Progressive Supranuclear Palsy(PSP)

The Progressive Supranuclear Palsy Clinical Trial Platform (PTP) is a multi-center, multi-regimen clinical trial evaluating the safety and efficacy of investigational products for the treatment of PSP.

Progressive supranuclear palsy (PSP), mild cognitive impairment with Lewy bodies (MCI-LB), and Dementia with Lewy Bodies (DLB) are severe neurodegenerative diseases that cause significant motor impairment impacting daily function. Researchers at BioSensics, Johns Hopkins School of Medicine, Massachusetts General Hospital and their collaborators aim to conduct an analytical and clinical validation of wearable-based digital health technologies for monitoring upper and lower limb function in PSP, MCI-LB and DLB that could enable frequent, at-home monitoring and be incorporated into future clinical trials.

This research employs AI to analyze facial expressions and speech patterns, aiming to develop new digital tools for diagnosing and differentiating Parkinson's disease and similar disorders.

The aim of this study is to create a repository of both cross-sectional and longitudinal data, including cognitive, linguistic, imaging and biofluid biological specimens, for neurodegenerative disease research and treatment.

This study is an observational, prospective genetic study. It aims to obtain DNA for research and testing from patients with PSP, CBS, MSA, and related neurological conditions and their families. Up to 1,000 adults who have been clinically diagnosed with PSP, CBS, MSA, or related neurological conditions will be enrolled. The study intervention involves sequencing of participant blood samples using non-CLIA-approved whole genome sequencing at the National Institutes of Health. Pathogenic variants that are deemed possibly related to these conditions will be confirmed using CLIA-approved testing. The study involves minimal risk to participants.

The neurological examination (NE) is a cornerstone of clinical neurology, with ocular motor assessment being a key component. Technology offers an opportunity to augment and standardize parts of the NE. Eye-tracking systems provide objective quantitative data on eye movements by continuously tracking the eye over time. This data can be used to derive parameters like saccadic latency, gaze velocity, and fixation stability with a precision that is impossible to achieve through human observation by neurologists. The integration of such technology could enhance the traditional NE. Before such technology can be widely adopted, its feasibility and acceptability in a clinical population must be established. The primary purpose of this study is to assess the usability of a novel eye-tracking system from the patient's perspective when used in a clinical settings. A secondary purpose is to determine if quantitative data from the eye-tracker correlate with the findings of the traditional clinical neurological examination and to explore whether eye-tracking can provide additional, complementary information not typically captured by standard clinical assessment. To achieve these aims, the study will assess several outcome measures. The primary outcome measure is the Usability of the Eye-Tracking System, which will be measured using the System Usability Scale (SUS). Beyond the primary objectives, this study will investigate two secondary objectives. The first involves assessing the relationship between quantitative eye-tracking parameters and clinical ocular motor assessment. Specifically, the investigators will analyze objective, numerical data obtained from eye-tracking systems and the clinician's subjectively graded assessment of ocular movements derived from the standard neurological examination. The second is the exploratory analysis of novel eye-tracking biomarkers. This involves quantifying and analyzing eye-tracking parameters not typically assessed during a routine NE. For example, the dynamics of the pupillary light reflex or the frequency of microsaccades. The aim is to identify potential digital biomarkers that could provide additional objective insights into ocular motor function and neurological status.

A quantitative evaluation method was developed for Parkinson's disease and other atypical parkinonism by integrating an innovative motor paradigm with perception technologies and artificial intelligence. Combined with traditional motor paradigms and the acute levodopa challenge test, this study aims to identify diagnostic cut-off values for PD and other atypical parkinonism, explore digital biomarkers for early and differential diagnosis, and establish a corresponding diagnostic model.