WASHINGTON, DC—Accurate classifiers for the diagnosis and subclassification of migraine have been developed using brain MRI data, reported Todd J. Schwedt, MD, at the 57th Annual Meeting of the American Headache Society. “We’ve built multivariate models of brain cortical thickness, cortical surface areas, and regional volumes that are pretty accurate for classifying individual people with migraine as having either chronic migraine versus episodic migraine,” said Dr. Schwedt. “We have also built pretty accurate classifiers for differentiating individual people as having chronic migraine versus being a healthy control. However, we didn’t have as much success with classifying episodic migraine versus healthy controls.”
Todd J. Schwedt, MD
In presenting his research, Dr. Schwedt had one important clarification. “I want to be very clear that I am not suggesting that we move towards a time where we should be using brain MRI to make the diagnosis of migraine,” he said. “Obviously that would be wasteful of medical resources. That’s not what we’re suggesting here.” But his team has developed objective imaging classifiers that may help to optimize clinical diagnostic criteria, much of which is currently consensus-generated and symptom-based, and may have other clinical utility, for example in a situation where it is difficult to make the right diagnosis in real time or distinguish between headache types with similar presentations.
An Unmet Need
“It would be great if we had biomarkers that would predict natural migraine progression,” said Dr. Schwedt, who is a Professor of Neurology at the Mayo Clinic in Scottsdale, Arizona. If a biomarker could predict who is likely to transition into a more severe state or who is more likely to revert to a less severe state, that information could inform treatment decisions. “It would be great to have biomarkers that could predict treatment responses within individuals. That would be very welcomed by our patients. It also would be great to have biomarkers that could give us early signals of likely treatment responses. That would really reduce the time it takes to develop new therapies and reduce the expense associated with that.” And finally, Dr. Schwedt said, it would be great to have biomarkers that could diagnose and subclassify headache disorders objectively.
To tackle this problem, Dr. Schwedt and his research colleagues used structural imaging data to build classifiers for migraine. They used measures of cortical surface area and cortical thickness and measures of volume from different regions of the brain to develop accurate classifiers that can identify, on an individual patient level, whether a brain MRI belongs to someone who has chronic migraine or episodic migraine, or to a healthy control. The researchers also sought to test the current threshold of 15 headache days per month for differentiating chronic from episodic migraine.
Building Migraine Models
Dr. Schwedt and research colleagues from the Mayo Clinic Arizona and Arizona State University enrolled adult participants between the ages of 18 and 65. Participants had either episodic or chronic migraine according to ICHD2 criteria. They also enrolled healthy controls. Participants were excluded if they were using migraine prophylactic medications or opiates, met criteria for medication overuse, had any acute or chronic pain conditions other than migraine, or if their brain MRI was abnormal according to typical clinical definitions.
Scans were done at two institutions, “the reason being that I started this study while I was at Washington University in St. Louis and then continued it at Mayo Clinic in Arizona,” explained Dr. Schwedt. Both scanners were Siemens 3-T scanners. “It is important to understand that nearly equal proportions of migraineurs and healthy controls were imaged on each of the two scanners. This really reduces the potential for scanner bias,” Dr. Schwedt added.
After collecting the MRIs, the researchers made the structural measurements with FreeSurfer, which is freely available software. “It allowed us to get 68 regional measurements of cortical thickness, 68 regional measurements of cortical surface area, as well as volume measurements in 68 different regions.” Overall, there were 204 structural measurements per individual. “Because we were looking at a lot of measurements for each participant, we decided to use a technique of dimension reduction called principal component analysis. We built principal components that accounted for 85% of the variability in each of the three structural measures—area, thickness, and volume,” Dr. Schwedt said. To build the migraine models, the researchers added principal components to the overall classifier until adding another one wouldn’t improve the accuracy by greater than 1%.
Next, the researchers performed tenfold cross validation and assessed classification accuracy within each of 10 runs, with data from 90% of participants randomly selected for classifier development and data from the remaining 10% of participants used to test classification performance. The average classification accuracy of those 10 runs was considered the overall accuracy.