A Low-Cost Modular Multi-Region Electrode for Distributed Network Recording and Brain State Decoding

Area of Science:

• Neuroscience
• Biomedical Engineering

Background:

• Current brain state decoding methods for neuromodulation are limited by inadequate electrodes and recording strategies.
• Multi-region recording offers network-level insights but lacks low-cost, flexible electrode solutions and standardized workflows.

Purpose of the Study:

• To develop a low-cost, modular 16-channel electrode for multi-region local field potential (LFP) recording in small animals.
• To establish an integrated workflow for brain state decoding using spectral analysis, functional connectivity, and machine learning.

Main Methods:

• Designed a modular, silica capillary tube-based 16-channel electrode for flexible, low-cost assembly.
• Validated the electrode in rats, targeting eight emotional network nuclei for LFP recording.
• Developed a machine learning pipeline utilizing spectral analysis, functional connectivity, and a random forest classifier for brain state decoding.

Main Results:

• Achieved 79.2% implantation accuracy with stable LFP recordings for over 3 months.
• Identified state-specific oscillatory changes and drug-induced network synchronization changes in a depression model.
• Attained ~96.4% accuracy in decoding brain states from multi-region signals using the developed machine learning pipeline.

Conclusions:

• The modular multi-region electrode offers a practical, adaptable, and cost-effective platform for long-term distributed neural recording.
• The system supports quantitative LFP analysis, functional connectivity assessment, and high-accuracy brain state decoding.
• This technology provides a foundation for preclinical closed-loop neuromodulation research.