Background Adaptive deep brain stimulation (aDBS) utilizing BrainSense™ technology by Medtronic Percept™ stimulator, is a recent advancement that dynamically adjusts stimulation in real-time based on the power of frequency-specific neural signals. Although clinical trials have shown the feasibility of aDBS, real-world implementation remains underreported. We describe the development and implementation of a structured workflow for aDBS activation and programming at a tertiary movement disorders center. Methods This quality improvement project included the first 50 patients with Parkinson’s disease who initiated aDBS between March and September 2025 at the Cleveland Clinic. Data were abstracted from clinical records and programming logs as part of routine process evaluation. Findings were discussed with programming clinicians to identify variability and troubleshooting steps, leading to iterative refinement and consensus on a standardized workflow for aDBS activation and programming. Results The primary reasons for converting to aDBS were dyskinesias, motor fluctuations, and limited therapeutic windows on continuous DBS. Programming emphasized individualized threshold strategies (single vs. dual), ensuring each was fully optimized before transition. Successful implementation depended on three key factors: clearly defined therapeutic goals, validation of the frequency of interest, and confirmation of appropriate system adaptation through Timeline plots review and real-time signal streaming. A structured flowchart summarizing the programming and troubleshooting sequence is provided. Conclusions Implementation of aDBS in routine clinical practice is feasible with a structured, team-based workflow. Standardizing programming steps and incorporating iterative feedback support safety, consistency, and broader adoption across centers.

Background/Objectives:Postural instability compromises balance, contributing to annual fall rates of 45%-68% in Parkinson's disease (PD). A fundamental gap in clinical evaluation and treatment of postural instability is the use of insufficiently challenging postural control tests and reliance on subjective scoring. Traditional 3D motion capture (Traditional-MC), while precise and objective, is not feasible for clinical utilization. Markerless motion capture (MMC) is a viable candidate for quantifying postural control, feasible with embedded cameras of augmented reality (AR) headsets. This project aimed to assess MMC accuracy in quantifying postural control and to compare PD patients and healthy controls (HCs) to develop a loss-of-balance (LOB) prediction model. Methods:Video data were acquired by a clinician wearing a Microsoft HoloLens 2 AR headset as participants completed three progressively challenging postural control stances. Depth and red-green-blue camera data were analyzed with our custom-built human pose estimation software (CART-MMC). Criterion validity between Traditional-MC and CART-MMC was completed on outcomes, 95% ellipsoid sway area (Sway area) and 95% mediolateral and anteroposterior (ML and AP) ranges in 54 (HC = 28, PD = 26) participants. Group differences were assessed, and survival analysis was conducted to quantify no LOB probabilities in 31 HCs and 68 PD patients. A relaxed LASSO model was used to predict LOB in the PD group from CART-MMC data. Results:Sway area and ML range from CART-MMC were equivalent to Traditional-MC. Furthermore, ML range from CART-MMC differentiated PD patients from HCs in the stance that had the highest completion rate. As postural task difficulty increased, fewer PD patients were able to complete compared to HCs, resulting in lower survival probabilities of the PD group in Tandem-Firm-EO and FT-Foam-EC stances. In the least challenging stance, ML range predicted the occurrence of LOB in the more challenging stance (LOO-CV AUC = 0.78). Conclusions:CART-MMC is a valid method of obtaining objective postural control outcomes, which can detect latent postural control deficits and demonstrates a scalable, objective clinical and remote monitoring approach for predicting falls.

Parkinson’s disease (PD) exhibits significant variability in disease progression, making individual trajectory prediction challenging. Previously, we defined motor progression phenotypes based on longitudinal OFF-medication changes in the Movement Disorder Society–Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS-III) and demonstrated their predictability at baseline using MRI-informed machine learning. OFF-medication assessments are rare in routine care. Therefore, this study evaluated whether these phenotypes correspond to standard clinical outcomes. Eighty-eight early PD patients from the Parkinson’s Progression Markers Initiative were classified as “faster” or “slower” progressors based on 48-month OFF-medication ΔMDS-UPDRS-III scores. A support vector machine incorporating baseline structural MRI and clinical features classified these phenotypes with 89% accuracy. Phenotypes were compared across independent 48-month outcomes, including MDS-UPDRS Part II, Schwab & England Activities of Daily Living (S&E ADL), levodopa equivalent daily dose (LEDD), and Hoehn & Yahr (HY) staging. Faster progressors exhibited significantly greater functional declines in MDS-UPDRS Part II and S&E ADL scores and higher LEDD requirements at 48 months. Clinically meaningful deterioration consistent with published thresholds occurred more frequently among faster progressors for MDS-UPDRS Part II and HY staging. These findings validate the clinical relevance of MRI-informed progression phenotyping for prognostic stratification in early PD, highlighting its potential to identify biologically distinct trajectories.

Subjective, imprecise evaluation of lower extremity function hinders the effective treatment of gait impairments in Parkinson’s disease (PD). Markerless motion capture (MMC) offers opportunities for integrating objective biomechanical outcomes into clinical practice. However, validation of MMC biomechanical outcomes is necessary for clinical adoption of MMC technologies. This project evaluated the criterion validity of a custom MMC algorithm (CART-MMC) against gold-standard 3D motion capture (Traditional-MC) and its known-groups validity in differentiating PD from healthy controls (HC). Sixty-two individuals with PD and 29 HCs completed a stepping in place paradigm. The trials were recorded by an augmented reality headset with embedded RGB and depth cameras. The CART-MMC algorithm was used to reconstruct a 3D pose model and compute biomechanical measures of lower extremity performance. CART-MMC outcomes were statistically equivalent, within 5% of Traditional-MC, for measures of step count, cadence, duration, height, height asymmetry, and normalized path length. CART-MMC captured significant between-group differences in step height, height variability, height asymmetry, duration variability, and normalized path length. In conclusion, CART-MMC provides valid biomechanical outcomes that characterize important domains of PD lower extremity function. Validated biomechanical evaluation tools present opportunities for tracking subtle changes in disease progression, informing targeted therapy, and monitoring treatment efficacy.

Parkinson’s disease (PD) exhibits marked clinical and biological heterogeneity. This study aimed to identify neurobiologically defined PD subtypes using isotropic diffusion (ISO), a diffusion MRI-derived metric sensitive to changes in isotropic water diffusion associated with microstructural alterations, and to determine whether these subtypes differ in baseline motor profiles and longitudinal change in imaging and motor scores. Baseline ISO values were extracted from 12 subcortical motor regions in 156 de novo PD patients from the Parkinson’s Progression Markers Initiative. Hierarchical clustering was applied to ISO values to derive data-driven subtypes. Baseline differences in motor severity were assessed using independent two-sample t-tests. Longitudinal change in ISO and motor outcomes over four years was evaluated using baseline-adjusted change-score regression models in patients with complete follow-up data (n = 78). Two subtypes emerged: subtype 1 (n = 62) with lower ISO values and subtype 2 (n = 94) with higher ISO across all regions. Subtype 2 showed greater baseline rigidity and bradykinesia. In longitudinal analyses (subtype 1, n = 34; subtype 2, n = 44), no significant differences were observed between subtypes in change in ISO across subcortical regions or in progression of motor scores over four years. Our findings indicate that ISO-derived subtypes are associated with distinct baseline neurobiological and motor profiles in early PD, but do not show evidence of differential motor or imaging progression over a four-year follow-up. This pattern highlights the complexity of PD heterogeneity and underscores the need for further investigation in larger, long-term cohorts.

Parkinson’s disease (PD) is a heterogeneous neurodegenerative disorder marked by diverse motor and non-motor symptom profiles. Traditional symptom-based subtyping shows limited stability and lacks clear biological grounding. Integrating magnetic resonance imaging (MRI) with machine learning (ML) offers a promising avenue for defining biologically informed PD subtypes. This narrative review synthesizes evidence from MRI-based subtyping studies that used structural (T1-weighted), diffusion, functional, or multimodal MRI features as primary inputs for unsupervised or hybrid ML approaches to derive PD subtypes and outlines key methodological challenges and future translational needs. T1-weighted MRI studies consistently identify two to three subtypes characterized by distinct patterns of cortical and subcortical atrophy associated with variation in motor and non-motor symptoms. Although fewer in number, diffusion MRI studies have identified microstructural heterogeneity in PD. However, the findings remain heterogeneous and preliminary, and a stable subtyping framework has yet to be established. Multimodal MRI approaches show that combining modalities provides complementary insights into the neurobiology underlying PD heterogeneity but require further validation. Collectively, MRI-based subtyping shows promise for mapping clinical variability onto neuroanatomical patterns. At present, these subtypes are best viewed as research constructs that illuminate disease variability rather than clinical diagnostic tools. Translation into clinical practice will require addressing critical methodological gaps to achieve the reproducibility and prognostic utility necessary for precision medicine.

Introduction: Identifying deep brain stimulation (DBS) candidates, particularly those without access to an advanced specialty center, presents ongoing challenges. This study evaluates the feasibility of a smartwatch system for identifying DBS candidates in Parkinson’s disease (PD). Methods: We recruited adults diagnosed with PD and motor complications. Participants wore a consumer smartwatch for at least 4 days per month for 8 months. The smartwatch continuously recorded motion data from its internal motion sensors. Previously validated algorithms used motion data to measure tremor, slowness, and dyskinesia. We compared various metrics in participants who were and were not recommended for DBS and developed an artificial intelligence (AI) model to predict DBS candidacy using features extracted from the motion data. Results: Twenty-three participants were included in the data analysis. Sixteen participants were considered DBS candidates; among them, ten initiated DBS during the study, and six did not due to age or personal preference. Seven participants were not DBS candidates. Bad time (presence of tremor, slowness, and/or dyskinesia as measured by the smartwatch) occurred more often in DBS candidates (3.46 ± 2.23 vs. 1.24 ± 1.23 h/day; p < 0.001) compared to those who were not. Additionally, the system captured a significant reduction in bad time (3.46 ± 2.23 vs. 2.25 ± 2.76 h/day; p < 0.001) after receiving DBS. The AI model achieved an area under the receiver operating characteristic curve of 0.96 for identifying DBS candidates. Conclusions: The results suggest that sensors in commercial smartwatches and AI can help identify DBS candidates and detect improvements resulting from the therapy. This type of remote patient monitoring could expand access to patients who might not otherwise have considered DBS.

Remote, internet-based deep brain stimulation programming for Parkinson’s disease accelerates clinical benefits postoperatively by improving access to therapy adjustments compared to in-clinic optimization. After completion of the initial digital programming phase, we show that clinical outcomes, quality of life, and safety remain sustained over at least twelve months under routine care conditions. Embedding a randomized trial within a larger cohort study enables long-term, real-world evaluation, offering a scalable and pragmatic model for assessing complex digital interventions in routine clinical care. (NCT05269862 registered on 2022-03-08 and NCT04071847 registered on 2019-08-28).

OBJECTIVE:Hospitalized people with Parkinson's disease (PwP) face increased risks of medication errors and discharge to non-home settings, both of which are associated with adverse outcomes. This study assessed differences in medication error rates and discharge outcomes before and after implementation of a dedicated inpatient program for hospitalized PwP. METHODS:During 2023, a Parkinson's disease (PD) inpatient program was implemented and refined combining: (1) an electronic health record (EHR) census for identification of hospitalized PwP; (2) inpatient monitoring and alignment of inpatient and outpatient regimens by movement-disorders-trained advanced practitioners; (3) customized EHR alerts and levodopa orders; (4) pharmacist support, and (5) staff education. Medication error rates and clinical outcomes were compared between January and June 2024 (post-implementation phase) and a pre-implementation retrospective cohort from 2018. RESULTS:From January to June 2024, 366 post-implementation admissions were monitored. Among those receiving contraindicated medications, the median number of doses decreased from 2 (interquartile range [IQR] = 1-6) during pre-implementation to 1 (IQR: 1-2) post-implementation (p = 0.015). Days with a levodopa dose deviation decreased from 43.1% to 38.3%, p < 0.0001, improper levodopa formulation substitutions from 18.6% to 5%, p < 0.0001, timing deviations from 72.2% to 51.8%, p < 0.00001, missed doses from 21.5% to 16.8%, p = 0.013, and discharged to non-home settings from 44.8% to 38.3%, p = 0.038. INTERPRETATION:Following implementation of a multidisciplinary and proactive inpatient program, reductions in medication errors and improved discharge outcomes were observed among PwP. ANN NEUROL 2026.

Cryospheric processes and interactions between the cryosphere and other Earth system components are complex, host important climate feedbacks and are often difficult to measure. Yet their understanding is crucial for predicting the evolution of the cryosphere in a changing climate. Stable water isotopes are natural tracers of phase change processes within the hydrological cycle. The variability of the individual and combined isotope species offer a way to constrain environmental climatic conditions during phase change processes. Thus, they are a prime tool to investigate air-snow interactions, which are at the core of one of the most uncertain but eminently important climate feedbacks. In polar settings these phase change processes are predominantly vapor deposition and snow or ice sublimation. However, the principle of isotopic fractionation during sublimation has been controversially discussed and the usefulness of tracing stable water isotopes in cryospheric processes is thus debated.Here we demonstrate through field observations and laboratory experiments that air-snow humidity exchange leaves an isotopic fingerprint in the snow isotopic composition. We present in-situ data from the Greenland Ice Sheet and new results from cold-laboratory wind tunnel experiments. The measurements comprise isotopic signatures of snow, vapor and of the humidity flux itself. We show that snow sublimation is a fractionating process and outline how this information can be used to improve cryospheric process understanding. Specifically, we investigate the process of drifting and blowing snow by observing the evolution of both vapor and snow isotopic composition during cold-laboratory wind tunnel experiments. We document the existence of hitherto unobserved airborne snow metamorphism; a process observable on the macro-scale only through the lens of stable water isotopes. Based on the combined observations of in-situ surface humidity fluxes and wind tunnel experiments we discuss a physical explanation for the observed isotopic fractionation during snow sublimation. These insights and the data set will be the basis for determining the fractionation factors associated with airborne snow metamorphism. Our results have important implications for the interpretation of stable water isotope signals from snow and ice cores and challenge the translation of the second-order parameter d-excess signal in polar regions as moisture source signal.

Background People with Parkinson's disease (PwPD) who have undergone deep brain stimulation (DBS) surgery have been historically excluded from rehabilitation clinical trials. Objective This project investigated the safety, feasibility, and preliminary efficacy of a dual-task training intervention aimed at improving postural instability and gait dysfunction (PIGD) in PwPD with DBS. Methods Symptoms of PIGD were measured with the 2-Minute Walk Test (2MWT) and Timed Up and Go (TUG) test under single- and dual-task conditions. Results Five participants completed 97.5 % of the 16 intervention sessions without serious adverse events. One participant was excluded from the motor analysis due to frequent changes in DBS settings. Of the four participants included in the analysis, all demonstrated improvement in the TUG under single- and dual-task conditions. During the 2MWT, two participants experienced an improvement in gait speed under single-task conditions, and three experienced an improvement under dual-task conditions. Conclusion Initial data suggest dual-task training is safe and feasible for PwPD with DBS. Having DBS presents unique challenges including advanced motor symptoms, autonomic dysfunction, and potential changes in DBS parameters, that impact intervention delivery, adherence, and outcomes. Despite unique challenges, appropriately selected PwPD with DBS have the capacity to improve PIGD symptoms with dual-task training. To facilitate generalizability across the PD continuum, future rehabilitation trials including PwPD with DBS are justified and recommended.

Background: Parkinson’s disease (PD) shows marked variability in disease progression, and predicting individual trajectories remains challenging. We previously developed a structural MRI–based machine learning classifier that distinguished faster from slower motor progressors using OFF-medication Movement Disorder Society–Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS-III) scores with 89% accuracy. As OFF assessments are rarely performed in clinical practice, we evaluated whether this classifier predicts outcomes routinely used in care. Methods: Eighty-eight early PD patients from the Parkinson’s Progression Markers Initiative were previously classified as faster (n=42) or slower (n=46) motor progressors using a support vector machine model incorporating patient-specific multivariate gray matter volumetric distance and baseline clinical features. Primary outcomes were 48-month changes (Δ) in MDS-UPDRS Part II (experiences of daily living), Schwab & England Activities of Daily Living (S&E ADL), and levodopa equivalent daily dose (LEDD). Secondary analyses examined clinically meaningful thresholds: MDS-UPDRS-II worsening (≥2.51 points), ≥10% S&E ADL decline, ≥100 mg/day/year LEDD slope, and ≥1-stage Hoehn & Yahr (HY) progression. Results: Faster progressors showed significantly greater functional decline (ΔMDS-UPDRS-II: 5.31±4.77 vs. 2.76±4.56, p=0.01; ΔS&E ADL: 9.29±8.12% vs. 4.89±6.18%, p=0.009) and higher medication requirements (LEDD: 423.45±274.16 vs. 278.37±203.84 mg/day, p=0.006). Clinically meaningful deterioration was more frequent among faster progressors for MDS-UPDRS-II (81% vs. 52%, OR=3.90, p=0.009) and HY staging (62% vs. 28%, OR=4.13, p=0.003). Conclusions: An MRI-based classifier trained on OFF-medication motor assessments successfully predicts clinically meaningful deterioration across multiple real-world outcomes, supporting its potential utility for prognostic stratification in early PD.

Introduction:Aerobic exercise mitigates symptoms of Parkinson's disease (PD) and may slow disease progression; however, the neural mechanisms underlying these improvements are not well understood. In this study, we discuss the methodology for simultaneously recording local field potentials (LFP) from the subthalamic nucleus (STN), cortical activity using scalp electroencephalography (EEG), and exercise performance metrics during a 40-min aerobic cycling session. Data from a single patient with PD are presented to illustrate the utility, feasibility, and data integrity of the experimental set up. Methods:The Medtronic Percept™ DBS system was used to record and stream bilateral STN-LFP in the OFF-therapy condition (OFF-DBS and OFF-antiparkinson medications) during a 40-min aerobic exercise session. A 64-channel mobile EEG system recorded cortical data. The neural data streams were synchronized using a TENS device that injected a specified electrical signal into the EEG and LFP recordings. Exercise performance metrics, heart rate, cadence, and power were synchronized with neural data and collected during the exercise session. The study is registered on ClinicalTrials.gov, trial identifying numbers NCT05905302 and NCT05972759. Results:STN-LFP, EEG, and exercise performance data can be synchronized, recorded for more than 40 min, and analyzed to evaluate how aerobic exercise impacts patterns of cortical and subcortical neural activity. Conclusion:While exercise positively affects symptoms of PD, the precise effects of exercise on network activity remain unclear. The methods utilized for collecting and analyzing neural (cortical and subcortical) and exercise-related data during a typical bout of aerobic exercise suggest that this approach can be adopted for larger, long-term exercise studies in patients with PD and deep brain stimulation (DBS). The described protocol provides a roadmap for future projects aiming to combine STN-LFP and cortical data to better understand how exercise may alter cortico-basal-ganglia-thalamic dynamics in PD.

Deep brain stimulation (DBS) has emerged as an important therapeutic intervention for neurological and neuropsychiatric disorders. After initial programming, clinicians are tasked with fine-tuning DBS parameters through repeated in-person clinic visits. We aimed to evaluate whether DBS patients achieve clinical benefit more rapidly by incorporating remote internet-based adjustment (RIBA) of stimulation parameters into the continuum of care. We conducted a randomized controlled multicenter study (ClinicalTrails.gov NCT05269862) involving patients scheduled for de novo implantation with a DBS System to treat Parkinson’s Disease. Eligibility criteria included the ability to incorporate RIBA as part of routine follow-up care. Ninety-six patients were randomly assigned in a 1:1 ratio using automated allocation, blocked into groups of 4, allocation concealed, and no stratification. After surgery and initial configuration of stimulation parameters, optimization of DBS settings occurred in the clinic alone (IC) or with additional access to RIBA. The primary outcome assessed differences in the average time to achieve a one-point improvement on the Patient Global Impression of Change score between groups. Patients, caregivers, and outcome assessors were not blinded to group assignment. Most of the data collection took place in the patient’s home environment. Access to RIBA reduces the time to symptom improvement, with patients reporting 15.1 days faster clinical benefit (after 39.1 (SD 3.3) days in the RIBA group (n = 48) and after 54.2 (SD 3.7) days in the IC group (n = 48)). None of the reported adverse events are related to RIBA. This study demonstrates safety and efficacy of internet-based adjustment of DBS therapy, while providing clinical benefit earlier than in-clinic optimization of stimulation parameters by increasing patient access to therapy adjustment. Deep brain stimulation (DBS) uses electrical impulses to treat disorders of the nervous system such as Parkinson’s Disease. Patients undergoing DBS need to travel to a clinic to have their treatment optimized. This study investigated whether optimizing DBS settings remotely via a mobile application leads to faster symptom improvements. The control group consisted of patients whose DBS settings were adjusted only in the clinic. Patients who had the option to adjust their therapy remotely report symptom and quality of life improvement earlier without additional side effects. These results suggest that remotely adjusting DBS settings could benefit patients and improve treatment outcomes. Gharabaghi et al assess the clinical benefit of incorporating remote internet-based adjustment (RIBA) of deep brain stimulation parameters into the continuum of care. Clinical benefits were reported more than two weeks earlier with access to RIBA, compared to in-clinic therapy optimization alone, with a similar safety profile.

Freezing of gait (FoG) is a disabling symptom of Parkinson's disease (PD) characterized by involuntary cessation/reduction. While deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) effectively treats common PD symptoms such as tremor, its impact on FoG is less clear. Rarely, STN-DBS itself can induce FoG. Reversible cases have been linked to hyperdopaminergic states, high-frequency stimulation, and suboptimal DBS lead placement. One irreversible case occurred immediately after DBS surgery and was attributed to lesioning of gait pathways during lead insertion. We report a case of seemingly irreversible FoG that began not after lead insertion or initial activation, but after initial follow-up adjustment in STN-DBS with otherwise proper placement and expected benefit. A 62-year-old female underwent STN-DBS for medication-resistant symptoms of idiopathic PD. The leads were activated at 125 Hz without adverse events. The patient returned two weeks later for stimulation adjustment. Two days after that visit, the patient developed severe FoG, resulting in falls. Despite many adjustments to both stimulation parameters and medications, the patient continued to experience FoG even with the device off. Lower frequency stimulation (55 Hz) provided partial, temporary improvement in FoG. Low-frequency parameters and off-stimulation trials were limited by the patient's severe dystonia. Spatial reconstruction confirmed the active contacts were within the STN. This timeline of events differs from previously reported cases attributed to anatomic/structural causes (lead mispositioning vs. gait pathway lesioning), hyperdopaminergic states, or high-frequency stimulation. Although the utilized contacts appear to be appropriately positioned, iatrogenic induction of FoG is too poorly understood to exclude their position as a factor. This case could be explained as a combination of positional- and frequency-induced FoG if not for the persistence of FoG when off-stimulation. It is possible that the "off trials" were not long enough for the adverse effects of stimulation to fully subside. Longer "off trials" are limited by the patient's severe dystonia. Post-STN-DBS FoG refractory to stimulus on/off state is a rare phenomenon reported only twice in the current literature (including this case). The current report describes the first patient for whom significantly refractory FoG specifically began after stimulus adjustment; this may represent lasting negative effects of stimulation in the DBS "off" state. Comprehensive reporting of anatomical lead locations and stimulation parameters in similar cases is essential to identify patterns that could inform future interventions.

Snow precipitation frequently occurs under moderate to strong wind conditions, resulting in drifting and blowing snow. Processes like particle fragmentation and airborne metamorphism during snow transport result in microstructural modifications of the ultimately deposited snow. Despite the relevance (optically and mechanically) of surface snow for alpine and polar environments, this effect of wind on the snow microstructure remains poorly understood and quantified. Available descriptions of snow densification due to wind are exclusively derived from field measurements where conditions are difficult to control. Information on the effect of wind on the specific surface area (SSA) is basically nonexistent. The goal of this experimental study was to systematically quantify the influence of wind on the surface snow density and SSA for various atmospheric conditions (temperature, wind speed, precipitation intensity), and to identify the relevant processes. We conducted experiments in a cold laboratory using a closed-circuit ring wind tunnel with an infinite fetch to investigate wind-induced microstructure modifications under controlled atmospheric, flow and snow conditions. Artificially produced dendritic nature-identical snow was manually poured into the ring wind tunnel for simulating precipitation during the experiments. Airborne snow particles are characterized by high-speed imaging, and deposited snow is characterized by density and SSA measurements resulting in a comprehensive dataset. The high-speed images support a snow particle transport scheme in the saltation layer similar to natural conditions. We measured an increase of the densification rate with increasing wind speed which differs from available model parameterizations. The SSA was found to decrease under the influence of wind, while increasing wind velocities intensified the SSA decrease. For higher air temperatures (Ta > -5°C), both the densification and SSA rates significantly differ from the rather constant rates at lower temperatures. We attribute this to the effects of enhanced cohesion or sintering (density) and intensified airborne snow metamorphism (SSA) at higher air temperatures. A sensitivity experiment revealed a strong influence of airborne snow metamorphism on the SSA decrease. Our results provide a first step towards an improved understanding and modeling of the effect of aeolian snow transport on optically and mechanically relevant microstructural properties of surface snow.

Aeolian transport of snow is a cryospheric process prevalent in all snow-covered areas. It influences the energy and mass balance of these cold regions. Apart from the direct effects during the process, aeolian transport alters the snow’s microstructure, leaving behind a wind-blown snow layer with different snowpack characteristics than before the wind event. For high-resolution climate modeling in snow-covered regions, it is thus important to incorporate the immediate and lasting effects of wind-induced aeolian snow transport for an accurate representation of the energy and mass balances of a snowpack. Apart from mechanical mechanisms such as fragmentation and aggregation of snow crystals, the metamorphic mechanism (sublimation and deposition of water molecules on the suspended snow particles) can alter the microstructure of snow during aeolian transport. It is difficult to predict the relative importance of the two mechanisms for the evolution of the microstructure of wind-blown snow, not least because the process is happening on the micro-scale but is unfolding on large spatial scales on the respective particle trajectories. Thus, it is difficult to observe.However, metamorphic processes leave a fingerprint on the snow’s composition of stable water isotopes whereas the mechanical mechanisms do not. Hence, monitoring the evolution of the stable water isotope signal of the snow can act as a macro-scale tracer for the metamorphic micro-scale processes. The stable water isotope signal can thus help to differentiate the processes at play during aeolian snow transport.Here we show through observations of cold laboratory ring-wind tunnel experiments that aeolian transport of snow involves airborne snow metamorphism. We monitored the evolution of the microstructure and the isotopic composition of airborne snow through repeated sampling of snow from the air stream. In a total of 19 experiments we varied the temperature in a range of -20°C to -3°C and the transport times varied between 50 - 180 minutes. We find a rapid exponential decay in specific surface area (SSA) with transport time which reduces the SSA value to 35-70% of its starting value by the end of the experiments. Further, we observe a shift in the particle size distribution towards larger snow particles, both for the most abundant and maximum particle sizes with aeolian transport time. Simultaneously, the water isotope signature shows mainly an enrichment in δ18O and a decrease in d-excess which is a strong indicator for isotopic fractionation and thus evidence for the presence of metamorphic processes. Combining the results, we attribute the change in snow microstructure to airborne snow metamorphism. The unique combination of information on the isotopic composition and microstructure of airborne snow under well-constrained laboratory conditions can be used to develop parameterizations for the incorporation of airborne snow metamorphism in snow-process models.

Background/Objectives: Technological approaches for the objective, quantitative assessment of motor functions have the potential to improve the medical management of people with Parkinson’s disease (PwPD), offering more precise, data-driven insights to enhance diagnosis, monitoring, and treatment. Markerless motion capture (MMC) is a promising approach for the integration of biomechanical analysis into clinical practice. The aims of this project were to evaluate a commercially available MMC system, develop and validate a custom MMC data processing algorithm, and evaluate the effectiveness of the algorithm in discriminating fine motor performance between PwPD and healthy controls (HCs). Methods: A total of 58 PwPD and 25 HCs completed finger-tapping assessments, administered and recorded by a self-worn augmented reality headset. Fine motor performance was evaluated using the headset’s built-in hand tracking software (Native-MMC) and a custom algorithm (CART-MMC). Outcomes from each were compared against a gold-standard motion capture system (Traditional-MC) to determine the equivalence. Known-group validity was evaluated using CART-MMC. Results: A total of 82 trials were analyzed for equivalence against the Traditional-MC, and 152 trials were analyzed for known-group validity. The CART-MMC outcomes were statistically equivalent to Traditional-MC (within 5%) for tap count, frequency, amplitude, and opening velocity metrics. The Native-MMC did not meet equivalence with the Traditional-MC, deviating by an average of 24% across all outcomes. The CART-MMC captured significant differences between PwPD and HCs for tapping amplitude, amplitude variability, frequency variability, finger opening and closing velocities, and their respective variabilities, and normalized path length. Conclusions: The biomechanical data gathered using a commercially available augmented reality device and analyzed via a custom algorithm accurately characterize fine motor performance in PwPD.