Multiple Sclerosis “Rebound” [After Stopping Medication]

Dr. Brandon Beaber (@Brandon_Beaber) / Twitter


Sometimes people with MS can get dramatically worse after stopping certain types of disease modifying therapies known as immunosequestrants (such as Tysabri, gilenya, mayzent, zeposia). This video discusses the phenomenon of “rebound” disease activity and reviews the risk factors and treatment references:

Using robotic exoskeletons to restore function in persons with multiple sclerosis

Active vs. passive exoskeletons explained


John DeLuca, PhD, and Brian M. Sandroff, PhD, of Kessler Foundation provided expert commentary on a current controversy in the management of persons with multiple sclerosis (MS). Their article, titled, “Exoskeletons in MS rehabilitation are ready for widespread use in clinical practice: Commentary,” was published in Multiple Sclerosis Journal on July 6, 2022, doi: 10.1177/13524585221102923.  Their commentary assessed the opposing opinions of experts in Italy (Calabrò) and Belgium (Swinnen et al), supporting ‘yes’ and ‘no’, respectively, published in the same journal.

Robotic exoskeletons are being more widely used during clinical rehabilitation, which is considered the optimal pathway to restoring function in individuals with MS. But experts are debating whether current evidence supports their widespread use in this population.

“While we recognize the promise of wearable, powered exoskeletons, broad implementation must be based on appropriately powered clinical studies,” said lead author John DeLuca, PhD, senior vice president for Research and Training at Kessler Foundation, echoing a point made by the authors of both opposing opinions.

On the positive side, Calabrò cited a growing number of studies support the capability of robotic exoskeletons to provide intensive high-quality rehabilitation with the potential for functional improvements in upper as well as lower extremities. “However, we agree that wide acceptance of this type of intervention depends on clinical studies aimed at determining optimal selection criteria, and the timing, dosing, and long-term outcomes of this type of intervention,” added Dr. DeLuca.

In their ‘no’ opinion, Swinnen et al cited the need for improvements in the comfort, weight, and ease of use of robotic exoskeletons. “But given the ongoing improvements in these devices and the recent positive regulatory and research activity, these are not insurmountable barriers to the clinical application of these devices in MS,” noted Dr. Sandroff, senior research scientist in the Center for Neuropsychology and Neuroscience Research at Kessler Foundation. “Similarly, while there are real issues relating to costs and availability of these devices, required training of therapists, and insurance coverage, these may resolve as the body of literature grows.”

Rehabilitation using robotic exoskeletons may confer benefits beyond improvement in motor function, which has been observed in the population with spinal cord injury. “We cannot overlook the potential for improvements in cardiovascular health, bowel and bladder function, and psychological outcomes,” Dr. Sandroff concluded. “These are important considerations for the future expansion of powered exoskeletons in MS rehabilitation.”

The machine learning model predicts the health conditions of people with Multiple Sclerosis during stay-at-home periods

Smartphone and watch


New CMU-led research has developed a model that can predict how stay-at-home orders affect the mental health of people with chronic neurological disorders. CREDIT Irina Shatilova

Research led by Carnegie Mellon University has developed a model that can accurately predict how stay-at-home orders like those put in place during the COVID-19 pandemic affect the mental health of people with chronic neurological disorders such as multiple sclerosis.

Researchers from CMU, the University of Pittsburgh and the University of Washington gathered data from the smartphones and fitness trackers of people with MS both before and during the early wave of the pandemic. Specifically, they used the passively collected sensor data to build machine learning models to predict depression, fatigue, poor sleep quality and worsening MS symptoms during the unprecedented stay-at-home period.

Before the pandemic began, the original research question was whether digital data from the smartphones and fitness trackers of people with MS could predict clinical outcomes. By March 2020, as study participants were required to stay at home, their daily behavior patterns were significantly altered. The research team realized the data being collected could inform the effect of the stay-at-home orders on people with MS.

“It presented us with an exciting opportunity,” said Mayank Goel, head of the Smart Sensing for Humans (SMASH) Lab at CMU. “If we look at the data points before and during the stay-at-home period, can we identify factors that signal changes in the health of people with MS?”

The team gathered data passively over three to six months, collecting information such as the number of calls on the participants’ smartphones and the duration of those calls; the number of missed calls; and the participants’ location and screen activity data. The team also collected heart rate, sleep information and step count data from their fitness trackers. The research, “Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-Home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping,” was recently published in the Journal of Medical Internet Research Mental Health. Goel, an associate professor in the School of Computer Science’s Software and Societal Systems Department (S3D) and Human-Computer Interaction Institute (HCII), collaborated with Prerna Chikersal, a Ph.D. student in the HCII; Dr. Zongqi Xia, an associate professor of Neurology and director of the Translational and Computational Neuroimmunology Research Program at the University of Pittsburgh; and Anind Dey, a professor and dean of the University of Washington’s Information School.

The work was based on previous studies from Goel’s and Dey’s research groups. In 2020, a CMU team published research that presented a machine learning model that could identify depression in college students at the end of the semester using smartphone and fitness tracker data. Participants in the earlier study, specifically 138 first-year CMU students, were relatively similar to each other when compared to the larger population beyond the university. The researchers set out to test whether their modeling approach could accurately predict clinically relevant health outcomes in a real-world patient population with greater demographic and clinical diversity, leading them to collaborate with Xia’s MS research program.

People with MS can experience several chronic comorbidities, which gave the team a chance to test if their model could predict adverse health outcomes such as severe fatigue, poor sleep quality and worsening of MS symptoms in addition to depression. Building on this study, the team hopes to advance precision medicine for people with MS by improving early detection of disease progression and implementing targeted interventions based on digital phenotyping.

The work could also help inform policymakers tasked with issuing future stay-at-home orders or other similar responses during pandemics or natural disasters. When the original COVID-19 stay-at-home orders were issued, there were early concerns about its economic impacts but only a belated appreciation for the toll on peoples’ mental and physical health — particularly among vulnerable populations such as those with chronic neurological conditions.

“We were able to capture the change in people’s behaviors and accurately predict clinical outcomes when they are forced to stay at home for prolonged periods,” Goel said. “Now that we have a working model, we could evaluate who is at risk for worsening mental health or physical health, inform clinical triage decisions, or shape future public health policies.”

Study advances knowledge of the role of brain pathology and cognitive fatigue in multiple sclerosis

Kessler researchers demonstrate the relationship between the rate of cognitive fatigue in MS and microstructural brain changes. Findings promise to advance the development of clinical interventions for disabling fatigue

Rocco Ortenzio Neuroimaging Center at Kessler Foundation

This study was conducted using the latest neuroimaging techniques at the Ortenzio Center, which is dedicated solely to rehabilitation research, CREDIT Kessler Foundation

Using advanced diffusion neuroimaging technology, Kessler Foundation researchers investigated the relationship between the rate of cognitive fatigue to microstructural changes in the brain in persons with multiple sclerosis. Their findings help fill a gap in the current understanding of how brain pathology influences the development of fatigue over time.

Their findings were reported in Frontiers in Neurology on July 04, 2022, in the open access article “Associations of White Matter and Basal Ganglia Microstructure to Cognitive Fatigue Rate in Multiple Sclerosis,” (doi: 10.3389/fneur.2022.911012). The authors are Cristina Almeida Flores Román, PhDGlenn Wylie, DPhilJohn DeLuca, PhD, and Bing Yao, PhD, and of Kessler Foundation.

The study was conducted at the Rocco Ortenzio Neuroimaging Center at Kessler Foundation, which is dedicated solely to rehabilitation research. Participants were 62 individuals with relapsing-remitting MS. All completed questionnaires measuring depression, state and trait anxiety, and trait fatigue. While in the scanner, participants underwent a cognitively fatiguing task. In addition to measuring rate of cognitive fatigue, researchers measured whole brain lesion volume and performance during the fatigue-inducing task.

“We found that the cognitive rate related to white matter tracts, many with associations with the basal ganglia or what we have proposed as the ‘fatigue network’,” said lead author Dr. Román, National MS Society postdoctoral fellow at Kessler Foundation. “These findings bring us closer to understanding how brain pathology impacts the experience in the moment. This is fundamental to developing effective interventions for managing the disabling fatigue of MS and other neurological conditions.”