Process and Method for Two-Photon Calcium Image Analysis

Technology

This technology is a method of computationally reducing noise in live imaging of neuronal circuits in the brain. Neurons fire in a repeated, periodic fashion in response to stimuli, therefore when the firing of an individual neuron is traced over time the resulting graph forms a wave function. These inventors use a cyclic descent algorithm to converge on the harmonic regression that best describes the fluorescence intensity over time of each pixel. The resulting data ignores background noise that is not part of the stimulus-response cycle. Next, the regression generated by the algorithm at each pixel can be used to reconstruct a denoised image. The resulting images have improved subcellular resolution with greatly enhanced contrast and clarity. The algorithms used in this technology allow curve fitting to a complex shape and periodicity, which models cellular response better than the more constrained curve fitting techniques classically used in neurobiology. Additionally, the low computational complexity of the algorithms used in this technology facilitate real-time computation and high throughput processing.  

Problem Addressed

The brain is composed of thousands of neurons that form complex, interconnected circuitry. Due to this immense complexity, many of the mechanisms of brain function remain undiscovered. Recent developments in microscopy and fluorescent calcium responsive imaging agents now facilitate subcellular scale real-time imaging of brain neuronal circuits. However, the data generated by these imaging techniques is noisy and computationally complex to process. These inventors have developed new denoising algorithms that can be used for high throughput data analysis of live neuron imaging.  

Advantages

• Denoising method for neural activity imaging
• Enhances contrast and clarity
• Subcellular resolution of neural activity
• Computationally simple
• High throughput