Tag: News Approved

It has long been known that brain gray matter plays a key role in multiple sclerosis (MS) disease progression and cognitive impairment, but because magnetic resonance imaging (MRI) has only been able to detect lesions in white matter, neither clinicians nor researchers have had a way to detect or monitor gray matter (cortical) lesions. And while many new drugs developed in the past decade can slow disease progression significantly, they primarily work on reducing white matter lesions.

A University at Buffalo (UB)-led team now reports that it has found a way to use artificial intelligence to reveal these otherwise invisible cortical lesions by reviewing existing MRI scans. The researchers say the significance of finally being able to see what has been known as one of the most important indicators in MS disease progression cannot be overstated.

“Detecting previously invisible cortical lesions on conventional legacy MRI scans has major implications for MS research and clinical care,” commented Robert Zivadinov, MD, PhD, SUNY distinguished professor in the Department of Neurology and director of the Buffalo Neuroimaging Analysis Center (BNAC) in the Jacobs School of Medicine and Biomedical Sciences at UB. “The ability to see for the first time these previously hidden indicators of MS disease progression, including cognitive impairment and disability, is an important advance.”

Added Michael G. Dwyer, PhD, associate professor of neurology and biomedical informatics in the Jacobs School and a researcher with BNAC, “What this collaboration has been able to accomplish is a real success story for applying AI in the medical arena. We now have access to these incredibly useful data on MRI scans that were there but you couldn’t see them without using AI to pull them out. The computational methods are finally at the point where we can do this.”

Zivadinov is senior author, Dwyer first and corresponding author of the team’s published paper in Communications Medicine, titled “Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post- processing and deep learning.”

“Multiple sclerosis (MS) affects both the inner, connectivity-oriented portions of the brain (white matter) and the outer layer of the brain (the cortex),” the authors explained. While the involvement of cortical lesions in MS has been known almost since the identification of MS in the late 19^th century, they weren’t included on diagnostic criteria until the 21^st century. And even when they were included, it was noted that their use would be greatly limited due to the current capabilities of clinical MRI.

“Historically, research and clinical care in MS have focused on white matter, where focal demyelinating lesions are a hallmark of the disease,” they continued. And although there are now many therapies that can almost completely halt the incidence of new white-matter lesions in individuals with MS, they haven’t had the same impact on clinical progression, the team continued.

Over more recent decades it’s been found that gray matter is affected from the earliest MS disease stages, and it’s become evident that gray matter pathology is more than secondary to white matter damage. “From a clinical perspective, cortical lesions are strongly associated with clinical disability and cognitive impairment,” the authors stated. “They may also have more prognostic value than white matter lesions for disability and disease course.”

There’s an urgent need for in vivo imaging methods that can show gray matter lesions, they stressed. Dwyer added, “We have all been very frustrated, knowing that these cortical lesions were there but not being able to see them. There’s a lot of ongoing damage that continues to happen in MS that you won’t see with conventional MRI, but that histopathologists have been clearly demonstrating for decades on postmortem tissue.”

For their newly reported study the team applied advanced image processing techniques, including artificial intelligence, to standard MRI scans from a large MS clinical trial. “Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data,” they noted. “These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects.”

The AI approaches the researchers used, building on work from co-authors from the Netherlands, were designed to extrapolate vital information from the relationships between multiple images that can’t be seen on a single image.

The researchers combined multiple image-processing techniques, including a new one they developed called MMCLE, or multimodal cortical lesion enhancement. They then applied these techniques to MRI scans from the large, phase III FDA regulatory ORATORIO clinical trial, a study of the MS drug Ocrelizumab that included more than 700 participants.

They found that while individual images of a patient’s brain revealed mostly white matter lesions, once they applied the AI-based image processing methods to multiple different contrast images, they were able to see anywhere from 15 to 20 cortical lesions for each patient, more than 11,000 for the whole dataset. “We confirmed that cortical lesions can be clearly visualized and quantified with these methods,” they stated. “Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.”

Dwyer explained further, “If you look on the original scans, you generally can’t see the cortical lesions, but generative AI is very powerful because it can look between the scans and detect tiny differences between them. Because it sees those minor discrepancies, AI can reveal that there’s something going wrong there, that the tissue is not behaving like healthy tissue. The trained models can view multiple MRI images together and synthesize them and synthesize what had been missing.”

Zivadinov added “This work, which has revealed that there is so much invisible pathology in the brain, will have tremendous impact for reviewing data from past clinical trials and also for those going forward,” he says.

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