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In Vivo Neurochemical Monitoring: Benchmarking Techniques, Integrating Modalities, and Selecting Platforms for
Greatness O Olaitan1, Rhoda Asiamah1, Wendy Lynch2
1Department of Chemistry, University of Virginia, Charlottesville, Virginia22904, United States.
ACS Sensors
|August 12, 2026
Summary
New technologies enable chronic, freely behaving brain monitoring for neurochemical dynamics. Innovations in sensors, probes, and machine learning offer enhanced stability and specificity for studying brain function and disease.
Area of Science:
- Neuroscience
- Biotechnology
- Chemical Engineering
Background:
- Monitoring neurochemical dynamics is crucial for understanding brain function and disease.
- Traditional methods like FSCV and microdialysis have limitations in stability and multiplexing.
- Advancements are needed for chronic, freely behaving monitoring at the circuit level.
Purpose of the Study:
- To review recent technological innovations for advanced brain neurochemical monitoring.
- To highlight the convergence of technologies addressing historical challenges in the field.
- To provide a framework for selecting and translating these technologies.
Main Methods:
- Review of innovations including single-atom catalysts, biodegradable probes, and genetically encoded indicators (GEIs).
- Exploration of droplet-based microdialysis and machine learning for signal analysis.
- Discussion of the Quality Performance Index (QPI) for platform evaluation.
Main Results:
- Key innovations enhance signal stability, enable cell-type specific detection (dopamine, serotonin, glutamate), and offer high-resolution analysis.
- Machine learning aids in automated signal classification.
- The QPI framework assists in selecting appropriate monitoring platforms.
Conclusions:
- A convergence of technologies is overcoming challenges in chronic brain monitoring.
- Integrating diverse methods like voltammetry and optical sensors promises comprehensive brain chemistry understanding.
- Translational considerations including regulatory and ethical frameworks are essential for neural data.
