networks process syntax mostly to the exclusion of words and meaning, supporting a cognitive architecture with a distinct syntactic system. Most strikingly, our data reveal an unexpected property of syntax: it is encoded independent of neural activity levels. We propose that this "low-activity coding" scheme represents a novel mechanism for encoding information, reserved for higher-order cognition more broadly.

OBJECTIVE:Hippocampal sclerosis (HS), the most common pathology associated with temporal lobe epilepsy (TLE), is not always visible on magnetic resonance imaging (MRI), causing surgical delays and reduced postsurgical seizure-freedom. We developed an open-source software to characterize and localize HS to aid the presurgical evaluation of children and adults with suspected TLE. METHODS:We included a multicenter cohort of 365 participants (154 HS; 90 disease controls; 121 healthy controls). HippUnfold was used to extract morphological surface-based features and volumes of the hippocampus from T1-weighted MRI scans. We characterized pathological hippocampi in patients by comparing them to normative growth charts and analyzing within-subject feature asymmetries. Feature asymmetry scores were used to train a logistic regression classifier to detect and lateralize HS. The classifier was validated on an independent multicenter cohort of 275 patients with HS and 161 healthy and disease controls. RESULTS:HS was characterized by decreased volume, thickness, and gyrification alongside increased mean and intrinsic curvature. The classifier detected 90.1% of unilateral HS patients and lateralized lesions in 97.4%. In patients with MRI-negative histopathologically-confirmed HS, the classifier detected 79.2% (19/24) and lateralized 91.7% (22/24). The model achieved similar performances on the independent cohort, demonstrating its ability to generalize to new data. Individual patient reports contextualize a patient's hippocampal features in relation to normative growth trajectories, visualise feature asymmetries, and report classifier predictions. INTERPRETATION/CONCLUSIONS:Automated and Interpretable Detection of Hippocampal Sclerosis (AID-HS) is an open-source pipeline for detecting and lateralizing HS and outputting clinically-relevant reports. ANN NEUROL 2024.

OBJECTIVE:Hippocampal sclerosis (HS), the most common pathology associated with temporal lobe epilepsy (TLE), is not always visible on magnetic resonance imaging (MRI), causing surgical delays and reduced postsurgical seizure-freedom. We developed an open-source software to characterize and localize HS to aid the presurgical evaluation of children and adults with suspected TLE. METHODS:We included a multicenter cohort of 365 participants (154 HS; 90 disease controls; 121 healthy controls). HippUnfold was used to extract morphological surface-based features and volumes of the hippocampus from T1-weighted MRI scans. We characterized pathological hippocampi in patients by comparing them to normative growth charts and analyzing within-subject feature asymmetries. Feature asymmetry scores were used to train a logistic regression classifier to detect and lateralize HS. The classifier was validated on an independent multicenter cohort of 275 patients with HS and 161 healthy and disease controls. RESULTS:HS was characterized by decreased volume, thickness, and gyrification alongside increased mean and intrinsic curvature. The classifier detected 90.1% of unilateral HS patients and lateralized lesions in 97.4%. In patients with MRI-negative histopathologically-confirmed HS, the classifier detected 79.2% (19/24) and lateralized 91.7% (22/24). The model achieved similar performances on the independent cohort, demonstrating its ability to generalize to new data. Individual patient reports contextualize a patient's hippocampal features in relation to normative growth trajectories, visualise feature asymmetries, and report classifier predictions. INTERPRETATION/CONCLUSIONS:Automated and Interpretable Detection of Hippocampal Sclerosis (AID-HS) is an open-source pipeline for detecting and lateralizing HS and outputting clinically-relevant reports. ANN NEUROL 2024.

Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, neuroscience researchers haven't kept up with the quick progress in LLM development. Here, we utilized several families of transformer-based LLMs to investigate the relationship between model size and their ability to capture linguistic information in the human brain. Crucially, a subset of LLMs were trained on a fixed training set, enabling us to dissociate model size from architecture and training set size. We used electrocorticography (ECoG) to measure neural activity in epilepsy patients while they listened to a 30-minute naturalistic audio story. We fit electrode-wise encoding models using contextual embeddings extracted from each hidden layer of the LLMs to predict word-level neural signals. In line with prior work, we found that larger LLMs better capture the structure of natural language and better predict neural activity. We also found a log-linear relationship where the encoding performance peaks in relatively earlier layers as model size increases. We also observed variations in the best-performing layer across different brain regions, corresponding to an organized language processing hierarchy.

OBJECTIVE:To characterize the experience of people with epilepsy and aligned healthcare workers (HCWs) during the first 18 months of the COVID-19 pandemic and compare experiences in high-income countries (HICs) with non-HICs. METHODS:Separate surveys for people with epilepsy and HCWs were distributed online in April 2020. Responses were collected to September 2021. Data were collected for COVID-19 infections, the effect of COVID-related restrictions, access to specialist help for epilepsy (people with epilepsy), and the impact of the pandemic on work productivity (HCWs). The frequency of responses for non-HICs and HICs were compared using non-parametric Chi-square tests. RESULTS:Two thousand one hundred and  five individuals with epilepsy from 53 countries and 392 HCWs from 26 countries provided data. The same proportion of people with epilepsy in non-HICs and HICs reported COVID-19 infection (7%). Those in HICs were more likely to report that COVID-19 measures had affected their health (32% vs. 23%; p < 0.001). There was no difference between non-HICs and HICs in the proportion who reported difficulty in obtaining help for epilepsy. HCWs in non-HICs were more likely to report COVID-19 infection than those in HICs (18% vs 6%; p = 0.001) and that their clinical work had been affected by concerns about contracting COVID-19, lack of personal protective equipment, and the impact of the pandemic on mental health (all p < 0.001). Compared to pre-pandemic practices, there was a significant shift to remote consultations in both non-HICs and HICs (p < 0.001). SIGNIFICANCE/CONCLUSIONS:While the frequency of COVID-19 infection was relatively low in these data from early in the pandemic, our findings suggest broader health consequences and an increased psychosocial burden, particularly among HCWs in non-HICs. Planning for future pandemics should prioritize mental healthcare alongside ensuring access to essential epilepsy services and expanding and enhancing access to remote consultations. PLAIN LANGUAGE SUMMARY/CONCLUSIONS:We asked people with epilepsy about the effects of COVID-19 on their health and healthcare. We wanted to compare responses from people in high-income countries and other countries. We found that people in high-income countries and other countries had similar levels of difficulty in getting help for their epilepsy. People in high-income countries were more likely to say that their general health had been affected. Healthcare workers in non-high-income settings were more likely to have contracted COVID-19 and have the care they deliver affected by the pandemic. Across all settings, COVID-19 associated with a large shift to remote consultations.

Whether high-frequency phase-locked oscillations facilitate integration ('binding') of information across widespread cortical areas is controversial. Here we show with intracranial electroencephalography that cortico-cortical co-ripples (~100-ms-long ~90 Hz oscillations) increase during reading and semantic decisions, at the times and co-locations when and where binding should occur. Fusiform wordform areas co-ripple with virtually all language areas, maximally from 200 to 400 ms post-word-onset. Semantically specified target words evoke strong co-rippling between wordform, semantic, executive and response areas from 400 to 800 ms, with increased co-rippling between semantic, executive and response areas prior to correct responses. Co-ripples were phase-locked at zero lag over long distances (>12 cm), especially when many areas were co-rippling. General co-activation, indexed by non-oscillatory high gamma, was mainly confined to early latencies in fusiform and earlier visual areas, preceding co-ripples. These findings suggest that widespread synchronous co-ripples may assist the integration of multiple cortical areas for sustained periods during cognition.

DHPS deficiency syndrome is an ultra-rare neurodevelopmental disorder (NDD) which results from biallelic mutations in the gene encoding the enzyme deoxyhypusine synthase (DHPS). DHPS is essential to synthesize hypusine, a rare amino acid formed by post-translational modification of a conserved lysine in eukaryotic initiation factor 5 A (eIF5A). DHPS deficiency syndrome causes epilepsy, cognitive and motor impairments, and mild facial dysmorphology. In mice, a brain-specific genetic deletion of Dhps at birth impairs eIF5A^HYP-dependent mRNA translation. This alters expression of proteins required for neuronal development and function, and phenotypically models features of human DHPS deficiency. We studied the role of DHPS in early brain development using a zebrafish loss-of-function model generated by knockdown of dhps expression with an antisense morpholino oligomer (MO) targeting the exon 2/intron 2 (E2I2) splice site of the dhps pre-mRNA. dhps knockdown embryos exhibited dose-dependent developmental delay and dysmorphology, including microcephaly, axis truncation, and body curvature. In dhps knockdown larvae, electrophysiological analysis showed increased epileptiform activity, and confocal microscopy analysis revealed reduced arborisation of GABAergic neurons. Our findings confirm that hypusination of eIF5A by DHPS is needed for early brain development, and zebrafish with an antisense knockdown of dhps model features of DHPS deficiency syndrome.