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Analysis of large-scale neuronal imaging enables closed-loop investigation of neural dynamics

Analysis of large-scale neuronal imaging enables closed-loop investigation of neural dynamics
Stable real-time analysis of optical imaging of large-scale neuronal activities. a, FX design-based real-time analysis system for large-scale imaging, in comparison with traditional imaging analysis. b, Illustration of the whole-brain functional imaging and closed-loop delivery of feedback perturbation enabled by the real-time analysis system. c, Stable generation of feedback signals with a latency of <70.5 ms, measured during single-plane imaging at various frame rates or during volumetric imaging at 2.5 Hz. d, Feedback latency during stable imaging with various data stream rates. Credit: Nature Neuroscience (2024). DOI: 10.1038/s41593-024-01595-6

In a study published in Nature Neuroscience, Du Jiuli's group and Mu Yu's group at the Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences (CAS), and Hao Jie at the Institute of Automation of CAS, utilized data processing techniques from astronomy and a field programmable gate array graphics processing unit (FPGA-GPU) hybrid architecture to perform real-time registration, signal extraction, and analysis on data streams of up to 500MB/s.

They achieved the of hundreds of thousands of neurons in the zebrafish brain for the first time, enabling decoding of the activities of arbitrarily selected neuron ensembles to control external devices.

Whole-brain neuron activity imaging is a powerful tool for deciphering the principles of the brain. However, its enormous data processing demands have become a bottleneck, making real-time analysis and closed-loop research of brain functions challenging.

Inspired by rapid radio burst detection technology in astronomy, the researchers employed the design of the FX system and utilized the flexibility of FPGA programming to establish an optical neural signal preprocessing system.

This system regularizes signals from optical sensors and sends them to a GPU-based real-time processing system for high-speed nonlinear registration, neural signals extraction and decoding, and obtaining feedback signals for controlling external devices.

The system generated feedback signals by continuously monitoring the activities of zebrafish whole-brain neurons with a feedback delay of less than 70.5 milliseconds.

The performance of the system was demonstrated in three closed-loop brain science research scenarios: real-time locked to the activity of arbitrarily selected neuron ensembles, real-time visual stimulation locked to specific brain functional states, and directly driven by neuronal activities in the brain.

By functionally clustering neurons in the whole brain, the spontaneous activity of the selected ensembles was used as a trigger signal to implement real-time optogenetic stimulation on target neuron ensembles. Compared to open-loop stimulation, closed-loop stimulation effectively activated downstream brain areas.

By real-time monitoring of the activity of the locus coeruleus (LC) norepinephrinergic system, was applied during the excitatory phase of LC neurons representing the animal's awake state, resulting in stronger responses of neurons across the brain. This indicated that brain states modulate the processing of visual information, and that closed-loop sensory stimulation facilitate the study of the interaction between internal brain states and the .

Real-time dimensionality reduction of all brain neurons' activities to multiple neuron ensembles and closed-loop coupling with the visual environment enables the establishment of a virtual reality system directly driven by the activities of in the brain. In this virtual reality system, the gain coupling between the neuronal activity and the environment can be adjusted arbitrarily, allowing the neuron ensemble controlling the environment to adaptively adjust its output based on gain change.

Leveraging the real-time analysis of big data streams and high-throughput whole-brain imaging technology, the researchers will screen out neuronal activity characteristics suitable for optical brain-machine interface (BMI), uncover the underlying mechanisms, and develop more efficient optical BMI technologies.

This study marks a crucial step in the application of techniques such as virtual reality based on whole-brain cellular-resolution optical imaging and optogenetic control in the field of closed-loop whole-brain-scale research.

More information: Chun-Feng Shang et al, Real-time analysis of large-scale neuronal imaging enables closed-loop investigation of neural dynamics, Nature Neuroscience (2024). DOI: 10.1038/s41593-024-01595-6

Citation: Analysis of large-scale neuronal imaging enables closed-loop investigation of neural dynamics (2024, March 13) retrieved 19 May 2024 from
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