Einstein Researchers Receive Grant to Study Brain Activity Related to Fall Risk in MS Patients

Stuart SchlossmanMS Research Study and Reports

December 17, 2019

Einstein Researchers Receive Grant to Study Brain Activity Related to Fall Risk in MS Patients

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grant awarded recently by the National Institutes of Health (NIH) will fund research to explore how brain changes in multiple sclerosis (MS) patients affect mobility.
The five-year grant (1R01NS109023-01A1), totaling $3.5 million, was awarded by the National Institute of Neurological Disorders and Stroke, part of the NIH, to a team led by Roee Holtzer, PhD, professor of neurology at Albert Einstein College of Medicine in the Bronx, New York.
Impaired mobility is one of the most common symptoms of MS, leading to frequent falls. According to the research team, not enough is known about mobility problems and falls in people with MS — issues that can affect patients’ quality of life significantly.
Previous research by Holtzer and colleagues suggested that motor problems in MS are due to abnormal functioning in the brain, particularly in a region called the prefrontal cortex, which is believed to play a critical role in orchestrating complex thoughts and actions.
The new project aims to determine whether changes in brain activity during walking can be used to predict fall risk in people with MS. To do this, the team will measure prefrontal cortex activity in 120 people with MS, and 120 people without (the control group), while they are walking.

Prefrontal cortex activity will be assessed through functional near-infrared spectroscopy, a non-invasive technique that measures how much oxygen-carrying blood is in certain parts of the brain. It can be used to estimate brain activity because when a part of the brain is more active, it requires more oxygen.
In addition, other traditional brain imaging techniques, such as magnetic resonance imaging (MRI), will be used to assess the structural integrity of participants’ brains.

Researchers hope findings from this study will allow the identification of biological targets that can be manipulated to minimize fall risk.
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