Search Results (91)

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Background: Parkinson’s disease (PD) is clinically heterogeneous, yet the genetic architecture underlying this heterogeneity remains incompletely understood. We examined the genetic correlates of four complementary PD subtyping frameworks: the clinical motor subtype (tremor-dominant [TD] vs. postural instability/gait difficulty [PIGD]), alpha-synuclein seed amplification assay status (SAA+ vs. SAA−), the pathological subtype (brain-first vs. body-first, based on the presence of REM sleep behavior disorder), and the data-driven subtype (diffuse malignant [DM] vs. mild-motor predominant [MMP] vs. intermediate [IM]). Methods: We analyzed 1390 PD patients from the Parkinson’s Progression Markers Initiative (PPMI) with genotypes available for seven PD-associated genes (LRRK2, GBA1, SNCA, PRKN, PINK1, PARK7, VPS35), including specific variant resolutions (LRRK2 G2019S, R1441G/C/H; GBA1 N409S, severe variants; SNCAA53T), and APOE (ε2/ε3/ε4 alleles). Genetic variant frequencies were compared across subtypes using chi-square or Fisher’s exact tests with the Benjamini–Hochberg false discovery rate (FDR) correction. Effect sizes were quantified using Cramér’s V. multivariable logistic regression estimated adjusted odds ratios with Wald-based 95% confidence intervals. Results: Among genotyped PD patients, LRRK2 carriers constituted 13.7% (190/1390; 170 G2019S, 18 R1441G/C/H), GBA1 8.6% (119/1390; 96 N409S, 23 severe), and SNCA 2.0% (28/1390; all A53T). APOE ε4 carriers comprised 23.4% (323/1380). SAA-negative patients were markedly enriched for LRRK2 variants (37.1% vs. 10.2%, p = 3.7 × 10⁻¹⁹, q < 0.001, V = 0.25), specifically G2019S (28.5% vs. 9.6%, p = 4.9 × 10⁻¹¹, q < 0.001) and R1441G/C/H (7.9% vs. 0.5%, p = 2.7 × 10⁻¹², q < 0.001). Body-first PD was enriched for GBA1 carriers (12.3% vs. 6.7%, p = 0.004, q = 0.021) and had less LRRK2 carriers (7.9% vs. 15.0%, p = 0.002, q = 0.013). The DM subtype had the highest GBA1 frequency (14.0% vs. MMP 5.9%, p < 0.001, q = 0.003). After FDR correction, 10 out of 48 univariate tests remained significant. Clinical subtypes (TD vs. PIGD) showed only nominal LRRK2 differences that did not survive FDR correction. The APOE genotype did not differ across any framework. Conclusions: PD subtypes defined by alpha-synuclein pathology (SAA), pathological onset pattern (brain-first/body-first), and data-driven classification (DM/MMP/IM) show distinct genetic profiles that survive multiple comparison correction. LRRK2 variants strongly associate with SAA negativity (V = 0.25); GBA1 variants associate with the severe body-first onset and the diffuse malignant subtype. Full article

Sleep disturbances are early hallmarks of Alzheimer’s disease (AD) and other dementias, yet the molecular mechanisms remain poorly understood. We previously showed that dipeptidyl aminopeptidase-like protein 6-knockout (DPP6-KO) mice exhibit accelerated neurodegeneration with synaptic loss, neuronal death, and circadian dysfunction resembling AD pathology. Here, we investigate whether DPP6 deficiency directly causes sleep dysregulation and assess age-dependent effects using wireless EEG/EMG telemetry, behavioral monitoring, and body temperature recordings. We found striking age-dependent sleep phenotypes in DPP6-KO mice. Adult (3-month) DPP6-KO mice showed hyperactivity-driven REM sleep increases, while aged (12-month) DPP6-KO mice developed insomnia with fragmented sleep architecture. Critically, aged DPP6-KO mice exhibited decreased REM latency, a biomarker of depression, which we confirmed by behavioral assays. Conversely, DPP6 overexpression in aged wild-type mice increased NREM duration and reduced sleep fragmentation, demonstrating a protective effect. Throughout aging, DPP6-KO mice showed dysregulated locomotor activity and body temperature rhythms, suggesting broader disruption of circadian and metabolic homeostasis. These findings establish DPP6 as a critical regulator of sleep architecture whose loss recapitulates key sleep disturbances observed in AD/dementia. The progressive nature of sleep dysfunction in DPP6-KO mice, from REM abnormalities to insomnia, parallels human disease progression and positions DPP6 as a potential therapeutic target for sleep-related symptoms in neurodegenerative disorders. Full article

Background: Electromyography (EMG), video-polysomnography (vPSG), and wrist actigraphy are each used to develop diagnostic algorithms for rapid-eye-movement sleep behavior disorder (RBD). However, the extent to which they capture overlapping versus distinct motor phenomena remains unknown. We evaluated the respective contributions of actigraphy, EMG and vPSG to the measurement of REM sleep motor activity. Methods: Seventeen adults with RBD (Mount Sinai n = 9; Stanford n = 8) and eight control participants from an open Newcastle dataset underwent vPSG and concomitant wrist actigraphy. Flexor digitorum superficialis EMG activity and video-detected movements were manually scored in 3 s mini epochs. Actigraphy was quantified using an acceleration-magnitude-based activity count model. Statistical and agreement analyses were performed to assess the motor events captured by all three, any two, or by each modality independently during REM sleep. Results: In participants with RBD, actigraphy-derived movement load was significantly higher during REM sleep than during non-REM stages, a pattern not observed in control participants. REM movement load was also higher in RBD participants compared to controls, although this difference did not remain significant after correction for multiple comparisons. Across 12,941 3 s mini epochs, EMG, actigraphy, and video detected 1703, 1613, and 811 motor events, of which 413 were detected concurrently by all three modalities. Pairwise agreement was moderate and increased from EMG–actigraphy (κ = 0.27 ± 0.10) to actigraphy–video (κ = 0.41 ± 0.12) and EMG–video (κ = 0.45 ± 0.15). Of EMG-detected events, 49.0% were also detected by actigraphy; of actigraphy-detected events, 37.2% were detected by EMG and 34.9% by video. Actigraphy activity counts were highest for events detected by all three modalities and lowest for actigraphy-only events. Conclusions: Actigraphy-measured REM-related motor activity was elevated in RBD but not in controls. EMG, actigraphy, and video captured partially overlapping motor events in RBD patients, with actigraphy showing the highest sensitivity and manually scored video the lowest. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder in which early detection remains a major clinical challenge due to heterogeneous motor and non-motor manifestations and the lack of reliable biomarkers. In recent years, machine learning (ML) and deep learning (DL) methods have been increasingly investigated as decision-support tools for PD screening using diverse clinical and behavioral data. This review synthesizes PD detection studies published between 2017 and 2025, systematically analyzing 32 representative works across multiple modalities, including MRI, PET, EEG, REM sleep biomarkers, voice recordings, gait signals, handwriting/drawing tasks, and finger-tapping measurements. Across the reviewed literature, high classification performance is frequently reported, with CNN-based and hybrid DL architectures achieving particularly strong results in imaging and time-series settings, while classical ML approaches such as SVM and ensemble models remain competitive for engineered feature-based datasets. However, the review also reveals major barriers to reliable translation, including small datasets, inconsistent evaluation protocols, limited external validation, and the risk of performance inflation caused by non-subject-independent data splitting. Overall, this review provides a structured and modality-oriented reference of algorithms, datasets, and performance trends, while highlighting key methodological gaps and practical priorities for developing robust and clinically deployable PD detection systems. Full article

Parkinson’s disease (PD) and associated synucleinopathies are preceded by a prolonged prodromal phase during which neurodegenerative processes evolve years before the onset of motor or cognitive symptoms. Identifying biologically specific and accessible biomarkers during this window is critical for early diagnosis, risk stratification, and the development of disease-modifying therapies. Increasing evidence supports the skin as a key peripheral tissue involved in synucleinopathy, offering a minimally invasive source for in vivo detection of pathological α-synuclein. This review summarizes current evidence on skin-derived biomarkers across the prodromal continuum of PD, with particular emphasis on skin biopsy-based detection of phosphorylated α-synuclein and α-synuclein seed amplification assays (SAAs). Findings in high-risk prodromal phenotypes, including idiopathic REM sleep behavior disorder (iRBD) and pure autonomic failure (PAF), are critically reviewed. Emerging data suggest that cutaneous α-synuclein pathology may precede nigrostriatal dopaminergic degeneration and may predict phenoconversion to overt synucleinopathies. Important knowledge gaps are highlighted, including the lack of data in other prodromal phenotypes such as hyposmia. Overall, skin-based biomarkers appear to represent promising, scalable tools for biological diagnosis, prognostication, and enrichment of prodromal PD cohorts in clinical trials. Full article

Rapid eye movement sleep (REM) is a critical sleep stage associated with several sleep disorders, including sleep apnea and rapid eye movement sleep behavior disorder. Polysomnography (PSG) is the gold standard for identifying REM periods and diagnosing sleep disorders. However, PSG is typically conducted in sleep medicine centers using specialized equipment, where sleep experts assess sleep conditions through measurements such as brain activity, respiration, heart activity, and eye movements. An overnight stay in a sleep laboratory can adversely affect a patient’s natural sleep quality, introducing the risk of iatrogenic sleep disturbances. Recent studies have explored sleep stage detection using lightweight wearable devices, such as smartwatches, which offer lower cost but rely on a limited set of psychological signals. In this study, we propose a machine learning approach for REM sleep staging based solely on breathing rate (BR) and heart rate (HR), without relying on PSG recordings. Experimental evaluations conducted on the Dreamt dataset demonstrate the feasibility of the proposed approach and its potential to provide meaningful information for sleep staging. Future work will focus on developing a fully non-contact REM detection framework by integrating video-based estimation of HR and BR. Full article

Background: Rapid Eye Movement (REM) sleep behavior disorder (RBD) is a parasomnia resulting from degeneration of pontine and medullary circuits responsible for muscle atonia during REM sleep, leading to dream-enactment behaviors and vocalizations. It is strongly linked to α-synucleinopathies, particularly Parkinson’s disease. Current biomarkers such as neurophysiological measures and imaging support diagnosis and monitoring, but remain invasive or costly. Aim: This study aims to evaluate vocal and speech alterations as exploratory, non-validated candidate biomarkers of REM sleep behavior disorder. Methods: A systematic review was conducted according to PRISMA 2020 guidelines. PubMed, IEEE Digital Library Web of Science, Embase and the Cochrane Library were systematically searched for studies published from database inception to November 2025, as preregistered on the Open Science Framework. Studies were selected through a multi-step screening process and underwent qualitative quality assessment. Results: Twelve studies met inclusion criteria. Individuals with RBD exhibited abnormal nocturnal vocalizations and early lexical, syntactic, and narrative disruptions despite preserved perceptual speech. Quantitative analyses identified consistent deficits in prosody, phonation stability, timing, and articulation, with significant group differences and diagnostic accuracy up to 96% sensitivity. Multilingual cohorts demonstrated progression over time, while digital phenotyping detected emerging Parkinsonian signs with AUC > 0.70. Conclusions: Speech and vocal abnormalities in iRBD reflect early neurodegenerative changes and show promising but still exploratory diagnostic and prognostic potential. Integrating vocal markers with established biomarkers may enhance early detection; however, further research is required to validate a reliable and reproducible vocal signature of prodromal synucleinopathies. Full article

Sleep plays a central role in shaping emotional development during childhood and adolescence, yet increasing evidence indicates that these processes unfold differently in boys and girls. This narrative review synthesizes current findings on sex-specific associations between sleep patterns, neurodevelopmental trajectories, and emotional regulation across pediatric populations. It examines how biological factors, including pubertal timing, sex hormones, circadian physiology, and maturation of fronto-limbic circuits, interact with environmental influences to generate distinct vulnerabilities to anxiety, depression, and behavioral dysregulation. Growing data suggest that girls exhibit greater sensitivity to sleep disturbances, particularly during the pubertal transition, with stronger links to internalizing symptoms such as anxiety and mood disorders. In contrast, boys appear more prone to externalizing behaviors and show differential responses to circadian misalignment and short sleep duration. Emerging evidence on sex-specific sleep architecture, REM-related emotional processing, and the bidirectional pathways through which sleep quality affects affective functioning are explored. Finally, clinical implications for early detection, personalized prevention, and targeted interventions tailored by sex and developmental stage are discussed. Understanding sex-based differences in sleep–emotion interactions offers a critical opportunity to refine pediatric mental health strategies and improve outcomes across developmental trajectories. Full article

REM sleep behavioral disorder (RBD) is of increasing interest in Parkinson’s disease (PD). Previous studies exploring the association between REM sleep without atonia (RWA) and clinical PD features or other objective sleep metrics are scarce and have used PSG findings. A mobile electroencephalography (EEG)/electrooculography (EOG) recording system with two channels can objectively measure sleep parameters, including RWA, during natural sleep at home. We investigated whether RWA measured on a portable recording device at home could be associated with clinical PD features or other sleep metrics using baseline data from the ZEAL study. Differences between patients with and without RWA was analyzed using ANCOVA test. REM sleep length was significantly longer in patients with RWA than in those without RWA. A multivariate comparison using ANCOVA showed a significant difference in log-transformed REM sleep duration of patients with RWA after adjustment for potential confounders (adjusted mean difference of 1.203; 95% confidence interval 0.468 to 1.937; p = 0.003). The strength of this study was that it evaluated the association between RWA during natural sleep at home and clinical variables as well as other sleep metrics. The major result was that patients with and without RWA did not differ in their clinical variables, and there was no relation between RWA and objective sleep metrics other than REM sleep. The duration of REM sleep may be associated with RWA during natural sleep at home. Full article

REM sleep behavior disorder (RBD) represents a prodromal manifestation of Parkinson’s disease (PD), reflecting the breakdown of inhibitory networks extending from the brainstem to the cortex. This review synthesizes pathological, physiological, and behavioral evidence to illustrate how early α-synuclein pathology disrupts REM-sleep atonia and motor automaticity through degeneration of pontomedullary and cholinergic–inhibitory circuits. The resulting failure of inhibitory precision links nocturnal REM sleep without atonia to daytime gait and postural abnormalities, framing RBD as a dynamic systems disorder rather than a purely sleep-related phenomenon. By examining this continuum across neurophysiological, behavioral, and clinical domains, the review highlights current knowledge gaps, particularly regarding the temporal dynamics of degeneration and compensation. It further integrates multimodal biomarkers that capture these transitions in vivo and discusses therapeutic strategies aimed at preserving inhibitory network integrity and delaying phenoconversion to overt Parkinsonian syndromes. Full article

L1 cell adhesion molecule (L1CAM)-positive extracellular vesicles (EVs) are being explored as a potential source of biomarkers for Parkinson’s disease (PD) in peripheral blood. However, their utility remains controversial. In this study, we sought to investigate the proteome composition of L1CAM⁺-EVs isolated from human blood plasma and evaluate their potential as biomarkers for PD. L1CAM⁺-EVs were extracted from blood plasma using direct immunoprecipitation by employing magnetic beads coupled to an anti-L1CAM antibody. The Proximity Extension Assay platform, Olink Explore 3072, was used to analyze samples from 60 individuals: 20 healthy controls (HC), 20 patients with isolated REM sleep behavior disorder (iRBD), and 20 PD patients. Targeted proteomic analysis identified 2841 proteins in L1CAM⁺-EVs, of which 203 exhibited differential expression across groups. Although these changes were not statistically significant, after correction for multiple testing, a combination of 12 proteins could discriminate between PD and HC. Moreover, several proteins displayed trends toward upregulation or downregulation in PD and iRBD when compared with HC. These preliminary findings suggest that L1CAM⁺-EVs proteins show some potential as biomarkers for PD; however, further investigation and validation studies are required. Full article

Objective: To explore the value of multimodal molecular imaging in diagnosing and differentiating dementia with Lewy bodies (DLB). Methods: A retrospective analysis was conducted on clinical data and multimodal molecular imaging of 40 probable DLB patients treated at Peking Union Medical College Hospital (August 2017–December 2024). All 40 had ¹⁸F-FDG PET/CT; 15 had ¹³¹I-MIBG imaging; 11 had ¹⁸F-FP-CIT PET/CT. A total of 12 patients with poor cognition or atypical ¹⁸F-FDG PET/CT underwent ¹⁸F-AV45 PET/CT (2 also had ¹⁸F-PM-PBB3 imaging). A sex- and age-matched control group (cognitively normal, same-period health checkup ¹⁸F-FDG PET/CT) was included. ¹⁸F-FDG PET/CT images were visually and semi-quantitatively analyzed (ROI, SPM). ¹⁸F-AV45 PET/CT was assessed both visually and semi-quantitatively; ¹³¹I-MIBG imaging and ¹⁸F-FP-CIT PET/CT were visually evaluated. Results: The 40 DLB patients (29 males, 11 females; mean age 72 years) had distinct initial symptoms: 8 (20%) presented with cognitive decline as the first symptom, 23 (57.5%) with parkinsonian symptoms as the first symptom, and 9 (22.5%) with both symptoms occurring simultaneously. Mean intervals: 16.25 months from initial cognitive decline to parkinsonian symptoms, and 24.43 months from initial parkinsonian symptoms to cognitive decline. All had parkinsonian symptoms and cognitive impairment; 38 (95%) had visual hallucinations; and 26 (65%) had REM sleep behavior disorder. ¹⁸F-FDG PET/CT: 30(75%) showed typical occipital hypometabolism and posterior cingulate island sign; 10 (25%) had atypical findings. ¹³¹I-MIBG (15/15, 100%): cardiac sympathetic denervation. ¹⁸F-FP-CIT (10/11, 90.9%): basal ganglia dopaminergic damage. ¹⁸F-AV45 (9/12, 81.8%): positive. Semi-quantitative ¹⁸F-FDG analysis revealed parietal, occipital, and lateral temporal hypometabolism in DLB (left more severe than right). Conclusions: Dementia with Lewy bodies (DLB) presents with pre-onset parkinsonism and cognitive impairment, plus high rates of visual hallucinations and sleep disorders. Key imaging features—occipital hypometabolism/island sign on ¹⁸F-FDG PET/CT, cardiac sympathetic denervation on ¹³¹I-MIBG, and basal ganglia dopaminergic damage on ¹⁸F-FP-CIT—aid DLB diagnosis. ¹⁸F-AV45 PET/CT detects Aβ pathology in severely cognitively impaired patients, suggesting these DLB patients may have underlying AD pathology beyond DLB. Full article

Platelets are increasingly recognized as multifunctional cells with roles extending beyond hemostasis to immune regulation, inflammation, and neurodegeneration. Here, we performed RNA-Seq profiling of platelets from patients with idiopathic REM sleep behavior disorder (IRBD), dementia with Lewy bodies (DLB), Parkinson disease (PD), Alzheimer disease (AD), and healthy controls (CTRLs) to explore disease-specific transcriptomic signatures. Across all groups, the RNA class distribution was similar, dominated by mRNAs (78–80%) and long non-coding RNAs (lncRNAs; 15–16%). DLB platelets displayed a reduced proportion of lncRNAs, suggesting an impaired RNA regulation, whereas IRBD concentrated the highest number of disease-specific lncRNAs, half of which were Y-linked, consistent with the male predominance observed in alpha-synucleinopathies. Differential expression analysis (DEA) revealed extensive transcriptomic remodeling in IRBD and DLB, particularly affecting RNA processing, cytoskeletal organization, and platelet activation pathways, while PD and AD showed minimal changes. These findings suggest a progressive impairment of platelet activation and signaling across the DLB continuum, potentially mirroring neuronal dysfunction. The limited transcriptional deregulation in PD may reflect its pronounced biological heterogeneity, consistent with recent multidimensional disease models. Overall, our study highlights platelets as accessible indicators of early and disease-stage-specific molecular alterations in α-synucleinopathies. Full article

Parkinson’s disease (PD) is a multisystem disorder, with early changes extending beyond basal ganglia circuitries and involving non-dopaminergic pathways, including cerebellar networks. Whether cerebellar dysfunction reflects a compensatory mechanism or an intrinsic hallmark of disease progression remains unresolved. In this cross-sectional study, we examined how cerebellar γ-aminobutyric acid (GABA) and glutamate/glutamine (Glx) systems, as well as their excitatory/inhibitory (E/I) balance, are modulated along the disease course. As to ascertain how these mechanisms contribute to motor and non-motor features in the premotor and early stages of PD, 18 individuals with isolated REM sleep behavior disorder (iRBD), 20 de novo, drug-naïve PD (dnPD), and 18 matched healthy controls underwent clinical, cognitive, and neuropsychiatric assessments alongside cerebellar magnetic resonance spectroscopy (MRS, MEGA-PRESS, 3T). While cerebellar neurotransmitter levels did not differ significantly across groups, dnPD patients exhibited a shift toward hyperexcitability in the E/I ratio, without correlation to clinical or cognitive measures. In contrast, in iRBD, an inverse relationship between heightened GABAergic activity and neuropsychiatric symptoms emerged. These findings suggest an early, dynamic cerebellar involvement, potentially reflecting compensatory modulation of altered basal ganglia output. Our results support cerebellar GABA MRS as a promising biomarker and open perspectives for targeting non-dopaminergic pathways in PD. Full article