Multiple Particle Tracking Detects Changes in Brain Extracellular Matrix and Predicts Neurodevelopmental Age.

Area of Science:

• Neuroscience
• Biophysics
• Materials Science

Background:

• Brain extracellular matrix (ECM) structure is crucial for neural development and function.
• Changes in ECM are implicated in neurodevelopment, neural injury, aging, and neurological diseases.
• Understanding ECM microstructure is key to deciphering these processes.

Purpose of the Study:

• To demonstrate the utility of multiple particle tracking (MPT) for probing brain ECM microstructure changes.
• To correlate ECM structural changes with chronological age in developing rats.
• To develop a predictive model for age based on ECM features.

Main Methods:

• Multiple particle tracking (MPT) of polystyrene nanoparticles in organotypic rat brain slices (age 14-70 days).
• Analysis of nanoparticle diffusion and trajectory data.
• Machine learning (boosted decision tree) model training using trajectory features.

Main Results:

• Nanoparticle diffusion in the brain extracellular space showed an inverse relationship with age.
• The distribution of effective ECM pore sizes shifted towards smaller pores during development.
• A machine learning model accurately predicted chronological age from MPT data.

Conclusions:

• MPT combined with machine learning is effective for measuring brain ECM microstructure and predicting age.
• This approach enhances understanding of ECM roles in development, aging, and injury.
• Potential for developing models to detect and quantify neural injury based on microenvironmental changes.