Highly Sensitive Imaging Technique Detects Myelin Damage

However, studying this damage on a large scale has been difficult. Electron microscopy, although the gold standard for ultrastructural detail, has major limitations: it requires extensive preparation, offers only a tiny field of view, and is too slow for broad tissue analysis. A new imaging approach now addresses these gaps by pairing birefringence microscopy (BRM) with deep learning to map myelin damage across whole brain sections.

In a study conducted at Boston University Chobanian & Avedisian School of Medicine (Boston, MA, USA), the researchers demonstrated that BRM, a type of microscope that captures structural information without staining, can rapidly image large brain areas at high resolution and serve as a foundation for automated analysis.

Their approach relied on BRM's strengths to visualize the distribution of myelin damage. By imaging full brain sections without special dyes, the researchers overcame the field-of-view and time constraints associated with traditional methods. This enabled them to collect detailed data across regions that were previously impractical to study at scale.

The team tested the technique in two groups of experimental models that had sustained confined injuries in the motor cortex to simulate stroke. One group received treatment with stem-cell-derived extracellular vesicles and made a full functional recovery, which was reflected in the BRM images. The researchers then trained a deep learning algorithm to automatically detect and quantify areas of damaged myelin throughout the brain. Comparing the treated and untreated models allowed them to link the extent and distribution of myelin changes to recovery outcomes.

Their findings, published in the journal Neurophotonics, show that this combined imaging–AI method can identify damage patterns, map where myelin debris accumulates, and offer a framework for studying many other forms of myelin loss. The researchers note that this approach contributes to a deeper understanding of how structural changes in myelin relate to cognitive and functional deficits, helping inform future therapeutic development. They also highlighted its potential relevance to stroke, ischemic injury, chronic traumatic encephalopathy (CTE), multiple sclerosis, Alzheimer’s disease, and age-related cognitive decline.

“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,” said corresponding author Alex Gray, PhD, ’25.

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Boston University Chobanian & Avedisian School of Medicine