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Abstract Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson’s disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test–retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society–Unified Parkinson's Disease Rating Scale items (rho: 0.12–0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire health-related information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patient-centric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients’ functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson’s disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.

Currently, no treatments available for Parkinson’s disease (PD) can slow PD progression. At the early stage of the disease, only a subset of individuals with PD progress quickly, while the majority have a slowly progressive form of the disease. In developing treatments that aim to slow PD progression, clinical trials aim to include individuals who are likely to progress faster, such that a treatment effect, if one exists, can be identified easier and earlier. The aim of the present study was to identify baseline predictors of clinical progression in early PD. We analyzed 12-month data acquired from the PASADENA trial Part 1 (NCT03100149, n = 76 participants who were allocated to the placebo arm and did not start symptomatic therapy) and the Parkinson’s Progression Markers Initiative (PPMI) study (n = 139 demographically and clinically matched participants). By using ridge regression models including clinical characteristics, imaging, and non-imaging biomarkers, we found that Hoehn and Yahr stage and dopamine transporter single-photon emission computed tomography specific binding ratios (Dat-SPECT SBR) in putamen ipsilateral to the side of motor symptom onset predicted PD progression at the early stage of the disease. Further studies are needed to confirm the validity of these predictors to identify with high accuracy individuals with early PD with a faster progression phenotype.

Background: Currently available treatments for Parkinson's disease (PD) do not slow clinical progression nor target alpha-synuclein, a key protein associated with the disease.Objective: The study objective was to evaluate the efficacy and safety of prasinezumab, a humanized monoclonal antibody that binds aggregated alpha-synuclein, in individuals with early PD.Methods: The PASADENA study is a multicenter, randomized, double-blind, placebo-controlled treatment study. Individuals with early PD, recruited across the US and Europe, received monthly intravenous doses of prasinezumab (1,500 or 4,500 mg) or placebo for a 52-week period (Part 1), followed by a 52-week extension (Part 2) in which all participants received active treatment. Key inclusion criteria were: aged 40–80 years; Hoehn & Yahr (H&Y) Stage I or II; time from diagnosis ≤2 years; having bradykinesia plus one other cardinal sign of PD (e.g., resting tremor, rigidity); DAT-SPECT imaging consistent with PD; and either treatment naïve or on a stable monoamine oxidase B (MAO-B) inhibitor dose. Study design assumptions for sample size and study duration were built using a patient cohort from the Parkinson's Progression Marker Initiative (PPMI). In this report, baseline characteristics are compared between the treatment-naïve and MAO-B inhibitor-treated PASADENA cohorts and between the PASADENA and PPMI populations.Results: Of the 443 patients screened, 316 were enrolled into the PASADENA study between June 2017 and November 2018, with an average age of 59.9 years and 67.4% being male. Mean time from diagnosis at baseline was 10.11 months, with 75.3% in H&Y Stage II. Baseline motor and non-motor symptoms (assessed using Movement Disorder Society—Unified Parkinson's Disease Rating Scale [MDS-UPDRS]) were similar in severity between the MAO-B inhibitor-treated and treatment-naïve PASADENA cohorts (MDS-UPDRS sum of Parts I + II + III [standard deviation (SD)]; 30.21 [11.96], 32.10 [13.20], respectively). The overall PASADENA population (63.6% treatment naïve and 36.4% on MAO-B inhibitor) showed a similar severity in MDS-UPDRS scores (e.g., MDS-UPDRS sum of Parts I + II + III [SD]; 31.41 [12.78], 32.63 [13.04], respectively) to the PPMI cohort (all treatment naïve).Conclusions: The PASADENA study population is suitable to investigate the potential of prasinezumab to slow disease progression in individuals with early PD.Trial Registration: NCT03100149.

Background:. The aim of this study was to examine for the presence of implicit bias within the field of plastic surgery using a gender-specific Implicit Association Test (IAT), specifically looking at gender and career stereotypes. Methods:. A Gender-Career/Family Implicit Association Test was developed and distributed to the international plastic surgery community. Mean scores were calculated. Respondents were provided with an automated summary interpretation of their results, categorizing association for a particular grouping of gender and career/family as a little or no, slight, moderate, or strong. Respondents were also asked a series of demographic and post-IAT questions. Results:. Ninety-five responses were available for analysis. Overall, respondents showed a moderate-to-strong association of male + career / female + family compared with the reverse, which was statistically significant. Nearly half of the respondents thought they might have an implicit gender-related bias; however, 50% post-test would not change their behavior based on results, while 9.5% would. Conclusions:. Plastics surgeons may have an unconscious tendency to associate men with a career and women with a family. Further steps must be taken to increase awareness and mitigate the impact of implicit gender bias.

Background: Progressive neuronal loss in neurodegenerative diseases such as Parkinson’s disease (PD) is associated with progressive degeneration of associated white matter tracts as measured by diffusion tensor imaging (DTI). These findings may have diagnostic and functional implications but their value in de novo PD remains unknown. Here we analyzed longitudinal DTI data from Parkinson’s Progression Markers Initiative de novo PD patients for changes over time relative to healthy control (HC) participants.Methods: Baseline and 1-year follow-up DTI MRI data from 71 PD patients and 45 HC PPMI participants were included in the analyses. Whole-brain fractional anisotropy (FA) and mean diffusivity (MD) images were compared for baseline group differences and group–by–time interactions. Baseline and 1-year changes in DTI values were correlated with changes in DTI measures and symptom severity, respectively.Results: At baseline, PD patients showed significantly increased FA in brainstem, cerebellar, anterior corpus callosal, inferior frontal and inferior fronto-occipital white matter and increased MD in primary sensorimotor and supplementary motor regions. Over 1 year PD patients showed a significantly stronger decline in FA compared to HC in the optic radiation and corpus callosum and parietal, occipital, posterior temporal, posterior thalamic, and vermis gray matter. Significant increases in MD were observed in white matter of the midbrain, optic radiation and corpus callosum, while gray matter of prefrontal, insular and posterior thalamic regions. Baseline brainstem FA white matter (WM) values predicted 1-year changes in FA white matter and MD gray matter values. White but not gray matter changes in both FA and MD were significantly associated with changes in symptom severity.Conclusion: Significant gray and white matter DTI alterations are observable at the time of PD diagnosis and expand in the first year of de novo PD to other cortical and white matter regions. This pattern of DTI changes is in line with preclinical and neuroanatomical studies suggesting that the increased spatial spread of alpha-synuclein neuropathology is the key mechanism of PD progression. Taken together, these findings suggest that DTI may serve as a sensitive biomarker of disease progression in early-stage PD.

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.

The National Strategy for Biosurveillance defines biosurveillance as "the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels." However, the strategy does not specify how "essential information" is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being "essential". The question of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of "essential information" for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.

In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

Emotional information is typically better remembered than neutral content, and previous studies suggest that this effect is subserved particularly by the amygdala together with its interactions with the hippocampus. However, it is not known whether amygdala damage affects emotional memory performance at immediate and delayed recall, and whether its involvement is modulated by stimulus valence. Moreover, it is unclear to what extent more distributed neocortical regions involved in e.g. autobiographical memory, also contribute to emotional processing. We investigated these questions in a group of patients with Alzheimer’s disease (AD), which affects the amygdala, hippocampus and neocortical regions. Healthy controls (n = 14), patients with AD (n = 15) and its putative prodrome amnestic mild cognitive impairment (n = 11) completed a memory task consisting of immediate and delayed free recall of a list of positive, negative and neutral words. Memory performance was related to brain integrity in region of interest and whole-brain voxel-based morphometry analyses. In the brain-behavioral analyses, the left amygdala volume predicted the immediate recall of both positive and negative material, whereas at delay, left and right amygdala volumes were associated with performance with positive and negative words, respectively. Whole-brain analyses revealed additional associations between left angular gyrus integrity and the immediate recall of positive words as well as between the orbitofrontal cortex and the delayed recall of negative words. These results indicate that emotional memory impairments in AD may be underpinned by damage to regions implicated in emotional processing as well as frontoparietal regions, which may exert their influence via autobiographical memories and organizational strategies.

A key question in cognitive neuroscience is how the brain combines low-level features processed in remote sensory cortices to represent meaningful multisensory objects in our everyday environment. Models of visual object processing typically assume a feedforward cascade through the hierarchically organized ventral stream. We contrasted this feedforward view with an alternate hypothesis in which object processing is viewed as an interactive, feedforward and feedback process. We found that higher-order regions in anterior temporal and inferior prefrontal cortex performed audio-visual integration 100ms earlier than a sensory-driven region in the posterior occipital cortex, and were modulated by semantic variables (congruency), from as early as 50-100ms. We propose that the brain represents familiar and complex multisensory objects through early interactivity between higher-order and sensory-driven regions. This interactivity may underpin the enhanced behavioural performance reported for semantically congruent audio-visual objects.