Scaling up multiphoton neural scanning: the SSA algorithm.

Area of Science:

• Neuroscience
• Microscopy
• Biophysics

Background:

• Accurately mapping neural circuits requires simultaneous imaging of thousands of neurons.
• Conventional frame scanning methods in multiphoton microscopy are inefficient due to time spent scanning empty space.
• Inertial scanners in standard microscopes necessitate models that account for rotor and mirror momentum.

Purpose of the Study:

• To develop and validate a MATLAB model of multiphoton microscope scanning dynamics.
• To create a novel scanning algorithm that improves neural population imaging efficiency.

Main Methods:

• Characterized galvanometric scanners of a commercial multiphoton microscope.
• Developed a validated MATLAB model of microscope scanning dynamics.
• Simulated scan paths across varying neuronal densities and fields of view.

Main Results:

• The developed model accurately represents microscope scanning dynamics.
• A novel Adaptive Spiral Scanning (SSA) algorithm was developed, updating circular trajectory radii dynamically.
• SSA achieves higher sampling rates than shortest path methods and efficient coverage compared to frame-scanning approaches.

Conclusions:

• The validated model provides a foundation for optimizing scanning strategies.
• Adaptive Spiral Scanning (SSA) offers a more efficient method for high-density neural population imaging.
• This advancement facilitates the dense neural sampling required for reverse-engineering cortical circuit information processing.