Kyle R. Pellerin

Adults older than age 55 years have the highest incidence rate and are the fastest-growing population among people with epilepsy. The aim of this study was to characterize the etiologies of new-onset seizures in older adults and to examine how seizure etiology varies across demographic groups. We used data from 7 US epilepsy centers from 2021 to 2025 and compared findings with those of previous population-based studies, providing an updated view and highlighting opportunities for prevention and improved risk stratification. We retrospectively reviewed medical charts of 2,052 patients aged ≥55 years at the time of a first seizure, who were evaluated at 7 epilepsy centers between 2021 and 2025. We categorized seizures by etiology as follows: ischemic stroke, hemorrhagic stroke, tumor, neurodegeneration, provoked seizures, traumatic brain injury, and unknown. We examined differences in etiology by demographic strata (age, sex, race, and primary language) using chi-square tests, Kruskal-Wallis tests, analysis of variance, and Cuzick tests. The most frequent seizure etiologies among older adults were unknown (29.9%), ischemic stroke (15.4%), and provoked seizures (14.9%). Neurodegenerative disease was the etiology for 5.3% of cases overall but increased in prevalence with age, accounting for 18.5% among patients aged 85-89 years. Seizure etiologies also differed by sex and race. Men more commonly had seizures caused by cerebrovascular disease and traumatic brain injury, while women more commonly had seizures due to neurodegenerative disease. Black patients had higher proportions of ischemic stroke and neurodegenerative disease, while unexplained epilepsy was more common among White patients. The causes of late-onset seizures vary based on age, sex, and race. Nearly one-third of cases of epilepsy in older adults remain unexplained despite advances in imaging techniques, underscoring the need for further research on the mechanisms and health implications of late-onset unexplained epilepsy. Improved prevention of cerebrovascular disease and optimized management of provoked seizures may reduce the growing burden of epilepsy in older adults.

Sleep architecture, including spindles and slow oscillations, is disrupted in Alzheimer disease (AD). How changes in these sleep elements relate to cognitive decline is less clear. Our objectives were to examine changes in sleep macroarchitecture and microarchitecture in early clinical stages of AD compared with older adult controls (CTLs) and to investigate their associations with longitudinal cognitive change. This was both a cross-sectional and longitudinal study performed at Mass General Brigham Hospitals, where participants with early AD or CTLs underwent overnight ambulatory scalp EEG and longitudinal cognitive testing. We compared sleep microarchitectural features extracted from the EEG, including spindle activity, across the brain topography and between groups. We then performed longitudinal analyses using mixed-effects models to test the association of these sleep features with changes in cognition on the Montreal Cognitive Assessment (MoCA), collected annually for up to 7 years. AD (n = 47, mean age 74.1 years, 66% female) and CTL (n = 43, mean age 72.6 years, 56% female) groups spent a similar proportion of sleep time in each stage of sleep. Sleep efficiency, however, was lower in the AD group (mean: CTL 75.1% vs AD 70.9%; p = 0.034). We found a significant reduction in spindle range power (11-16 Hz) in patients with AD compared with CTLs, particularly in the temporal regions (mean normalized power at EEG channels T3/T4: CTL 3.13 ± 1.13 vs AD 2.48 ± 1.01; p = 0.005). In participants with longitudinal MoCA scores (AD = 26, CTL = 25), reduced temporal lobe spindle density (β = 0.61, 95% CI 0.35-0.87; false discovery rate [FDR]-adjusted p < 0.001) and temporal lobe spindle power (β = 0.56, 95% CI 0.22-0.88; FDR-adjusted p = 0.005) were each associated with a faster rate of cognitive decline. Temporal lobe sleep spindle activity is reduced in early clinical stages of AD and is associated with a faster rate of cognitive decline. Our results underscore the importance of including temporal lobe measurements when assessing sleep neurophysiology in AD, which is not standard in polysomnography. Future work examining the relationship between AD biomarkers and reduced spindle activity is needed to elucidate the potential mechanisms underlying these findings.

Objective: Late‐onset unexplained epilepsy (LoUE), defined as epilepsy onset after age 55 without an obvious cause, is an important risk factor for dementia. Studies have shown that 10%–25% of individuals with LoUE develop dementia within 3–4 years following their first seizure. However, the mechanisms underlying progression from LoUE to dementia remain poorly understood. The goals of the ELUCID study are to identify risk factors associated with the development of cognitive decline and dementia in LoUE and to develop tools to identify patients at a high risk for these outcomes and thereby establish a foundation for dementia prevention strategies in this population. Methods and Analysis: ELUCID is a multi‐center prospective longitudinal observational study that will enroll 600 participants aged 55 or older with LoUE across seven U.S. medical centers. Participants will undergo a baseline evaluation that includes a detailed clinical history, cognitive testing, brain MRI, overnight scalp EEG, and blood biomarkers. Participants will be followed at 6‐month intervals for up to 5 years, to record cognitive and neurological changes, with the primary outcomes of interest being the development of mild cognitive impairment and/or dementia. This study aims to establish LoUE disease subtypes based on biomarkers, cognitive trajectories, and imaging features and to develop a risk stratification tool for predicting cognitive decline and dementia in patients presenting with LoUE. Ethics and Dissemination: ELUCID has obtained IRB approval (no. 2023P001566, August 2023), with the Mass General Brigham IRB serving as the single IRB of record. All de‐identified study data will be made publicly available on completion of the study. Plain Language Summary: The ELUCID study is a research project involving several medical centers across the U.S. It will focus on older adults who have recently developed seizures without a clear cause. Participants undergo an initial evaluation that includes questions about their medical history, a brain MRI , an overnight scalp EEG (brain wave study), and a blood draw. They will be followed over time with health questionnaires and yearly tests of memory and thinking. The purpose of the study is to learn what factors increase the risk of dementia in this population and to develop tools to predict which individuals are at the highest risk.

Alzheimer disease (AD) is associated with a 2 to 3-fold increased risk of developing late-onset focal epilepsy, yet it remains unclear how development of focal epilepsy in AD is related to AD pathology. The objective of this study was to examine spatial relationships between the epileptogenic zone and tau deposition, amyloid deposition, and brain atrophy in individuals with AD who developed late-onset, otherwise unexplained focal epilepsy. We hypothesized that if network hyperexcitability is mechanistically linked to AD pathology, then there would be increased tau and amyloid deposition within the epileptogenic hemisphere. In this cross-sectional study, we performed tau and amyloid PET imaging, brain MRI, and overnight scalp EEG in individuals with early clinical stages of AD who developed late-onset, otherwise unexplained focal epilepsy (AD-Ep). Participants were referred from epilepsy and memory disorders clinics at our institutions. We determined epilepsy localization based on EEG findings and seizure semiology. We quantified tau deposition, amyloid deposition, and atrophy across brain regions and calculated asymmetry indices for these measures. We compared findings in AD-Ep with those in a control AD group without epilepsy (AD-NoEp). The AD-Ep group included 8 individuals with a mean age of 69.5 ± 4.2 years at PET imaging. The AD-NoEp group included 14 individuals with a mean age of 71.7 ± 9.8 years at PET imaging. In AD-Ep, we found a highly asymmetric pattern of tau deposition, with significantly greater tau in the epileptogenic hemisphere. Amyloid deposition and cortical atrophy were also greater in the epileptogenic hemisphere, although the magnitudes of asymmetry were reduced compared with tau. Compared with AD-NoEp, the AD-Ep group had significantly greater tau asymmetry and trends toward greater asymmetry of amyloid and atrophy. AD-Ep also had significantly greater amyloid burden bilaterally and trends toward greater tau burden within the epileptogenic hemisphere, compared with AD-NoEp. Our results reveal a spatial association between the epileptogenic focus and tau deposition, amyloid deposition, and neurodegeneration in early clinical stages of AD. Within the limitations of a cross-sectional study with small sample sizes, these findings contribute to our understanding of the clinicopathologic heterogeneity of AD, demonstrating an association between focal epilepsy and lateralized pathology in AD.

Background: Functional connectivity (FC) studies in Alzheimer’s disease (AD) have focused primarily on the awake resting state. Yet, sleep disturbances in AD are common and occur early in the disease. Network hyperexcitability in AD also arises during sleep, resulting in epileptiform activity and seizures, and impacting memory consolidation. Hence, understanding the FC changes that occur during sleep in AD could provide important biological insights. To date, no studies have examined FC changes occurring during sleep in AD and their relationship with network hyperexcitability.Method: Using ambulatory scalp EEG recordings, we assessed sleep FC in cognitively normal healthy controls (HC, n = 35), individuals with early clinical stages of AD with no history or risk factors for epilepsy (ADNoEp, n = 44), and individuals with early clinical stages of AD and epilepsy related to AD (ADEp, n = 14). We measured FC using corrected amplitude envelope correlation and assessed FC on different scales, including channel‐pair associations, regional associations, seed‐based connectivity, and graph theory metrics. Machine learning classifiers were developed to determine whether sleep FC measures provide accurate discrimination between HC, ADNoEp, and ADEp.Result: There were no significant differences in age, sex, or education between HC, ADNoEp, and ADEp. Compared to HC, the ADNoEp group had 1) reduced FC during REM sleep in the alpha band in posterior regions; and 2) increased FC during NREM2 sleep in the gamma‐band in frontal regions. Compared to ADNoEp, the ADEp group had reduced FC in NREM2 sleep in the delta‐band in temporal and posterior regions. Graph theory metrics corroborated these sleep stage‐ and frequency‐specific findings between groups. Machine learning classifiers using graph theory metrics as input features distinguished ADNoEp from HC, and ADNoEp from ADEp, with high performance (AUC>0.85).Conclusion: AD is associated with sleep stage‐, frequency band‐, and region‐specific changes in FC. Network hyperexcitability in ADEp is also associated with changes in sleep FC, which are distinct from those seen in ADNoEp. Our findings expand understanding of brain network changes in AD to the sleep state and demonstrate sleep‐stage specific alterations in FC that may have important implications for understanding AD neurophysiology and for disease monitoring and therapeutic development.

Objectives: To investigate the spatiotemporal characteristics of sleep waveforms in temporal lobe epilepsy (TLE) and examine their association with cognition. Methods: In this retrospective, cross-sectional study, we examined overnight EEG data from adult patients with temporal lobe epilepsy (TLE) and non-epilepsy comparisons (NEC) admitted to the epilepsy monitoring unit (EMU) at Mass General Brigham hospitals. Automated algorithms were used to characterize sleep macro-architecture (sleep stages), and micro-architecture (spindles, slow oscillations) on scalp EEG, and to detect hippocampal interictal epileptiform discharges (hIEDs) from foramen ovale (FO) electrodes simultaneously recorded in a subset of TLE patients. We examined the association of sleep features and hIEDs with memory and executive function from clinical neuropsychological evaluations. Results: A total of 81 TLE and 28 NEC adult patients were included with similar mean ages. There were no significant differences in sleep macro-architecture between groups, including relative time spent in each sleep stage, sleep efficiency, and sleep fragmentation. In contrast, the spatiotemporal characteristics of sleep micro-architecture were altered in TLE compared to NEC and were associated with cognitive impairments. Specifically, we observed a ∼30% reduction in spindle density in TLE compared to NEC patients, which was significantly associated with worse memory performance. Spindle-slow oscillation (SO) coupling strength was also reduced in TLE and, in contrast to spindles, was associated with diminished executive function. We found no significant association between sleep macro- and micro-architectural parameters and hIEDs. Conclusion: There is a fundamental alteration of sleep micro-architecture in TLE, characterized by a reduction in spindle density and spindle-SO coupling, and these changes may contribute to neurocognitive comorbidity in this disorder.