In Vivo Neurochemical Monitoring: Benchmarking Techniques, Integrating Modalities, and Selecting Platforms for Chronic, Freely Behaving Studies

Abstract

Monitoring neurochemical dynamics in the living brain is essential for understanding neural circuit function, behavior, and disease processes. While foundational techniques like fast-scan cyclic voltammetry (FSCV) and microdialysis have provided invaluable insights into neurotransmission, the field is now advancing toward enhanced stability, breadth, and multiplexing to address circuit-level complexities. This review highlights a remarkable convergence of independent technologies overcoming historical challenges for chronic, freely behaving monitoring. We examine key innovations across the technical spectrum, including single-atom catalysts that demonstrate enhanced long-term signal retention; biodegradable silk fibroin probes for untethered interfaces; and genetically encoded indicators (GEIs) such as the dLight and GRAB sensor families—that facilitate cell-type specific detection of dopamine, serotonin, and glutamate. Furthermore, we explore droplet-based microdialysis that combines omics discovery capabilities with second-scale resolution. Beyond hardware, we discuss the role of machine learning in automating signal classification and introduce the quality performance index (QPI) as a framework to guide platform selection. Finally, we address critical translational considerations, including regulatory pathways and ethical frameworks for neural data. The future of the field lies in integrating these complementary technologies—combining the redox precision of voltammetry with the molecular specificity of optical sensors—to achieve a comprehensive, multimodal understanding of brain chemistry.