ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons and an Application to Electroceutical Modeling

Area of Science:

• Computational neuroscience
• Biophysics
• Neuroimaging

Background:

• Current neuronal modeling often neglects axons, despite their high excitability and importance in electrical stimulation.
• Existing datasets for axonal morphologies are scarce and insufficient for accurate modeling due to high geometric variability.

Purpose of the Study:

• To develop a generative method for creating biologically realistic cortical axon terminal arbors.
• To explore the use of these models in predicting neural tissue response to electrical stimulation.
• To analyze the complexity required for accurate axonal models in stimulation prediction.

Main Methods:

• Development of the Ruled-Optimum Ordered Tree System (ROOTS) for generating neuronal morphologies.
• Quantitative and qualitative analysis of the ROOTS generative algorithm.
• Comparison of generated fibers with histological data.
• Simulation of extracellular electrical stimulation and analysis of neural tissue response.

Main Results:

• ROOTS successfully generates highly branched cortical axon arbors with improved biological realism.
• Generated fibers show good agreement with histological observations.
• The study identified the necessary complexity of axonal arbors for accurate stimulation response prediction.
• Electrical stimulation analysis provided insights into excitation thresholds.

Conclusions:

• ROOTS enhances the biological realism of axonal models, surpassing existing methods.
• Accurate axonal models are essential for predicting neural responses to electrical stimulation.
• This work facilitates improved computational models for clinical and therapeutic electrical stimulation applications.