An Open-Label Pilot Study to Examine the Safety, Tolerability and Efficacy of Deutetrabenazine in Isolated Dystonia.

Background: Dystonia may respond to VMAT2 inhibition.

Objectives: Providing pilot data on the safety, tolerability, and efficacy of deutetrabenazine in non dopa-responsive dystonia.

Methods: Deutetrabenazine was titrated by adults with isolated dystonia.

Staged Screening Identifies People with Biomarkers Related to Neuronal Alpha-Synuclein Disease.

Objective: Remote identification of individuals with severe hyposmia may enable scalable recruitment of participants with underlying alpha-synuclein aggregation. We evaluated the performance of a staged screening paradigm using remote smell testing to enrich for abnormal dopamine transporter single-photon emission computed tomography imaging (DAT-SPECT) and alpha-synuclein aggregation.

Methods: The Parkinson's Progression Markers Initiative (PPMI) recruited participants for the prodromal cohort who were 60-years and older without a Parkinson's disease diagnosis.

Impact of dopamine deficiency and REM sleep behavior disorder on cognition in early neuronal synuclein disease with hyposmia.

Objectives: To determine the impact of dopamine deficiency and isolated REM sleep behavior disorder (iRBD) on cognitive performance in early neuronal alpha-synuclein disease (NSD) with hyposmia.

Methods: Using Parkinson's Progression Markers Initiative baseline data, cognitive performance was assessed with a cognitive summary score (CSS) developed by applying regression-based internal norms derived from a robust healthy control (HC) group. Performance was examined for participants with hyposmia classified as NSD-Integrated Staging System (NSD-ISS) Stage 2, either Stage 2A (CSF alpha-synuclein seed amplification assay [SAA]+, SPECT dopamine transporter scan [DaTscan]-) or 2B (SAA+, DaTscan+).

Accelerating Parkinson's Disease drug development with federated learning approaches.

Parkinson's Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.