New Light-Sheet Microscope Maps Rapid Neural Activity Across Zebrafish Brains

Understanding how neural networks operate across the brain requires imaging electrical activity with both high temporal resolution and broad spatial coverage. To advance this field, a recent study published in the journal Nature Methods introduced a remote-scanning light-sheet microscope that can record neuronal voltage fluctuations across the brains of larval zebrafish.

Zebrafish in an aquarium
Study: Voltage imaging of neurons distributed across entire brains of larval zebrafish. Image Credit: Kazakov Maksim/Shutterstock.com

The system combines brain-wide coverage with millisecond-scale temporal resolution, enabling real-time observation of neural activity across large tissue volumes.

Researchers found that visually evoked responses were systematically distributed across specific brain regions, while spontaneous neural bursts followed distinct temporal sequences. This approach provides a powerful framework for studying whole-brain dynamics while addressing key limitations of conventional deep-tissue optical imaging.

Limitations of Calcium Imaging

For decades, scientists have relied on calcium imaging to monitor activity in live neural networks. Although calcium fluorescence provides key information, it typically operates on a timescale of hundreds of milliseconds, making it difficult to capture rapid electrical spikes.

Calcium imaging inherently is very slow, so you’re talking about imaging activity on the order of seconds or even minutes. Typically that is too slow for us to be able to see a lot of these high-speed neural activities. Neurons compute using electrical activity, so with voltage imaging, you can get direct observation of that.

Former MIT research scientist and co-lead study author, Jie Zhang.

Genetically encoded voltage indicators (GEVIs), such as Positron2-Kv, offer a faster alternative by producing fluorescent signals when neurons fire. However, detecting these signals across large three-dimensional (3D) volumes remains challenging. Conventional light-sheet microscopy provides good optical sectioning but struggles to scan the focal plane rapidly enough to capture voltage changes without compromising resolution or signal quality.

Design of a Remote-Scanning Microscope

To overcome the speed limitations of conventional volumetric imaging, researchers developed a custom remote-scanning light-sheet microscope optimized for rapid acquisition and high spatial

resolution.

Instead of mechanically moving a heavy detection objective, the system employs a remote-refocusing module with an ultralight silver-coated mirror weighing 0.01 grams, driven by a piezoelectric actuator. This allows the optical focal plane to shift across approximately 200 micrometers in depth at speeds of up to 300 hertz.

The detection plane was synchronized with a laterally swept 515 nm illumination beam with microsecond precision. The excitation laser flashed for only 40 μs per frame, reducing motion blur and phototoxicity. Custom tube lenses minimized optical aberrations while maintaining a diffraction-limited field of view across a 900 µm span.

To increase imaging speed and light collection, the optical signal was divided with a knife-edge mirror and directed to two high-speed CMOS (complementary metal-oxide-semiconductor) cameras, effectively doubling the frame rate while preserving the wide field of view needed to image the larval zebrafish brain.

A secondary remote-scanning path at the orthogonal port of a polarizing beam splitter recovered otherwise lost fluorescence, increasing light-collection efficiency by 75%. Together, these optical and imaging modifications helped overcome mechanical scanning and data bandwidth limitations while preserving faint signals produced by GEVIs.

Capturing Neural Dynamics with High Resolution

Using the remote-scanning system on larval zebrafish expressing the Positron2-Kv voltage indicator, researchers achieved a volumetric scanning rate of approximately 200.8 hertz and mapped around 85% of the brain.

Image processing isolated signals from 12,935 to 19,039 neurons per subject, representing roughly one quarter of the total neuronal population in seven-day-old larvae. Baseline noise was measured at 1.58 ±0.45 times the theoretical shot-noise level.

However, the authors note that the 200-hertz sampling rate could miss some rapidly occurring spikes when used with the Positron2-Kv. Therefore, they avoided drawing any conclusions that required every electrical signal to be drawn.

The high-speed recordings revealed temporal patterns that slower imaging methods could not resolve. Following ultraviolet stimulation, neuronal activity developed over approximately 200 milliseconds and was concentrated in the optic tectum. Neurons in lateral regions generally activated first, followed by a sequence propagating toward medial regions, indicating organized spatial and temporal processing of the visual stimulus.

In addition to stimulus-evoked responses, the system also captured spontaneous synchronized bursts originating mainly in the cerebellum and medulla oblongata. Precise temporal analysis demonstrated a consistent firing sequence, with a compact neuronal cluster near the ventral medulla initiating the bursts before activity spread through the network.

These observations indicate the system's ability to resolve both stimulus-driven and spontaneous neuronal activity across large brain volumes at high temporal resolution.

Implications for Behavioral and Computational Neuroscience

The ability to optically monitor electrical activity across large brain regions could expand research in behavioral and computational neuroscience. Researchers can observe how localized sensory inputs are processed and translated into activity across distant, interconnected regions, revealing the temporal sequence of neural activation.

Different groups of neurons that are distributed across the brain coordinate together at millisecond timescales to generate a lot of behaviors and brain computations. To understand the principles, we need the technology to observe their activity at the same time, across the whole brain, so we are not missing any important participant neurons.

Zeguan Wang, former J. Douglas Ran Postdoctoral Fellow and the study’s co-lead author.

This technique provides a foundation for combining large-scale voltage imaging with targeted optogenetic stimulation. By using specific wavelengths of light to activate neurons while simultaneously recording brain-wide electrical responses, scientists could construct detailed functional circuit maps, which may prove useful for studying global brain states, including transitions between sleep cycles and the balance among excitatory and inhibitory signaling.

Future Directions in Optical Imaging Technology

This study demonstrates that large-scale optical recording of rapid neuronal voltage dynamics is feasible. By improving light-sheet scanning, photon collection, and data processing, researchers captured single-neuron activity and precise firing sequences across nearly the entire larval zebrafish brain.

These results highlight the value of millisecond-resolution volumetric imaging for studying neural activity across large biological networks.

Future work should focus on enhancing GEVIs and optical hardware. Employing multiphoton excitation and longer-wavelength illumination could further reduce tissue scattering and improve imaging of deeper structures. Continued advancements in optical engineering and computational processing could expand this approach for studying and understanding the organization and dynamics of complex neural networks.

Journal Reference

Wang, Z., et al. (2026). Voltage imaging of neurons distributed across entire brains of larval zebrafish. Nature Methods. DOI: 10.1038/s41592-026-03179-7. https://www.nature.com/articles/s41592-026-03179-7.

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Source:
Muhammad Osama

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Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

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