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The extent of microglial heterogeneity in humans remains a central yet poorly explored question in light of the development of therapies targeting this cell type. Here, we investigate the population structure of live microglia purified from human cerebral cortex samples obtained at autopsy and during neurosurgical procedures. Using single cell RNA sequencing, we find that some subsets are enriched for disease-related genes and RNA signatures. We confirm the presence of four of these microglial subpopulations histologically and illustrate the utility of our data by characterizing further microglial cluster 7, enriched for genes depleted in the cortex of individuals with Alzheimer’s disease (AD). Histologically, these cluster 7 microglia are reduced in frequency in AD tissue, and we validate this observation in an independent set of single nucleus data. Thus, our live human microglia identify a range of subtypes, and we prioritize one of these as being altered in AD. Imbalance of microglial phenotypes in the aging brain might underlie their involvement in late onset neurodegenerative diseases. Here we report the population structure of microglia in the aged human brain and the reduction of a particular microglia subset in individuals with Alzheimer’s disease .

Abstract Study Objectives Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings. Methods Using a clinical dataset of polysomnograms from 6,431 patients (MGH–PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm’s inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH–PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24–72 h) scalp EEG recordings from 112 patients (scalpEEG dataset). Results The algorithm achieved a Cohen’s kappa of 0.74 on the MGH–PSG holdout testing set and cross-validated Cohen’s kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen’s kappa ~ 0.75 ± 0.11). The algorithm’s performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities. Conclusion We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.

We aimed to investigate the terms used to refer to cognitive and fatigue related side effects and their prevalence in phase III add-on clinical trials of anti-epileptic drugs (AEDs). We extracted data from publicly available FDA documents as well as the published literature. Target drug doses were then calculated as drug loads and divided into three categories (low, average, high). The odds ratio of developing the side effects was calculated for each drug load, and the presence of a dose–response effect was also assessed. We found that the cognitive terms used across trials were very variable, and data on discontinuation rates were limited. Placebo rates for cognitive side effects ranged from 0 to 10.6% while those for fatigue ranged from 2.5 to 37.7%. Keeping in mind the variable placebo rates and terminology, the majority of AEDs exhibited a clear dose response effect and significant odds ratios at high doses except brivaracetam and zonisamide for the cognitive side effects and tiagabine, topiramate, and zonisamide for the fatigue side effects. Due to their clinical relevance and impact on quality of life, new trials should make data related to the prevalence and discontinuation rates of these side effects publicly available. Given the clear dose response effect, physicians should consider aiming for lower drug loads and adjusting doses to improve tolerability.

This proceedings article summarizes the inaugural “T Cells in the Brain” symposium held at Columbia University. Experts gathered to explore the role of T cells in neurodegenerative diseases. Key topics included characterization of antigen‐specific immune responses, T cell receptor (TCR) repertoire, microbial etiology in Alzheimer's disease (AD), and microglia–T cell crosstalk, with a focus on how T cells affect neuroinflammation and AD biomarkers like amyloid beta and tau. The symposium also examined immunotherapies for AD, including the Valacyclovir Treatment of Alzheimer's Disease (VALAD) trial, and two clinical trials leveraging regulatory T cell approaches for multiple sclerosis and amyotrophic lateral sclerosis therapy. Additionally, single‐cell RNA/TCR sequencing of T cells and other immune cells provided insights into immune dynamics in neurodegenerative diseases. This article highlights key findings from the symposium and outlines future research directions to further understand the role of T cells in neurodegeneration, offering innovative therapeutic approaches for AD and other neurodegenerative diseases. Highlights Researchers gathered to discuss approaches to study T cells in brain disorders. New technologies allow high‐throughput screening of antigen‐specific T cells. Microbial infections can precede several serious and chronic neurological diseases. Central and peripheral T cell responses shape neurological disease pathology. Immunotherapy can induce regulatory T cell responses in neuroinflammatory disorders.

INTRODUCTION Sleep disturbances are common in Alzheimer's disease (AD) and may reflect pathologic changes in brain networks. To date, no studies have examined changes in sleep functional connectivity (FC) in AD or their relationship with network hyperexcitability and cognition. METHODS We assessed electroencephalogram (EEG) sleep FC in 33 healthy controls, 36 individuals with AD without epilepsy, and 14 individuals with AD and epilepsy. RESULTS AD participants showed increased gamma connectivity in stage 2 sleep (N2), which was associated with longitudinal cognitive decline. Network hyperexcitability in AD was associated with a distinct sleep connectivity signature, characterized by decreased N2 delta connectivity and reversal of several connectivity changes associated with AD. Machine learning algorithms using sleep connectivity features accurately distinguished diagnostic groups and identified “fast cognitive decliners” among study participants who had AD. DISCUSSION Our findings reveal changes in sleep functional networks associated with cognitive decline in AD and may have implications for disease monitoring and therapeutic development. Highlights Brain functional connectivity (FC) in Alzheimer's disease is altered during sleep. Sleep FC measures correlate with cognitive decline in AD. Network hyperexcitability in AD has a distinct sleep connectivity signature.

Late-onset unexplained epilepsy (LoUE), defined as epilepsy starting after age 55 with no clearly identified cause, has emerged as a significant risk factor for dementia. Individuals presenting with LoUE have no prior history of dementia. Yet, LoUE is associated with a 2-3x increased risk of developing dementia, and up to 25% of individuals with LoUE develop dementia within 4 years after their first seizure. We have little understanding of the mechanisms that underlie development of dementia in LoUE. The ELUCID Study (Epilepsy of Late-onset Unknown etiology as a risk factor for Cognitive Impairment and Dementia) is a multi-center, prospective longitudinal observational study of LoUE, focused on understanding mechanisms and predicting outcomes of mild cognitive impairment and dementia in LoUE. ELUCID will enroll 600 participants with LoUE (and without dementia) across 7 study sites. Participants undergo a baseline evaluation with clinical history, cognitive testing, brain MRI, overnight scalp EEG, and blood draw, and are followed longitudinally with interval history every 6 months and annual cognitive testing. The primary outcomes are development of mild cognitive impairment and dementia. To date, 67 ELUCID participants have completed their initial study visit, with mean age of 67.9±7.2 years and 38.8% female. The sample includes 89.6% White, 3% Black, 1.5% Asian, 6% unreported race, and 1.5% Hispanic ethnicity. Mean level of education was 16.9±2.7 years. Vascular risk factors were common, including hypertension (51%), hyperlipidemia (58%), diabetes mellitus (6%), coronary artery disease (9%), and obstructive sleep apnea (28%). A family history of seizures was present in 23.9% of participants, and a family history of dementia in 58%. Cognitive test scores largely fell within normal range, including: MMSE: 28.7±1.5; Logical Memory Delayed: 11.9±3.4; FCSRT Free Recall: 31.6±6.4; Trails B: 94.3±54.4; Digit Symbol Substitution: 41.9±10.1; and Category Fluency (animals): 17.0±4.9. Subjectively, 32.8% of participants felt their memory had worsened compared to 6 months prior. The ELUCID Study is a large longitudinal study of LoUE that will define its relationship to Alzheimer's disease and related dementias. Here we describe the study protocol and provide an early report of the baseline demographic and clinical characteristics of the accruing ELUCID study population.

We aimed to investigate the cognitive and psychosocial outcomes of patients older than 50 with drug-resistant temporal lobe epilepsy as compared to a younger cohort. One hundred and thirty-one patients with temporal lobe epilepsy (47% age ≥ 50) who underwent comprehensive neuropsychological testing were retrospectively identified. A comparison of percentage of Z scores < − 1.5 between the older and younger cohort on Trail Making Tests A and B, Boston Naming Test, Rey Auditory Verbal Learning Test (RAVLT) delayed recall, and Rey–Osterrieth complex figure test delayed recall was performed as well as the presence of disability due to epilepsy and depression scores. Grading of white matter hyperintensities on MRI was also performed. Older patients with epilepsy were more likely to score Z < − 1.5 on the RAVLT (54.1 vs 32.8%) and were more likely to be on disability due to their seizures (23.0 vs 5.7%). A higher grade of white matter hyperintensities correlated with worse performance on Trail Making Test A, while a higher number of anti-epileptic drugs (AEDs) correlated with worse performance on Trail Making Test B regardless of age. The results of this study reveal that older patients with drug-resistant epilepsy are a vulnerable population with an impaired cognitive profile. In addition, limiting the number of AEDs and addressing markers of small vessel disease should also be prioritized by clinicians.

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. 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. 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. 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.

Spindles and slow oscillations (SO) are fundamental elements of the NREM sleep microarchitecture, often co-occurring in a phase-dependent manner, and this cross-frequency coupling is critical for the temporal coordination of neural activity in sleep. However, spindles and SO occur at different times in different regions, and it is unclear how the coupling of these oscillations is organized across the brain. Here, we provide evidence in humans for a novel spatiotemporal organization of spindle-SO coupling, characterized by a of the SO phase of spindles along the brain's anterior-posterior axis. We show that this phase precession relationship is a robust phenomenon and can be quantified across individual subjects. Moreover, the integrity of phase precession strength and slope declines with advancing age. These findings provide new insight into the temporal coordination of sleep rhythms across brain space, linking this coordination to a canonical principle of neural coding.