Automated 3D Axonal Morphometry of White Matter

Area of Science:

• Neuroscience
• Biophysics
• Medical Imaging

Background:

• White matter functionality is crucial for brain connectivity, relying heavily on axonal structure.
• Current analysis of axonal structure using 2D cross-sections is limited and potentially inaccurate.
• Understanding 3D axonal morphology is essential for accurate assessment of white matter integrity.

Purpose of the Study:

• To develop an automated pipeline for 3D segmentation and morphometric analysis of white matter ultrastructure.
• To overcome the limitations of manual segmentation and 2D analysis of axonal morphology.
• To investigate the 3D axonal structure and its alterations following brain injury.

Main Methods:

• Development of ACSON (AutomatiC 3D Segmentation and morphometry Of axoNs), an automated pipeline for 3D white matter analysis.
• Segmentation of myelin, axons (myelinated and unmyelinated), mitochondria, cells, and vacuoles.
• Application of ACSON to serial block-face scanning electron microscopy images of rat corpus callosum.

Main Results:

• Demonstrated that myelinated axon cross-sections are typically elliptic, not circular, with significant longitudinal diameter variation.
• Revealed a significant reduction in myelinated axon diameter in the ipsilateral corpus callosum of brain-injured rats 5 months post-injury.
• Indicated ongoing axonal alterations in chronic brain injury models.

Conclusions:

• ACSON provides an efficient and accurate method for 3D axonal morphometry, surpassing 2D limitations.
• Axonal morphology is complex in 3D, with significant implications for understanding white matter function.
• Brain injury induces lasting changes in axonal structure, highlighting the need for advanced imaging techniques for chronic injury assessment.