Abnormal gamma phase-amplitude coupling in the parahippocampal cortex is associated with network hyperexcitability in Alzheimer's disease.

While animal models of Alzheimer's disease (AD) have shown altered gamma oscillations (∼40 Hz) in local neural circuits, the low signal-to-noise ratio of gamma in the resting human brain precludes its quantification via conventional spectral estimates. Phase-amplitude coupling (PAC) indicating the dynamic integration between the gamma amplitude and the phase of low-frequency (4-12 Hz) oscillations is a useful alternative to capture local gamma activity. In addition, PAC is also an index of neuronal excitability as the phase of low-frequency oscillations that modulate gamma amplitude, effectively regulates the excitability of local neuronal firing. In this study, we sought to examine the local neuronal activity and excitability using gamma PAC, within brain regions vulnerable to early AD pathophysiology-entorhinal cortex and parahippocampus, in a clinical population of patients with AD and age-matched controls. Our clinical cohorts consisted of a well-characterized cohort of AD patients (n = 50; age, 60 ± 8 years) with positive AD biomarkers, and age-matched, cognitively unimpaired controls (n = 35; age, 63 ± 5.8 years). We identified the presence or the absence of epileptiform activity in AD patients (AD patients with epileptiform activity, AD-EPI+, n = 20; AD patients without epileptiform activity, AD-EPI-, n = 30) using long-term electroencephalography (LTM-EEG) and 1-hour long magnetoencephalography (MEG) with simultaneous EEG. Using the source reconstructed MEG data, we computed gamma PAC as the coupling between amplitude of the gamma frequency (30-40 Hz) with phase of the theta (4-8 Hz) and alpha (8-12 Hz) frequency oscillations, within entorhinal and parahippocampal cortices. We found that patients with AD have reduced gamma PAC in the left parahippocampal cortex, compared to age-matched controls. Furthermore, AD-EPI+ patients showed greater reductions in gamma PAC than AD-EPI- in bilateral parahippocampal cortices. In contrast, entorhinal cortices did not show gamma PAC abnormalities in patients with AD. Our findings demonstrate the spatial patterns of altered gamma oscillations indicating possible region-specific manifestations of network hyperexcitability within medial temporal lobe regions vulnerable to AD pathophysiology. Greater deficits in AD-EPI+ suggests that reduced gamma PAC is a sensitive index of network hyperexcitability in AD patients. Collectively, the current results emphasize the importance of investigating the role of neural circuit hyperexcitability in early AD pathophysiology and explore its potential as a modifiable contributor to AD pathobiology.

Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). METHODS: SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). RESULTS: Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. CONCLUSIONS: These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Neurophysiological trajectories in Alzheimer's disease progression.

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-ß and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

Latest advances in mechanisms of epileptic activity in Alzheimer's disease and dementia with Lewy Bodies.

Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) stand as the prevailing sources of neurodegenerative dementia, impacting over 55 million individuals across the globe. Patients with AD and DLB exhibit a higher prevalence of epileptic activity compared to those with other forms of dementia. Seizures can accompany AD and DLB in early stages, and the associated epileptic activity can contribute to cognitive symptoms and exacerbate cognitive decline. Aberrant neuronal activity in AD and DLB may be caused by several mechanisms that are not yet understood. Hyperexcitability could be a biomarker for early detection of AD or DLB before the onset of dementia. In this review, we compare and contrast mechanisms of network hyperexcitability in AD and DLB. We examine the contributions of genetic risk factors, Ca2+ dysregulation, glutamate, AMPA and NMDA receptors, mTOR, pathological amyloid beta, tau and α-synuclein, altered microglial and astrocytic activity, and impaired inhibitory interneuron function. By gaining a deeper understanding of the molecular mechanisms that cause neuronal hyperexcitability, we might uncover therapeutic approaches to effectively ease symptoms and slow down the advancement of AD and DLB.

Improving genetic risk modeling of dementia from real-world data in underrepresented populations.

BACKGROUND: Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. METHODS: We employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation. RESULTS: Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to the APOE and the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. CONCLUSIONS: Our study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.

Multi-class Modeling Identifies Shared Genetic Risk for Late-onset Epilepsy and Alzheimer's Disease.

Background: Previous studies have established a strong link between late-onset epilepsy (LOE) and Alzheimer's disease (AD). However, their shared genetic risk beyond the APOE gene remains unclear. Our study sought to examine the shared genetic factors of AD and LOE, interpret the biological pathways involved, and evaluate how AD onset may be mediated by LOE and shared genetic risks. Methods: We defined phenotypes using phecodes mapped from diagnosis codes, with patients' records aged 60-90. A two-step Least Absolute Shrinkage and Selection Operator (LASSO) workflow was used to identify shared genetic variants based on prior AD GWAS integrated with functional genomic data. We calculated an AD-LOE shared risk score and used it as a proxy in a causal mediation analysis. We used electronic health records from an academic health center (UCLA Health) for discovery analyses and validated our findings in a multi-institutional EHR database (All of Us). Results: The two-step LASSO method identified 34 shared genetic loci between AD and LOE, including the APOE region. These loci were mapped to 65 genes, which showed enrichment in molecular functions and pathways such as tau protein binding and lipoprotein metabolism. Individuals with high predicted shared risk scores have a higher risk of developing AD, LOE, or both in their later life compared to those with low-risk scores. LOE partially mediates the effect of AD-LOE shared genetic risk on AD (15% proportion mediated on average). Validation results from All of Us were consistent with findings from the UCLA sample. Conclusions: We employed a machine learning approach to identify shared genetic risks of AD and LOE. In addition to providing substantial evidence for the significant contribution of the APOE-TOMM40-APOC1 gene cluster to shared risk, we uncovered novel genes that may contribute. Our study is one of the first to utilize All of Us genetic data to investigate AD, and provides valuable insights into the potential common and disease-specific mechanisms underlying AD and LOE, which could have profound implications for the future of disease prevention and the development of targeted treatment strategies to combat the co-occurrence of these two diseases.

Improving genetic risk modeling of dementia from real-world data in underrepresented populations.

BACKGROUND: Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. METHODS: We employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation. RESULTS: Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to the APOEand the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. CONCLUSIONS: Our study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.

Neurophysiological trajectories in Alzheimer's disease progression.

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-ß and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

Clinical, imaging, and biomarker evidence of amyloid- and tau-related neurodegeneration in late-onset epilepsy of unknown etiology.

Accumulating evidence suggests amyloid and tau-related neurodegeneration may play a role in development of late-onset epilepsy of unknown etiology (LOEU). In this article, we review recent evidence that epilepsy may be an initial manifestation of an amyloidopathy or tauopathy that precedes development of Alzheimer's disease (AD). Patients with LOEU demonstrate an increased risk of cognitive decline, and patients with AD have increased prevalence of preceding epilepsy. Moreover, investigations of LOEU that use CSF biomarkers and imaging techniques have identified preclinical neurodegeneration with evidence of amyloid and tau deposition. Overall, findings to date suggest a relationship between acquired, non-lesional late-onset epilepsy and amyloid and tau-related neurodegeneration, which supports that preclinical or prodromal AD is a distinct etiology of late-onset epilepsy. We propose criteria for assessing elevated risk of developing dementia in patients with late-onset epilepsy utilizing clinical features, available imaging techniques, and biomarker measurements. Further research is needed to validate these criteria and assess optimal treatment strategies for patients with probable epileptic preclinical AD and epileptic prodromal AD.

Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep.

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Dynamic functional connectivity MEG features of Alzheimer's disease.

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

Putting the Brakes on Accelerated Cognitive Decline in Alzheimer's Disease with Epileptic Activity.

Epileptic activity is known to exacerbate Alzheimer's disease (AD) pathology and worsen disease course. However, few studies have assessed whether treating epileptic activity with antiseizure drugs (ASDs) can improve patient outcomes. The current study by Hautecloque-Raysz et al. shows that patients with prodromal AD and epilepsy (epAD) fare well with ASD treatment, achieving seizure control in a large majority of cases using low dosage ASDs in monotherapy. Compared to slowly progressing AD patients without epilepsy, treated epAD patients experienced a similarly slow cognitive decline. These results suggest that ASDs that suppress seizures can improve outcomes in AD patients with epileptic activity.

Impaired Speaking-Induced Suppression in Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disease involving cognitive impairment and abnormalities in speech and language. Here, we examine how AD affects the fidelity of auditory feedback predictions during speaking. We focus on the phenomenon of speaking-induced suppression (SIS), the auditory cortical responses' suppression during auditory feedback processing. SIS is determined by subtracting the magnitude of auditory cortical responses during speaking from listening to playback of the same speech. Our state feedback control (SFC) model of speech motor control explains SIS as arising from the onset of auditory feedback matching a prediction of that feedback onset during speaking, a prediction that is absent during passive listening to playback of the auditory feedback. Our model hypothesizes that the auditory cortical response to auditory feedback reflects the mismatch with the prediction: small during speaking, large during listening, with the difference being SIS. Normally, during speaking, auditory feedback matches its predictions, then SIS will be large. Any reductions in SIS will indicate inaccuracy in auditory feedback prediction not matching the actual feedback. We investigated SIS in AD patients [n = 20; mean (SD) age, 60.77 (10.04); female (%), 55.00] and healthy controls [n = 12; mean (SD) age, 63.68 (6.07); female (%), 83.33] through magnetoencephalography (MEG)-based functional imaging. We found a significant reduction in SIS at ∼100 ms in AD patients compared with healthy controls (linear mixed effects model, F (1,57.5) = 6.849, p = 0.011). The results suggest that AD patients generate inaccurate auditory feedback predictions, contributing to abnormalities in AD speech.

Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.

Alzheimer's disease (AD) is the most common form of dementia, progressively impairing memory and cognition. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify abnormal biophysical mechanisms underlying these abnormal electrophysiological patterns, we estimated the parameters of a spectral graph-theory model (SGM). SGM is an analytic model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. The long-range excitatory time scale was associated with greater deficits in global cognition and was able to distinguish AD patients from controls with high accuracy. These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the spatiospectral signatures and cognition in AD.

Alzheimer's disease phenotypes show different sleep architecture.

INTRODUCTION: Sleep-wake disturbances are a prominent feature of Alzheimer's disease (AD). Atypical (non-amnestic) AD syndromes have different patterns of cortical vulnerability to AD. We hypothesized that atypical AD also shows differential vulnerability in subcortical nuclei that will manifest as different patterns of sleep dysfunction. METHODS: Overnight electroencephalography monitoring was performed on 48 subjects, including 15 amnestic, 19 atypical AD, and 14 controls. AD was defined based on neuropathological or biomarker confirmation. We compared sleep architecture by visual scoring and spectral power analysis in each group. RESULTS: Overall, AD cases showed increased sleep fragmentation and N1 sleep compared to controls. Compared to atypical AD groups, typical AD showed worse N3 sleep dysfunction and relatively preserved rapid eye movement (REM) sleep. DISCUSSION: Results suggest differing effects of amnestic and atypical AD variants on slow wave versus REM sleep, respectively, corroborating the hypothesis of differential selective vulnerability patterns of the subcortical nuclei within variants. Optimal symptomatic treatment for sleep dysfunction in clinical phenotypes may differ. HIGHLIGHTS: Alzheimer's disease (AD) variants show distinct patterns of sleep impairment. Amnestic/typical AD has worse N3 slow wave sleep (SWS) impairment compared to atypical AD. Atypical AD shows more rapid eye movement deficits than typical AD. Selective vulnerability patterns in subcortical areas may underlie sleep differences. Relatively preserved SWS may explain better memory scores in atypical versus typical AD.

Tauopathy and Epilepsy Comorbidities and Underlying Mechanisms.

Tau is a microtubule-associated protein known to bind and promote assembly of microtubules in neurons under physiological conditions. However, under pathological conditions, aggregation of hyperphosphorylated tau causes neuronal toxicity, neurodegeneration, and resulting tauopathies like Alzheimer's disease (AD). Clinically, patients with tauopathies present with either dementia, movement disorders, or a combination of both. The deposition of hyperphosphorylated tau in the brain is also associated with epilepsy and network hyperexcitability in a variety of neurological diseases. Furthermore, pharmacological and genetic targeting of tau-based mechanisms can have anti-seizure effects. Suppressing tau phosphorylation decreases seizure activity in acquired epilepsy models while reducing or ablating tau attenuates network hyperexcitability in both Alzheimer's and epilepsy models. However, it remains unclear whether tauopathy and epilepsy comorbidities are mediated by convergent mechanisms occurring upstream of epileptogenesis and tau aggregation, by feedforward mechanisms between the two, or simply by coincident processes. In this review, we investigate the relationship between tauopathies and seizure disorders, including temporal lobe epilepsy (TLE), post-traumatic epilepsy (PTE), autism spectrum disorder (ASD), Dravet syndrome, Nodding syndrome, Niemann-Pick type C disease (NPC), Lafora disease, focal cortical dysplasia, and tuberous sclerosis complex. We also explore potential mechanisms implicating the role of tau kinases and phosphatases as well as the mammalian target of rapamycin (mTOR) in the promotion of co-pathology. Understanding the role of these co-pathologies could lead to new insights and therapies targeting both epileptogenic mechanisms and cognitive decline.

Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer's disease.

Background: Neuronal- and circuit-level abnormalities of excitation and inhibition are shown to be associated with tau and amyloid-beta (Aß) in preclinical models of Alzheimer's disease (AD). These relationships remain poorly understood in patients with AD. Methods: Using empirical spectra from magnetoencephalography and computational modeling (neural mass model), we examined excitatory and inhibitory parameters of neuronal subpopulations and investigated their specific associations to regional tau and Aß, measured by positron emission tomography, in patients with AD. Results: Patients with AD showed abnormal excitatory and inhibitory time-constants and neural gains compared to age-matched controls. Increased excitatory time-constants distinctly correlated with higher tau depositions while increased inhibitory time-constants distinctly correlated with higher Aß depositions. Conclusions: Our results provide critical insights about potential mechanistic links between abnormal neural oscillations and cellular correlates of impaired excitatory and inhibitory synaptic functions associated with tau and Aß in patients with AD. Funding: This study was supported by the National Institutes of Health grants: K08AG058749 (KGR), F32AG050434-01A1 (KGR), K23 AG038357 (KAV), P50 AG023501, P01 AG19724 (BLM), P50-AG023501 (BLM and GDR), R01 AG045611 (GDR); AG034570, AG062542 (WJ); NS100440 (SSN), DC176960 (SSN), DC017091 (SSN), AG062196 (SSN); a grant from John Douglas French Alzheimer's Foundation (KAV); grants from Larry L. Hillblom Foundation: 2015-A-034-FEL (KGR), 2019-A-013-SUP (KGR); grants from the Alzheimer's Association: AARG-21-849773 (KGR); PCTRB-13-288476 (KAV), and made possible by Part the CloudTM (ETAC-09-133596); a grant from Tau Consortium (GDR and WJJ), and a gift from the S. D. Bechtel Jr. Foundation.

Subcortical Neuronal Correlates of Sleep in Neurodegenerative Diseases.

Importance: Sleep disturbance is common among patients with neurodegenerative diseases. Examining the subcortical neuronal correlates of sleep disturbances is important to understanding the early-stage sleep neurodegenerative phenomena. Objectives: To examine the correlation between the number of important subcortical wake-promoting neurons and clinical sleep phenotypes in patients with Alzheimer disease (AD) or progressive supranuclear palsy (PSP). Design, Setting, and Participants: This longitudinal cohort study enrolled 33 patients with AD, 20 patients with PSP, and 32 healthy individuals from the Memory and Aging Center of the University of California, San Francisco, between August 22, 2008, and December 31, 2020. Participants received electroencephalographic and polysomnographic sleep assessments. Postmortem neuronal analyses of brainstem hypothalamic wake-promoting neurons were performed and were included in the clinicopathological correlation analysis. No eligible participants were excluded from the study. Exposures: Electroencephalographic and polysomnographic assessment of sleep and postmortem immunohistological stereological analysis of 3 wake-promoting nuclei (noradrenergic locus coeruleus [LC], orexinergic lateral hypothalamic area [LHA], and histaminergic tuberomammillary nucleus [TMN]). Main Outcomes and Measures: Nocturnal sleep variables, including total sleep time, sleep maintenance, rapid eye movement (REM) latency, and time spent in REM sleep and stages 1, 2, and 3 of non-REM (NREM1, NREM2, and NREM3, respectively) sleep, and wake after sleep onset. Neurotransmitter, tau, and total neuronal counts of LC, LHA, and TMN. Results: Among 19 patients included in the clinicopathological correlation analysis, the mean (SD) age at death was 70.53 (7.75) years; 10 patients (52.6%) were female; and all patients were White. After adjusting for primary diagnosis, age, sex, and time between sleep analyses and death, greater numbers of LHA and TMN neurons were correlated with decreased homeostatic sleep drive, as observed by less total sleep time (LHA: r = -0.63; P = .009; TMN: r = -0.62; P = .008), lower sleep maintenance (LHA: r = -0.85; P < .001; TMN: r = -0.78; P < .001), and greater percentage of wake after sleep onset (LHA: r = 0.85; P < .001; TMN: r = 0.78; P < .001). In addition, greater numbers of LHA and TMN neurons were correlated with less NREM2 sleep (LHA: r = -0.76; P < .001; TMN: r = -0.73; P < .001). A greater number of TMN neurons was also correlated with less REM sleep (r = -0.61; P = .01). A greater number of LC neurons was mainly correlated with less total sleep time (r = -0.68; P = .008) and greater REM latency (r = 0.71; P = .006). The AD-predominant group had significantly greater sleep drive, including higher total sleep time (mean [SD], 0.49 [1.18] vs -1.09 [1.37]; P = .03), higher sleep maintenance (mean [SD], 0.18 [1.22] vs -1.53 [1.78]; P = .02), and lower percentage of wake after sleep onset during sleep period time (mean [SD], -0.18 [1.20] vs 1.49 [1.72]; P = .02) than the PSP-predominant group based on unbiased k-means clustering and principal component analyses. Conclusions and Relevance: In this cohort study, subcortical wake-promoting neurons were significantly correlated with sleep phenotypes in patients with AD and PSP, suggesting that the loss of wake-promoting neurons among patients with neurodegenerative conditions may disturb the control of sleep-wake homeostasis. These findings suggest that the subcortical system is a primary mechanism associated with sleep disturbances in the early stages of neurodegenerative diseases.

Neuronal synchrony abnormalities associated with subclinical epileptiform activity in early-onset Alzheimer's disease.

Since the first demonstrations of network hyperexcitability in scientific models of Alzheimer's disease, a growing body of clinical studies have identified subclinical epileptiform activity and associated cognitive decline in patients with Alzheimer's disease. An obvious problem presented in these studies is lack of sensitive measures to detect and quantify network hyperexcitability in human subjects. In this study we examined whether altered neuronal synchrony can be a surrogate marker to quantify network hyperexcitability in patients with Alzheimer's disease. Using magnetoencephalography (MEG) at rest, we studied 30 Alzheimer's disease patients without subclinical epileptiform activity, 20 Alzheimer's disease patients with subclinical epileptiform activity and 35 age-matched controls. Presence of subclinical epileptiform activity was assessed in patients with Alzheimer's disease by long-term video-EEG and a 1-h resting MEG with simultaneous EEG. Using the resting-state source-space reconstructed MEG signal, in patients and controls we computed the global imaginary coherence in alpha (8-12 Hz) and delta-theta (2-8 Hz) oscillatory frequencies. We found that Alzheimer's disease patients with subclinical epileptiform activity have greater reductions in alpha imaginary coherence and greater enhancements in delta-theta imaginary coherence than Alzheimer's disease patients without subclinical epileptiform activity, and that these changes can distinguish between Alzheimer's disease patients with subclinical epileptiform activity and Alzheimer's disease patients without subclinical epileptiform activity with high accuracy. Finally, a principal component regression analysis showed that the variance of frequency-specific neuronal synchrony predicts longitudinal changes in Mini-Mental State Examination in patients and controls. Our results demonstrate that quantitative neurophysiological measures are sensitive biomarkers of network hyperexcitability and can be used to improve diagnosis and to select appropriate patients for the right therapy in the next-generation clinical trials. The current results provide an integrative framework for investigating network hyperexcitability and network dysfunction together with cognitive and clinical correlates in patients with Alzheimer's disease.

Effect of Levetiracetam on Cognition in Patients With Alzheimer Disease With and Without Epileptiform Activity: A Randomized Clinical Trial.

Importance: Network hyperexcitability may contribute to cognitive dysfunction in patients with Alzheimer disease (AD). Objective: To determine the ability of the antiseizure drug levetiracetam to improve cognition in persons with AD. Design, Setting, and Participants: The Levetiracetam for Alzheimer's Disease-Associated Network Hyperexcitability (LEV-AD) study was a phase 2a randomized double-blinded placebo-controlled crossover clinical trial of 34 adults with AD that was conducted at the University of California, San Francisco, and the University of Minnesota, Twin Cities, between October 16, 2014, and July 21, 2020. Participants were adults 80 years and younger who had a Mini-Mental State Examination score of 18 points or higher and/or a Clinical Dementia Rating score of less than 2 points. Screening included overnight video electroencephalography and a 1-hour resting magnetoencephalography examination. Interventions: Group A received placebo twice daily for 4 weeks followed by a 4-week washout period, then oral levetiracetam, 125 mg, twice daily for 4 weeks. Group B received treatment using the reverse sequence. Main Outcomes and Measures: The primary outcome was the ability of levetiracetam treatment to improve executive function (measured by the National Institutes of Health Executive Abilities: Measures and Instruments for Neurobehavioral Evaluation and Research [NIH-EXAMINER] composite score). Secondary outcomes were cognition (measured by the Stroop Color and Word Test [Stroop] interference naming subscale and the Alzheimer's Disease Assessment Scale-Cognitive Subscale) and disability. Exploratory outcomes included performance on a virtual route learning test and scores on cognitive and functional tests among participants with epileptiform activity. Results: Of 54 adults assessed for eligibility, 11 did not meet study criteria, and 9 declined to participate. A total of 34 adults (21 women [61.8%]; mean [SD] age, 62.3 [7.7] years) with AD were enrolled and randomized (17 participants to group A and 17 participants to group B). Thirteen participants (38.2%) were categorized as having epileptiform activity. In total, 28 participants (82.4%) completed the study, 10 of whom (35.7%) had epileptiform activity. Overall, treatment with levetiracetam did not change NIH-EXAMINER composite scores (mean difference vs placebo, 0.07 points; 95% CI, -0.18 to 0.32 points; P = .55) or secondary measures. However, among participants with epileptiform activity, levetiracetam treatment improved performance on the Stroop interference naming subscale (net improvement vs placebo, 7.4 points; 95% CI, 0.2-14.7 points; P = .046) and the virtual route learning test (t = 2.36; Cohen f2 = 0.11; P = .02). There were no treatment discontinuations because of adverse events. Conclusions and Relevance: In this randomized clinical trial, levetiracetam was well tolerated and, although it did not improve the primary outcome, in prespecified analysis, levetiracetam improved performance on spatial memory and executive function tasks in patients with AD and epileptiform activity. These exploratory findings warrant further assessment of antiseizure approaches in AD. Trial Registration: ClinicalTrials.gov Identifier: NCT02002819.