[CorTech] Millisecond-Precision Neural Voltage Imaging Using 2P Genetically Encoded Indicators (GEVIs) and 3D Acousto-Optic (AO) Scanning

Although two-photon calcium imaging has provided groundbreaking discoveries since its first application more than 30 years ago, the emerging field of two-photon voltage imaging offers significant advantages by directly capturing millisecond-scale membrane potential dynamics. Unlike calcium imaging, which relies on slower, indirect calcium signals that lag behind electrical activity and fail to resolve subthreshold potentials or hyperpolarizations, which are crucial for understanding synaptic integration and network computation. Voltage imaging with genetically encoded indicators (GEVIs) achieves sub-millisecond temporal resolution, allowing for the detection of individual action potentials and dendritic voltage fluctuations in vivo. Additionally, GEVIs enable cell-type-specific targeting and chronic recordings, which are nearly impossible to achieve with patch-clamp-based electrophysiological methods.

To fully exploit the advantages of voltage indicators, only fast imaging methods capable of matching this temporal resolution are essential. Cutting-edge 3D acousto-optical (AO) system combines acousto-optical scanning and 3D online real-time motion correction (RTMC) to achieve even 100 kHz/ROI sampling rates in freely behaving animals, resolving fast neuronal and dendritic signals as well as their integration. Voltage imaging with 3D acousto-optical system also facilitates unique closed-loop experiments, enabling simultaneous 3D photostimulation and 3D recording to evoke and track artificial percepts with 0.5 ms precision.
Furthermore, novel GEVI sensors that can be excited at shorter wavelengths with positive fluorescent signals incorporate all the optical advantages of two-photon imaging, including deep imaging depths of up to 700 μm through the entire mouse cortex and low phototoxicity. These advancements position two-photon voltage imaging as a transformative tool for studying neural circuits and advancing neuroprosthetic applications.