Sleep disturbances are highly prevalent and clinically significant non-motor features of Parkinson's disease (PD). Although in-laboratory polysomnography remains the gold standard, its limited scalability and ecological validity constrain longitudinal and real-world assessment. Recent advances in digital health technologies have introduced a broad spectrum of portable, wearable, and contactless tools for sleep monitoring. In this scoping review, we systematically map the landscape of digital sleep technologies in PD using a tiered framework based on technical maturity and clinical validation (Tier 1-4), and further classify them by signal modality and sleep symptom domain. Through a systematic review of the literature, we identified 19 studies (Tier 2-4) applying digital biomarkers to assess sleep disturbances in PD, including REM sleep behavior disorder, nocturnal immobility, insomnia, circadian rhythm disturbances, excessive daytime sleepiness, and sleep-related respiratory and movement disorders. We additionally contextualize these findings against the rapid expansion of multimodal and AI-driven Tier 3-4 platforms in the general population. Despite this technological progress, a major translational gap persists in PD, characterized by limited disease-specific validation, small cohort sizes, and insufficient multimodal benchmarking. Multimodal systems leveraging machine learning offer a promising direction by enabling more precise characterization of complex and overlapping sleep phenotypes. Emerging contactless systems further expand the potential for continuous, low-burden monitoring, although their clinical validity remains to be established. Future development of digital sleep biomarkers in PD will require prospective validation against established standards and integration of multimodal data to enable scalable, longitudinal phenotyping and clinical trial applications.

Oculogyric crisis (OGC; also referred to as oculogyric spasm) is a rare movement disorder characterized by tonic conjugate eye deviation, typically upward, with preserved consciousness.Associated features include blepharospasm, cervical dystonia, autonomic manifestations (pupillary dilation, lacrimation), and psychiatric symptoms (anxiety, agitation) 1 .Levodopainduced OGC (LI-OGC) represents sustained tonic deviation, typically appearing 10-30 minutes post-dose, associated with patient awareness and distress 2,3 .On the other hand, levodopa-induced ocular dyskinesia (LIOD) differs fundamentally: brief phasic eye deviations lasting seconds, with patients usually unaware and untroubled by the movements 3 .

Parkinson's disease (PD) is characterized by marked clinical and biological heterogeneity, and despite decades of intensive research, no single biomarker has yet been established that can reliably predict disease progression or long-term clinical outcomes. 1Hallmarks of PD pathophysiology-including -synuclein aggregation patterns, nigrostriatal dopaminergic degeneration, motor manifestations, and a wide spectrum of non-motor symptoms-represent essential components of the disease process; however, each captures only a fragment of the

Obstacle crossing during walking poses a major fall risk in individuals with Parkinson's disease (PD), particularly those with cognitive impairment. Although cognitive decline worsens gait, its specific effects on gait performance and cortical activation during obstacle walking remain unclear. The dynamic interaction between brain activity and gait across different walking phases is especially understudied in PD with mild cognitive impairment (PD-MCI) compared to those without cognitive impairment (PD-nonMCI). Nineteen PD-nonMCI and fifteen PD-MCI participants performed obstacle walking while functional near-infrared spectroscopy (fNIRS) measured activation in the prefrontal cortex (PFC), supplementary motor area (SMA), and premotor cortex (PMC). Gait parameters (speed, cadence, stride length, stride time) and obstacle crossing metrics (crossing speed, stride length, stride time, step width) were analyzed across early (5-20 s) and late (20-40 s) phases. Generalized estimating equations examined group, phase, and interaction effects. Brain-gait associations were assessed using Spearman's correlations. PD-MCI participants exhibited poorer obstacle walking performance but no significant phase-related behavioral change. Both groups showed higher PFC, SMA, and PMC activation during the early phase, reflecting greater neural engagement at task onset. However, SMA and PMC activation declined more steeply across phases in the PD-MCI group. In PD-MCI, obstacle walking performance correlated negatively with early-phase PMC and late-phase PFC activation. PD-MCI participants showed poorer gait and higher cortical activation, indicating increased neural effort and reduced efficiency. These results highlight altered brain-gait coupling in PD-MCI and emphasize the need for interventions enhancing neural efficiency during complex walking.

This longitudinal study evaluated the long-term efficacy and safety of hematopoietic stem cell transplantation (HSCT) in slowing CSF1R-RD progression. Researchers compared six symptomatic patients (mean follow-up 6.59 years) to six matched untreated controls. Evaluation utilized the CSF1R-Clinical Severity Score (CCSS), Montreal Cognitive Assessment (MoCA), and Sundal radiological scores. Post-HSCT clinical progression slowed significantly from 14.1 to 3.7 CCSS/year, compared to 15.2 in the control group ($p<0.01$). Cognitive decline was substantially reduced (-1.5 vs. -7.6 points/year), and radiological deterioration slowed (0.37 vs. 3.9 per year). Remarkably, all HSCT patients survived, while 50% of the control group died during the observation period. No serious transplant-related complications were observed. HSCT is a potent disease-modifying therapy for CSF1R-RD, drastically improving survival and slowing deterioration. The findings underscore the necessity of early intervention during the symptomatic phase to maximize the preservation of quality of life for affected individuals.