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Updated: Jan 12, 2026

Assessing Microglial Phagocytosis of Myelin Debris in vitro Under Repeated Magnetic Stimulation
Published on: June 17, 2025
Birefringence microscopy enables rapid, label-free quantification of myelin debris following induced cortical injury
Alexander J Gray1, Rhiannon E Robinson2, Samer A Berghol1
1Boston University College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States.
Significance:
Myelin breakdown is prevalent in a range of neurodegenerative diseases, aging, and following various forms of trauma. Yet, current imaging techniques have limited capacity for large-scale study of myelin structural damage. A high-throughput, quantitative imaging method would greatly enhance our understanding of myelin degradation in different contexts.
Aim:
We aim to establish birefringence microscopy (BRM) as a high-throughput, label-free imaging technique for large-scale, quantitative assessment of myelin pathology in post-mortem brain tissue. BRM has the capacity to provide rapid myelin imaging, which will provide information complementary to other myelin imaging techniques.
Approach:
BRM enables label-free structural imaging of myelin with high spatial resolution. We leverage the high-throughput imaging capability of BRM to characterize the distribution of myelin pathology in a rhesus monkey model of cortical injury across the corpus callosum. This framework is applied at two different post-injury survival times (6 and 12 weeks).
Results:
We validate BRM for label-free structural imaging of myelin pathology across large regions of tissue (within the corpus callosum) using a fluorescent myelin stain and several immunohistochemical labels. Next, we train and validate a deep learning-based object detection network for automated identification of myelin pathology, using BRM, in the corpus callosum of monkeys with an induced cortical lesion. BRM, paired with deep learning, revealed significantly higher myelin damage through the corpus callosum, resulting from the lesion, in 6-week recovery monkeys compared with 12-week recovery and age-matched controls ( ). There was no significant difference between 12-week recovery monkeys and age-matched controls.
Conclusions:
BRM enables large-scale assessment of myelin structural alterations in post-mortem brain tissue. When combined with deep-learning object detection, BRM enables rapid quantification of myelin damage in the corpus callosum after cortical injury. With proper training, this can be extended to study structural changes in other diseases and regions such as Alzheimer's disease and chronic traumatic encephalopathy as well as normal aging.

