Success Story
Predicting Recovery in Stroke Patients
Neurology
Combining different technologies can reveal network effects within the brain that have implications for motor rehabilitation
Stroke is the leading cause of long-term disability, leaving more than 50 percent of patients with long-term deficits that affect their professional and personal lives. What happens in the brain after a stroke and which processes are relevant to recovery? Is it possible to predict the degree of recovery in individual patients and develop patient-tailored neuro-rehabilitative treatment to maximize treatment effects? Friedhelm Hummel of the EPFL sought to answer some of these questions with his PHRT grant.
Understanding how brain activity changes from the acute phase of stroke to the chronic phase is the overarching theme of his research. About 90 patients took part in the study. They were followed in the first three days after the stroke, then again after three weeks, at three months and at twelve months. Using a combination of different technologies, including functional and structural imaging, electrophysiology and extensive neuropsychological and motor testing, a vast amount of data was collected. In addition, machine learning approaches were applied to better understand the data.
“When you know that the funding will last for five years, you can really expand your research.”
“In recent years, it has become increasingly clear that the brain functions as a network”, Hummel explains. “Individual areas are not responsible for any given function in isolation, but rather function is the result of interactions between different brain regions.” The researchers focused on elucidating these network effects, both structurally and functionally. Structural networks can be thought of as the fixed architecture of the brain. “We wanted to understand the hard-wired changes caused by a stroke and how they affect patients’ symptoms and recovery,” he says. And at the functional level, they were interested in how the brain, with this fixed but damaged structure, can reorganize and bring together different areas to regain function.
One interesting finding was that the effects of a stroke may be more varied than first meets the eye. “When a stroke leaves a patient paralyzed, it was long thought that this was the only symptom. But we found that about 80% of patients who were selected for motor deficits also have problems in one to five cognitive domains”, he explains. Therefore, a stroke is usually more of a combination of deficits rather than an isolated one. “This has implications for motor rehabilitation. For example, someone with an additional attention problem is likely to find training programs more challenging.”
The variety of technologies used in these studies is quite unique. “These patients are extremely well characterized, in fact, this cohort is probably one of the largest in the world that has been studied with the detail we have”, says Hummel. His research group has so far published around 40 papers acknowledging PHRT funding. In one of the milestone papers, for instance, they showed that recovery over the first three months can be accurately predicted using machine learning and structural connectivity analyses. This can be done as early as in the subacute stage following a stroke. PHRT funding has been crucial to its success. “This project would have been difficult to do without the planning security we had. When you know that the funding will last for five years, you can really expand your research.”