Tracking axon initial segment plasticity using high-density microelectrode arrays: A computational study.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Electrophysiology

Background:

• The nervous system maintains stable function through homeostatic plasticity, involving negative feedback mechanisms.
• The axon initial segment (AIS) exhibits structural plasticity, but its regulation and functional role are not fully understood.
• Tracking AIS plasticity in large neuronal populations is challenging due to expression heterogeneity.

Purpose of the Study:

• To develop an analysis framework for detecting activity-dependent AIS structural plasticity using high-density microelectrode arrays (HD-MEAs).
• To investigate the relationship between AIS position changes and simulated extracellular field potentials.
• To establish a method for high-throughput experimental studies of AIS plasticity.

Main Methods:

• Utilized sophisticated computational models to simulate extracellular field potentials.
• Systematically explored the effects of incremental changes in AIS positions on simulated electrophysiological readouts.
• Identified specific feature changes in extracellular fields indicative of AIS plasticity.
• Trained machine learning models to accurately detect these signatures.

Main Results:

• An ensemble of extracellular field potential features reliably characterizing AIS plasticity was identified.
• Computational models accurately detected simulated AIS plasticity signatures.
• Demonstrated the feasibility of using extracellular recordings to infer AIS structural changes.

Conclusions:

• A hybrid analysis framework was proposed for high-throughput detection of activity-dependent AIS plasticity.
• This framework can leverage HD-MEA recordings to study neuronal homeostatic mechanisms.
• Advances in computational analysis are crucial for understanding dynamic neuronal processes.