Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study.

Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes.

NEURO-COVAX: An Italian Population-Based Study of Neurological Complications after COVID-19 Vaccinations.

OBJECTIVE: In this Italian population-based study, we aimed to evaluate the neurological complications after the first and/or second dose of COVID-19 vaccines and factors potentially associated with these adverse effects. METHODS: Our study included adults aged 18 years and older who received two vaccine doses in the vaccination hub of Novegro (Milan, Lombardy) between 7 and 16 July 2021. The NEURO-COVAX questionnaire was able to capture the neurological events, onset and duration. That data that were digitized centrally by the Lombardy region were used to match the demographic/clinical characteristics and identify a vulnerability profile. Associations between vaccine lines and the development of complications were assessed. Digital healthcare system matching was also performed to evaluate severe neurological complications (Guillain-Barrè syndrome, Bell's palsy, transverse myelitis, encephalitis) and the incidence of hospital admissions and/or the mortality rate after two doses of the vaccines. RESULTS: The NEURO-COVAX-cohort included 19.108 vaccinated people: 15.368 with BNT162b2, 2077 with mRNA-1273, 1651 with ChAdOx1nCov-19, and 12 with Ad26.COV2.S who were subsequently excluded. Approximately 31.2% of our sample developed post-vaccination neurological complications, particularly with ChAdOx1nCov-19. A vulnerable clinical profile emerged, where over 40% of the symptomatic people showed comorbidities in their clinical histories. Defining the neurological risk profile, we found an increased risk for ChAdOx1nCov-19 of tremors (vs. BNT162b2, OR: 5.12, 95% CI: 3.51-7.48); insomnia (vs. mRNA-1273, OR: 1.87, 95% CI: 1.02-3.39); muscle spasms (vs. BNT162b2, OR: 1.62, 95% CI: 1.08-2.46); and headaches (vs. BNT162b2, OR: 1.49, 95% CI: 0.96-1.57). For mRNA-1273, there were increased risks of parethesia (vs. ChAdOx1nCov-19, OR: 2.37, 95% CI: 1.48-3.79); vertigo (vs. ChAdOx1nCov-19, OR: 1.68, 95% CI: 1.20-2.35); diplopia (vs. ChAdOx1nCov-19, OR: 1.55, 95% CI: 0.67-3.57); and sleepiness (vs. ChAdOx1nCov-19, OR: 1.28, 95% CI: 0.98-1.67). In the period that ranged from March to August 2021, no one was hospitalized and/or died of severe complications related to COVID-19 vaccinations. DISCUSSION: This study estimates the prevalence and risk for neurological complications potentially associated with COVID-19 vaccines, thus improving the vaccination guidelines and loading in future personalized preventive medicine.

Genetics and epigenetics of rare hypersomnia.

Herein we focus on connections between genetics and some central disorders of hypersomnolence - narcolepsy types 1 and 2 (NT1, NT2), idiopathic hypersomnia (IH), and Kleine-Levin syndrome (KLS) - for a better understanding of their etiopathogenetic mechanisms and a better diagnostic and therapeutic definition. Gene pleiotropism influences neurological and sleep disorders such as hypersomnia; therefore, genetics allows us to uncover common pathways to different pathologies, with potential new therapeutic perspectives. An important body of evidence has accumulated on NT1 and IH, allowing a better understanding of etiopathogenesis, disease biomarkers, and possible new therapeutic approaches. Further studies are needed in the field of epigenetics, which has a potential role in the modulation of biological specific hypersomnia pathways.

A data-driven approach to neuropsychological features in isolated REM behaviour disorder: A latent class analysis.

Recent evidence demonstrated that neuropsychological assessment may be considered a valid marker of neurodegeneration in idiopathic REM sleep behaviour disorder (iRBD). However, little is known about the possible neuropsychological heterogeneity within the iRBD population. This retrospective study aimed to identify and describe different neuropsychological phenotypes in iRBD patients by means of a data-driven approach using latent class analysis. A total of 289 iRBD patients underwent a neuropsychological assessment evaluating cognitive domains: global cognition, language, short- and long-term memory, executive functions and visuospatial abilities. The presence of mild cognitive impairment (MCI) was also assessed. Latent class analysis was carried out to identify iRBD subtypes according to neuropsychological scores. The most parsimonious model identified three latent classes. Groups were labelled as follows: Class 2 "severely impaired" (n = 83/289): mean pathological scores in different tests, a high percentage of MCI multiple-domain and impairment in all neuropsychological domains. Class 1 "moderately impaired" (n = 44/289): mean neuropsychological score within the normal value, a high percentage of MCI (high risk to phenoconversion) and great impairment in the visuospatial domain. Class 3 "slightly impaired" (n = 162/289): no deficit worthy of attention except for short- and long-term memory. Our results suggest three different clinical phenotypes within the iRBD population. These findings may be relevant in the future for predicting the clinical trajectories of phenoconversion in iRBD.

Challenging functional connectivity data: machine learning application on essential tremor recognition.

BACKGROUND AND AIMS: This paper aimed to investigate the usefulness of applying machine learning on resting-state fMRI connectivity data to recognize the pattern of functional changes in essential tremor (ET), a disease characterized by slight brain abnormalities, often difficult to detect using univariate analysis. METHODS: We trained a support vector machine with a radial kernel on the mean signals extracted by 14 brain networks obtained from resting-state fMRI scans of 18 ET and 19 healthy control (CTRL) subjects. Classification performance between pathological and control subjects was evaluated using a tenfold cross-validation. Recursive feature elimination was performed to rank the importance of the extracted features. Moreover, univariate analysis using Mann-Whitney U test was also performed. RESULTS: The machine learning algorithm achieved an AUC of 0.75, with four networks (language, primary visual, cerebellum, and attention), which have an essential role in ET pathophysiology, being selected as the most important features for classification. By contrast, the univariate analysis was not able to find significant results among these two conditions. CONCLUSION: The machine learning approach identifies the changes in functional connectivity of ET patients, representing a promising instrument to discriminate specific pathological conditions and find novel functional biomarkers in resting-state fMRI studies.

A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder.

Background and purpose: Growing evidence suggests that Machine Learning (ML) models can assist the diagnosis of neurological disorders. However, little is known about the potential application of ML in diagnosing idiopathic REM sleep behavior disorder (iRBD), a parasomnia characterized by a high risk of phenoconversion to synucleinopathies. This study aimed to develop a model using ML algorithms to identify iRBD patients and test its accuracy. Methods: Data were acquired from 32 participants (20 iRBD patients and 12 controls). All subjects underwent a video-polysomnography. In all subjects, we measured the components of heart rate variability (HRV) during 24 h recordings and calculated night-to-day ratios (cardiac autonomic indices). Discriminating performances of single HRV features were assessed. ML models based on Logistic Regression (LR), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were trained on HRV data. The utility of HRV features and ML models for detecting iRBD was evaluated by area under the ROC curve (AUC), sensitivity, specificity and accuracy corresponding to optimal models. Results: Cardiac autonomic indices had low performances (accuracy 63-69%) in distinguishing iRBD from control subjects. By contrast, the RF model performed the best, with excellent accuracy (94%), sensitivity (95%) and specificity (92%), while XGBoost showed accuracy (91%), specificity (83%) and sensitivity (95%). The mean triangular index during wake (TIw) was the best discriminating feature between iRBD and HC, with 81% accuracy, reaching 84% accuracy when combined with VLF power during sleep using an LR model. Conclusions: Our findings demonstrated that ML algorithms can accurately identify iRBD patients. Our model could be used in clinical practice to facilitate the early detection of this form of RBD.

Cognitive functioning in essential tremor without dementia: a clinical and imaging study.

BACKGROUND AND AIMS: To explore the cognitive functioning of ET patients without dementia and delineate its imaging counterpart. METHODS: We enrolled 99 subjects (49 non-demented ET patients and 50 education-matched healthy controls) that underwent neuropsychological and MRI evaluation. In order to identify the cognitive parameters that better reflect the profile of ET patients, we used a double statistical approach: (i) direct comparison between groups and (ii) machine learning approach with feature selection. Then, to evaluate the correlation between cognitive performances and the degree of brain atrophy in the ET group, we included the results derived from the uni- and multivariate analysis in whole-brain voxel-based morphometry (VBM) model. RESULTS: In ET patients, the univariate analysis showed differences in cognitive tests evaluating executive functions (FAB, MCST-CA), verbal memory-delayed recall (RAVLT-DR), and working memory (Digit Span B). The relative scores were significantly worse compared to controls, although within the normal range (subclinical dysfunctions). The machine learning approach also provided similar findings: tests exploring the executive functions, verbal memory, and language (RAVLT-DR, FAB, COWAT, RAVLT-IR, TOKEN) showed the highest importance rank in classification's task. Regardless of the explored test, the MRI analysis revealed a correlation (p < 0.005 uncorrected, whole brain) between test scores and widespread areas including cerebellum, inferior and middle frontal cortices, cingulate cortices, and temporal cortex. CONCLUSION: This study improves the knowledge on cognitive impairment in ET, as our findings demonstrate a heterogeneous pattern of cognitive dysfunction involving memory, executive function, and language domains in the ET group. This clinical profile relates with the deep involvement of the cerebellum and its connections with large-scale brain structures, suggesting that changes spreading in wide-ranging brain pathways may contribute to the physiopathology of cognitive dysfunction in ET.

Aceruloplasminemia: a multimodal imaging study in an Italian family with a novel mutation.

OBJECTIVE: Structural abnormalities in thalami and basal ganglia, in particular the globus pallidus (GP), are a neuroimaging hallmark of hereditary aceruloplasminemia (HA), yet few functional imaging data exit in HA carriers. This study investigated the iron-related structural and functional abnormalities in an Italian HA family. METHODS: Multimodal imaging was used including structural 3 T MRI, functional imaging (SPECT imaging with 123I-ioflupane (DAT-SPECT), cardiac 123I metaiodobenzylguanidine (123I-MIBG) scintigraphy, and 18F-fluorodeoxyglucose (18F-FDG)-PET imaging). In the proband, MRI and scintigraphic evaluations were performed at baseline, 2 and 4 years (structural imaging), and 2 years of follow-up period (functional imaging). RESULTS: We investigated two cousins carrying a novel splicing homozygous mutation in intron 6 (IVS6 + 1 G > A) of CP gene. Interestingly, MRI features in both subjects were characterized by marked iron accumulation in the thalami and basal ganglia nuclei, while GP was not affected. MRI performed in the proband at 2 and 4 years of follow-up confirmed progressive neurodegeneration of the thalami and basal ganglia without the involvement of GP. Functional imaging showed reduced putaminal DAT uptake in both cousins, whereas cardiac MIBG and FDG uptakes performed in the proband were normal. Longitudinal scintigraphic investigations did not show significant changes over the time. CONCLUSIONS: For HA carriers, our findings demonstrate that GP was spared by iron accumulation over the time. The nigrostriatal presynaptic dopaminergic system was damaged while the cardiac sympathetic system remained longitudinally preserved, thus expanding the imaging features of this rare inherited disorder.

Microstructural changes in normal-appearing white matter in male sleep apnea patients are reversible after treatment: A pilot study.

Visually appreciable white matter (WM) changes have been described in obstructive sleep apnea (OSA). However, few data exist on the involvement of silent WM abnormalities. This prospective study investigated the microstructural integrity of normal-appearing white matter (NAWM) in male OSA patients before and after continuous positive airway pressure (CPAP) treatment, using a neuroimaging approach. Magnetic resonance imaging (MRI) was acquired from 32 participants (16 severe never-treated OSA and 16 controls). Diffusion tensor imaging (DTI) and Tract-Based Spatial Statistics (TBSS) were used to assess the microstructural NAWM changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). In order to evaluate the efficacy of the therapy, OSA patients underwent MRI evaluations at baseline and after 3 months of treatment (follow-up). CPAP treatment significantly increased the FA in NAWM of the brain stem, corpus callosum and bilateral internal capsule of OSA patients at follow-up compared to baseline (p < 0.05, TFCE-corrected). OSA patients also showed increases in AD in the corpus callosum, superior corona radiata, and internal capsule of the right hemisphere (p < 0.05, TFCE-corrected) after CPAP treatment. A significant negative correlation was found between the FA of the corona radiata, corpus callosum, internal capsule, limbic structures, and neuropsychological scores at follow-up evaluation. No significant differences were found in MD and RD of NAWM in our patients after treatment. Our results demonstrate that FA and AD of NAWM in major tracts such as the corpus callosum and the internal capsule increased significantly after CPAP treatment, as a potential beneficial effect of ventilatory therapy. The recovery of NAWM alterations might also be related to the improvement in the neurocognitive profile, suggesting that nonclearly visible WM alterations may contribute to the physiopathology of OSA-related cognitive impairment.

Genetic Architecture and Molecular, Imaging and Prodromic Markers in Dementia with Lewy Bodies: State of the Art, Opportunities and Challenges.

Dementia with Lewy bodies (DLB) is one of the most common causes of dementia and belongs to the group of α-synucleinopathies. Due to its clinical overlap with other neurodegenerative disorders and its high clinical heterogeneity, the clinical differential diagnosis of DLB from other similar disorders is often difficult and it is frequently underdiagnosed. Moreover, its genetic etiology has been studied only recently due to the unavailability of large cohorts with a certain diagnosis and shows genetic heterogeneity with a rare contribution of pathogenic mutations and relatively common risk factors. The rapid increase in the reported cases of DLB highlights the need for an easy, efficient and accurate diagnosis of the disease in its initial stages in order to halt or delay the progression. The currently used diagnostic methods proposed by the International DLB consortium rely on a list of criteria that comprises both clinical observations and the use of biomarkers. Herein, we summarize the up-to-now reported knowledge on the genetic architecture of DLB and discuss the use of prodromal biomarkers as well as recent promising candidates from alternative body fluids and new imaging techniques.

Impact of pre-sleep habits on adolescent sleep: an Italian population-based study.

OBJECTIVE: Several evidences demonstrate that pre-sleep habits may negatively impact adolescent sleep, yet few data exist on Italian population. This study aimed to investigate the relationship between pre-sleep habits, use of technology/activity and sleep in Italian adolescents. METHODS: Self-report questionnaires including Italian version of School Sleep Habits Survey and use of technology/activity (eg smartphone, PC) at bedtime were administered to 972 adolescents (13-19 years) from Lombardia. We stratified the sample in five groups according to the age: Group I (13-14 years), Group II (15 years), Group III (16 years), Group IV (17 years), Group V (18-19 years). RESULTS: Our descriptive analysis reveals a different sleep profile across age-groups: Group III showed highest percentage of bad sleep (26.7%) and frequent nocturnal awakenings (24.1%), Group V had the highest percentage of insufficient sleep (40,4%) and difficulty falling asleep (42.7%) and Group IV presented an elevated difficulty in waking up in the morning (70.1%). A significant negative correlation was found in total group between use of smartphone, internet and studying/doing homework and total sleep time. The use of smartphone, internet videogames, listening to music and studying/doing homework was positively associated with delayed bedtime. CONCLUSIONS: This study confirms the great impact of pre-sleep habits, and in particular the use of technology on adolescent sleep. Our results demonstrate that sleep is strongly altered among Italian adolescents using electronic devices in evening. The type of technology may be related to specific sleep profile, emphasizing the importance of stratification analyses to identify associated factors to sleep problems.

Decoding Causal Links Between Sleep Apnea and Alzheimer's Disease.

Obstructive sleep apnea (OSA) and Alzheimer's disease (AD) are two common chronic diseases with a well-documented association. Whether the association is causal has been highlighted by recent evidence reporting a neurobiological link between these disorders. This narrative review discusses the brain regions and networks involved in OSA as potential vulnerable areas for the development of AD neuropathology with a particular focus on gender-related implications. Using a neuroimaging perspective supported by neuropathological investigations, we provide a new model of neurodegeneration common to OSA and AD, that we have called OSA-AD neurodegeneration in order to decode the causal links between these two chronic conditions.

Neuropsychological assessment could distinguish among different clinical phenotypes of progressive supranuclear palsy: A Machine Learning approach.

Progressive supranuclear palsy (PSP) is a rare, rapidly progressive neurodegenerative disease. Richardson's syndrome (PSP-RS) and predominant parkinsonism (PSP-P) are characterized by wide range of cognitive and behavioural disturbances, but these variants show similar cognitive pattern of alterations, leading difficult differential diagnosis. For this reason, we explored with an Artificial Intelligence approach, whether cognitive impairment could differentiate the phenotypes. Forty Parkinson's disease (PD) patients, 25 PSP-P, 40 PSP-RS, and 34 controls were enrolled following the consensus criteria diagnosis. Participants were evaluated with neuropsychological battery for cognitive domains. Random Forest models were used for exploring the discriminant power of the cognitive tests in distinguishing among the four groups. The classifiers for distinguishing diseases from controls reached high accuracies (86% for PD, 95% for PSP-P, 99% for PSP-RS). Regarding the differential diagnosis, PD was discriminated from PSP-P with 91% (important variables: HAMA, MMSE, JLO, RAVLT_I, BDI-II) and from PSP-RS with 92% (important variables: COWAT, JLO, FAB). PSP-P was distinguished from PSP-RS with 84% (important variables: JLO, WCFST, RAVLT_I, Digit span_F). This study revealed that PSP-P, PSP-RS and PD had peculiar cognitive deficits compared with healthy subjects, from which they were discriminated with optimal accuracies. Moreover, high accuracies were reached also in differential diagnosis. Most importantly, Machine Learning resulted to be useful to the clinical neuropsychologist in choosing the most appropriate neuropsychological tests for the cognitive evaluation of PSP patients.

COVID-19 and neurological disorders: are neurodegenerative or neuroimmunological diseases more vulnerable?

Neurological disorders and coronavirus 2019 (COVID-19) pandemic are two conditions with a recent well-documented association. Intriguing evidences showed that COVID-19 infection can modify clinical spectrum of manifested neurological disorders but also it plays a crucial role in the development of future diseases as long-tem consequences. In this viewpoint review, we aimed to assess the vulnerability to SARS-CoV-2 infection and development of COVID-19 among neurological disorders. With this in mind, we tested the hypothesis that age rather than neuropathology itself could be decisive in neurodegenerative diseases such as Parkinson's disease, whereas neuropathology rather than age may be critical in neuroimmunological diseases such as Multiple Sclerosis. Highlighting the role of potential susceptibility or protection factors from this disastrous infection, we also stratify the risk for future neurodegeneration.

COVID-19 and Sleep in Medical Staff: Reflections, Clinical Evidences, and Perspectives.

PURPOSE OF THE REVIEW: There is evidence that, before the coronavirus pandemic 2019 (COVID-19), healthcare workers did not experience good sleep quality with relevant consequences on health. By contrast, little is known about the sleep quality of medical staff during the COVID-19 pandemic. In this review, we aimed to contribute with a review of the literature, sharing our clinical experience supported by actigraphic evaluation and by proposing future strategies. RECENT FINDINGS: Sleep disorders, in particular insomnia, have been commonly reported in frontline medical workers, in hospitals during the COVID-19 pandemic and are often accompanied by depressive and anxiety symptoms. Sleep quality, however, has been mainly assessed by the use of self-reported measures, thus limiting clinical usefulness. SUMMARY: Poor sleep quality among the medical staff is prevalent, and our experience supports that this has increased during the COVID-19 pandemic. A longitudinal investigation assessing whether and for how long sleep remains altered in medical staff could be of interest to evaluate the temporal effect of the pandemic on health.

Magnetic Resonance Imaging Biomarkers Distinguish Normal Pressure Hydrocephalus From Progressive Supranuclear Palsy.

BACKGROUND: Idiopathic normal pressure hydrocephalus and PSP share several clinical and radiological features, making differential diagnosis, at times, challenging. OBJECTIVES: To differentiate idiopathic normal pressure hydrocephalus from PSP using MR volumetric and linear measurements. METHODS: Twenty-seven idiopathic normal pressure hydrocephalus patients, 103 probable PSP patients, and 43 control subjects were consecutively enrolled. Automated ventricular volumetry was performed using Freesurfer 6 on MR T1 -weighted images. Linear measurements, such as callosal angle and a new measure, termed MR Hydrocephalic Index, were calculated on MR T1 -weighted images. Receiver operating characteristic analyses were used for differentiating between patient groups. Generalizability and reproducibility of the results were validated, dividing each participant group in two cohorts used as training and testing subsets. RESULTS: Ventricular volumes and linear measurements (callosal angle and Magnetic Resonance Hydrocephalic Index) revealed greater ventricular enlargement in patients with idiopathic normal pressure hydrocephalus than in PSP patients and controls. PSP patients had ventricular volume larger than controls. Automated ventricular volumetry and Magnetic Resonance Hydrocephalic Index were the most accurate measures (98.5%) in differentiating patients with idiopathic normal pressure hydrocephalus from PSP patients, whereas callosal angle misclassified several PSP patients and showed low positive predictive value (70.0%) in differentiating between these two diseases. All measurements accurately differentiated idiopathic normal pressure hydrocephalus patients from controls. Accuracy values obtained in the training set (automated ventricular volumetry, 98.4%; Magnetic Resonance Hydrocephalic Index, 98.4%; callosal angle, 87.5%) were confirmed in the testing set. CONCLUSIONS: Our study demonstrates that AVV and Magnetic Resonance Hydrocephalic Index were the most accurate measures for differentiation between idiopathic normal pressure hydrocephalus and PSP patients. Magnetic Resonance Hydrocephalic Index is easy to measure and can be used in clinical practice to prevent misdiagnosis and ineffective shunt procedures in idiopathic normal pressure hydrocephalus mimics. © 2020 International Parkinson and Movement Disorder Society.

Hippocampal impairment in patients with Essential Tremor.

INTRODUCTION: There is growing evidence that a proportion of patients with Essential Tremor (ET) may develop a memory impairment over time. However, no studies have evaluated whether hippocampal damage really occur in ET. This study investigated the macro and micro-structural integrity of the hippocampus in ET subjects using a multimodal MRI approach. METHODS: Neuropsychological and MRI data were acquired from 110 participants (60 patients with ET and 50 age-, sex-, and education-matched healthy controls [HC]). Whole-brain T1-weighted and Diffusion Tensor Imaging (DTI) were performed to assess macro-and microstructural alterations. MRI parameters (volume; mean diffusivity [MD]; fractional anisotropy [FA]) of bilateral hippocampi were obtained. In order to evaluate the relationship between MRI alterations and neurocognitive impairment, hippocampal parameters were also correlated with cognitive test scores. RESULTS: Compared to controls, ET patients showed a subclinical memory impairment with significantly lower memory scores, but within the normal ranges. Despite the subclinical damage, however, ET patients showed a significant increase in MD values in the bilateral hippocampi in comparison with HC. A significant correlation was also found between MD and memory scores in ET. CONCLUSION: This study improves the knowledge on memory impairment in ET, as our results demonstrate for the first time the hippocampal microstructural damage related to subclinical memory impairment in ET patients. Further studies are needed before these findings can be considered predictive of a distinct ET subtype or suggestive of a co-occurent dementia.

Therapy for Insomnia and Circadian Rhythm Disorder in Alzheimer Disease.

PURPOSE OF THE REVIEW: There is strong evidence for a bidirectional association between sleep disorders and Alzheimer's disease (AD). In particular, insomnia may be a potentially modifiable risk factor for AD. The present review summarizes recent advances in treatment of sleep disorders in AD. RECENT FINDINGS: Some studies investigated the efficacy and safety of hypnotic agents as ramelteon and mirtazapine to treat sleep disorders in AD but no significant therapeutic effects have been observed. Benzodiazepines are the most frequently used medication for treatment of insomnia but they may cause significant side effects in old subjects. Suvorexant, an orexin receptor antagonist, showed a positive effect on AD insomnia. Recent report suggests an association between trazodone use and delayed cognitive decline in AD. With respect to circadian rhythm disorders, non-pharmacological treatments, especially bright light therapy, could be useful and safe options for treatment in AD. Some pharmacological and non-pharmacological treatments might have benefits in AD patients with sleep disturbances, but further well-designed controlled trials are needed.