Vascular Integrity SysTem for Assessment and Treatment (VISTAT)
The patent-pending technology will soon be available for use with MS patients and for exploration of broader applications with a healthy-aging population.
Identifies individuals susceptible to cognitive decline
RISK LEVEL MEASUREMENT
Uses the “Arterial Compliance Index” (ACI) biomarker
SUPERIOR PREDICTIVE ABILITY
In multiple sclerosis (MS) patients
Potential to determine efficacy of experimental therapeutics to improve cognition
CAN HELP CLINICIANS:
- Better monitor the cognitive trajectory of patients and research participants
- Intervene earlier in people more susceptible to developing cognitive decline
- Assess the impact of targeted treatments
Patent-pending technology has broader applications to evaluate efficacy of treatments
They investigated 30 MS patients and 14 age-, sex- and education-matched healthy individuals. The MS patients were further divided into cognitively normal and slow groups based on their processing speed. Participants underwent a 10-minute scan using cutting-edge dual-echo functional magnetic resonance imaging (fMRI) that allowed researchers to simultaneously record brain activity and brain blood-flow. Study participants also underwent a comprehensive neuropsychological evaluation, both during and after MRI scanning.
Through this approach, they found that cognitively slow MS patients had stiffer arteries along the vascular tree in the brain when compared to both cognitively normal MS patients and healthy control participants. By associating the arterial compliance index (ACI) with individual reaction time, researchers were able – for the first time ever – to create a predictor of future cognitive decline.
The potential to predict cognitive performance in MS compared to currently available metrics (age, disease duration, Expanded Disability Status Scale, total lesion volume, brain atrophy, 9-hole peg test performance) revealed that ACI performed approximately 15% better than all other currently available MS metrics combined. The results are published in Multiple Sclerosis Journal (August 2019).
“By providing a predictor of future cognitive change, this groundbreaking technology creates the opportunity for early intervention,” stated Dinesh Sivakolundu, the study’s lead author.