The three-panel image shows how BRM (birefringence microscopy) maps myelin and reveals injury (yellow arrow). The large left panel is a color‑coded whole brain slice where colors show myelin fiber direction and brightness indicates the density of myelin across the brain; the top right is a higher resolution zoomed-in image (from the red boxed region) showing the breakdown of myelin (red arrows) as a result of the brain injury. The bottom right close‑up shows a comparison from the same region of the brain without injury where there is no visible breakdown of myelin. This work shows the feasibility to quantify myelin damage across different regions of the brain, and under different disease and treatment conditions. 

Research

Researchers Develop Highly Sensitive Imaging Technique to Detect Myelin Damage

Revealing how myelin breaks down may lead to therapies to reduce damage or slow damage progression.

The breakdown of myelin, the insulating layer around brain cells that supports brain function, is prevalent in a range of neurodegenerative diseases, aging and because of various forms of trauma. While electron microscopy is considered the gold standard for ultrastructural imaging of myelin, it is considered impractical for large-scale studies due to its limited field of view and time-consuming and complex sample preparation requirements.

In a new study from Boston University Chobanian & Avedisian School of Medicine and BU’s College of Engineering, researchers used a special microscope called birefringence microscopy (BRM) paired with an automated deep learning algorithm to reliably count and map myelin damage across whole sections of the brain—something not feasible with other techniques. The ability to image and measure damage to myelin will lead to better understanding the patterns and extent that occurs with disease, injury and normal aging.

“A major advantage of BRM over conventional imaging methods is its ability to rapidly image large areas at high resolution without special staining, making it uniquely suited for studying widespread myelin pathology,” says corresponding author Alex Gray, PhD, ’25.

The researchers used two groups of experimental models that had sustained limited damage in the motor area of the brain, mimicking a stroke. Models were treated with stem cell derived extracellular vesicles (a therapeutic treatment) fully recovered from injury which was shown when the imaged brain sections were seen with the BRM. The researchers then trained an AI deep learning algorithm to automatically identify and quantify myelin damage across the brain. Lastly, they compared the amount and location of damage between treated and untreated models to relate tissue changes to recovery of function.

According to the researchers, this approach not only provides insights into the spatial distribution of myelin debris in this model, but also offers a framework for studying other models of myelin damage, ultimately contributing to a deeper understanding of the relationships between myelin structural integrity and functional and cognitive deficits. “This can guide development and testing of therapies that protect or restore neural wiring. It may help research into stroke and ischemic injury, chronic traumatic encephalopathy (CTE), multiple sclerosis, Alzheimer’s disease and other neurodegenerative conditions with myelin involvement, and even age-related cognitive decline,” adds Tara L. Moore, PhD, professor of anatomy and neurobiology.

These findings appear online in the journal Neurophotonics.