Faculty Bibliography

BACKGROUND:Preclinical studies suggest body temperature (Tb) and consequently brain temperature has the potential to bidirectionally interact with tau pathology in Alzheimer's Disease (AD). Tau phosphorylation is substantially increased by small (<1 °C) decreases in temperature within the human physiological range, and thermoregulatory nuclei are affected by tau pathology early in the AD continuum. In this study we evaluated whether Tb (as a proxy for brain temperature) is cross-sectionally associated with clinically utilized markers of tau pathology in cognitively normal older adults. METHODS:Tb was continuously measured with ingestible telemetry sensors for 48-h. This period also included two nights of nocturnal polysomnography to delineate whether Tb during waking vs sleep is differentially associated with tau pathology. Tau phosphorylation was assessed with plasma and cerebrospinal fluid (CSF) tau phosphorylated at threonine 181 (P-tau), sampled the day following Tb measurement. In addition, neurofibrillary tangle (NFT) burden in early Braak stage regions was imaged with PET-MR using the [18F]MK-6240 radiotracer on average one month later. RESULTS:Lower Tb was associated with increased NFT burden, as well as increased plasma and CSF P-tau levels (p < 0.05). NFT burden was associated with lower Tb during waking (p < 0.05) but not during sleep intervals. Plasma and CSF Ptau levels were highly correlated with each other (p < 0.05), and both variables were correlated with tau tangle radiotracer uptake (p < 0.05). CONCLUSIONS:These results, the first available for human, suggest that lower Tb in older adults may be associated with increased soluble and aggregated tau pathology. Our findings add to the substantial preclinical literature associating lower body and brain temperature with tau hyperphosphorylation. CLINICAL TRIAL NUMBER/BACKGROUND:NCT03053908.

BACKGROUND:There is high prevalence of neuropsychiatric symptoms (NPS) among dementia patients. NPS are correlated with dementia progression, functional decline, early institutionalization, and death. There is scarce evidence on the progression of NPS in the latest stages of dementia. OBJECTIVE:To describe the prevalence of NPS in mild-moderate to severe dementia and to reveal the progression of each NPS over time. METHODS:We studied 317 patients (77.3% female, average age: 81.5 years) with a DSM-IV-TR diagnosis of dementia. This is a cross-sectional, and a prospective longitudinal study with 78-month follow-up. We assessed cognitive status (Mini-Mental State Examination and Severe Mini-Mental State Examination), dementia severity (Global Deterioration Scale and Clinical Dementia Rating), and psychopathological measures (Neuropsychiatric Inventory, APADEM-Nursing Home, Apathy Inventory, Cornell Scale for Depression in Dementia, and Cohen-Mansfield Agitation Inventory). RESULTS:Overall prevalence of NPS was 94.6%, being apathy the most prevalent (66.7%) and the one whose severity increased the most with progression of dementia. Agitation/aggression, irritability, and sleeping and eating disorders also increased over time. Delusions and depressive symptoms decreased in severity with disease progression. In severe dementia, female displayed more severe depressive symptoms and eating disorders, while male displayed more agitation/aggression and sleep disturbances. CONCLUSION/CONCLUSIONS:NPS in dementia follow a heterogeneous course. Apathy is the most prevalent NPS and the one that worsens most significantly over time. The course of some NPS differs between sexes. Further research is required to understand the evolution of NPS at advanced stages of dementia.

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

STUDY OBJECTIVES/OBJECTIVE:Determine if changes in K-complexes associated with sustained inspiratory airflow limitation (SIFL) during N2 sleep are associated with next-day vigilance and objective sleepiness. METHODS:Data from thirty subjects with moderate-to-severe OSA who completed three in-lab polysomnograms: diagnostic, on therapeutic continuous positive airway pressure (CPAP), and on suboptimal CPAP (4cmH20 below optimal titrated CPAP level) were analyzed. Four 20-min psychomotor vigilance tests (PVT) were performed after each PSG, every two hours. Changes in proportion of spontaneous K-complexes and spectral characteristics surrounding K-complexes were evaluated for K-complexes associated with both delta (∆SWAK), alpha (∆αK) frequencies. RESULTS:Suboptimal CPAP induced SIFL (14.7(20.9) vs. 2.9(9.2); %total sleep time, p<0.001) with a small increase in apnea hypopnea index (AHI3A: 6.5(7.7) vs. 1.9(2.3); p<0.01) versus optimal CPAP. K-complex density (num./min of stage N2) was higher on suboptimal CPAP (0.97±0.7 vs. 0.65±0.5, #/min, mean±SD, p<0.01) above and beyond the effect of age, sex, AHI3A, and duration of SIFL. A decrease in ∆SWAK with suboptimal CPAP was associated with increased PVT lapses and explained 17% of additional variance in PVT lapses. Within-night during suboptimal CPAP K-complexes appeared to alternate between promoting sleep and as arousal surrogates. EEG changes were not associated with objective sleepiness. CONCLUSIONS:Sustained inspiratory airflow limitation is associated with altered K-complex morphology including increased occurrence of K-complexes with bursts of alpha as arousal surrogates. These findings suggest that sustained inspiratory flow limitation may be associated with non-visible sleep fragmentation and contribute to increased lapses in vigilance.

Obstructive sleep apnea (OSA) is thought to affect almost 1 billion people worldwide. OSA has well established cardiovascular and neurocognitive sequelae, although the optimal metric to assess its severity and/or potential response to therapy remains unclear. The apnea-hypopnea index (AHI) is well established; thus, we review its history and predictive value in various different clinical contexts. Although the AHI is often criticized for its limitations, it remains the best studied metric of OSA severity, albeit imperfect. We further review the potential value of alternative metrics including hypoxic burden, arousal intensity, odds ratio product, and cardiopulmonary coupling. We conclude with possible future directions to capture clinically meaningful OSA endophenotypes including the use of genetics, blood biomarkers, machine/deep learning and wearable technologies. Further research in OSA should be directed towards providing diagnostic and prognostic information to make the OSA diagnosis more accessible and to improving prognostic information regarding OSA consequences, in order to guide patient care and to help in the design of future clinical trials.

Sleep disorders, despite being very frequent in adults with Down syndrome (DS), are often overlooked due to a lack of awareness by families and physicians and the absence of specific clinical sleep guidelines. Untreated sleep disorders have a negative impact on physical and mental health, behavior, and cognitive performance. Growing evidence suggests that sleep disruption may also accelerate the progression to symptomatic Alzheimer's disease (AD) in this population. It is therefore imperative to have a better understanding of the sleep disorders associated with DS in order to treat them, and in doing so, improve cognition and quality of life, and prevent related comorbidities. This paper reviews the current knowledge of the main sleep disorders in adults with DS, including evaluation and management. It highlights the existing gaps in knowledge and discusses future directions to achieve earlier diagnosis and better treatment of sleep disorders most frequently found in this population.

BACKGROUND:Inflammatory mechanisms are believed to contribute to the manifestation of major depressive disorder (MDD). Central cholinergic activity may moderate this effect. Here, we tested if volume of the cholinergic basal forebrain is associated with cerebrospinal fluid (CSF) levels of sTREM2 as a marker of microglial activation in people with late life MDD. METHODS:Basal forebrain volume was determined from structural MRI scans and levels of CSF sTREM2 with immunoassay in 29 people with late-life MDD and 20 healthy older controls at baseline and 3 years follow-up. Associations were determined using Bayesian analysis of covariance. RESULTS:and total tau. Evidence was in favor of absence of an effect for baseline levels of CSF sTREM2 in MDD cases and for baseline and follow up data in controls. LIMITATIONS/CONCLUSIONS:The sample size of repeated CSF examinations was relatively small. Therefore, we used Bayesian sequential analysis to assess if effects were affected by sample size. Still, the number of cases was too small to stratify effects for different antidepressive treatments. CONCLUSIONS:Our data agree with the assumption that central cholinergic system integrity may contribute to regulation of microglia activity in late-life MDD.

OBJECTIVE:Sleep-dependent memory processing occurs in animals including humans, and disturbed sleep negatively affects memory. Sleep disturbance and memory dysfunction are common in multiple sclerosis (MS), but little is known about the contributions of sleep disturbance to memory in MS. We investigated whether subjective sleep disturbance is linked to worse memory in early MS independently of potential confounders. METHODS:Persons with early MS (n = 185; ≤5.0 years diagnosed) and demographically matched healthy controls (n = 50) completed four memory tests to derive a memory composite, and four speeded tests to derive a cognitive efficiency composite. Z-scores were calculated relative to healthy controls. Sleep disturbance was defined by the Insomnia Severity Index score ≥ 10. ANCOVAs examined differences in memory and cognitive efficiency between patients with and without sleep disturbance controlling for potential confounds (e.g., mood, fatigue, disability, T2 lesion volume, gray matter volume). Comparisons were made to healthy controls. RESULTS:Seventy-four (40%) patients reported sleep disturbance. Controlling for all covariates, patients with sleep disturbance had worse memory (z = -0.617; 95% CI: -0.886, -0.348) than patients without disturbance (z = -0.171, -0.425, 0.082, P = .003). Cognitive efficiency did not differ between groups. Relative to healthy controls, memory was worse among patients with sleep disturbance, but not among patients without sleep disturbance. INTERPRETATION/CONCLUSIONS:Sleep disturbance contributes to MS memory dysfunction, which may help explain differential risk for memory dysfunction in persons with MS, especially since sleep disturbance is common in MS. Potential mechanisms linking sleep disturbance and memory are discussed, as well as recommendations for further mechanistic and interventional research.

BACKGROUND:Recent advancement in deep learning provides a pivotal opportunity to potentially supplement or supplant the limiting step of manual sleep scoring. NEW METHOD/UNASSIGNED:In this paper, we characterize the WaveSleepNet (WSN), a deep convolutional neural network (CNN) that uses wavelet transformed images of mouse EEG/EMG signals to autoscore sleep and wake. RESULTS:WSN achieves an epoch by epoch mean accuracy of 0.86 and mean F1 score of 0.82 compared to manual scoring by a human expert. In mice experiencing mechanically induced sleep fragmentation, an overall epoch by epoch mean accuracy of 0.80 is achieved by WSN and classification of non-REM (NREM) sleep is not compromised, but the high level of sleep fragmentation results in WSN having greater difficulty differentiating REM from NREM sleep. We also find that WSN achieves similar levels of accuracy on an independent dataset of externally acquired EEG/EMG recordings with an overall epoch by epoch accuracy of 0.91. We also compared conventional summary sleep metrics in mice sleeping ad libitum. WSN systematically biases sleep fragmentation metrics of bout number and bout length leading to an overestimated degree of sleep fragmentation. COMPARISON WITH EXISTING METHODS/UNASSIGNED:In a cross-validation, WSN has a greater macro and stage-specific accuracy compared to a conventional random forest classifier. Examining the WSN, we find that it automatically learns spectral features consistent with manual scoring criteria that are used to define each class. CONCLUSION/CONCLUSIONS:These results suggest to us that WSN is capable of learning visually agreeable features and may be useful as a supplement to human manual scoring.