Through the induction of a fast thermal gradient, short pulses of infrared light provide a label-free method to stimulate and inhibit action potentials in neurons, but the biophysical effects that underlie this phenomenon are poorly understood. To understand this phenomenon, a computational model of metabolic rates and coenzyme binding dynamics in response to infrared light exposure was developed to investigate the effects of infrared light on cellular metabolism. The resulting model will facilitate our understanding of infrared neural stimulation to accelerate the development of infrared-light technologies which provide a noninvasive, nongenetic, and reversible method to stimulate or inhibit nerve activity.
Imaging the spatial and temporal effects of millisecond duration pulses of infrared light on neurons requires image frame rates approaching 1000+ Hz to capture neural activity. Autofluorescence imaging of the metabolic coenzymes reduced nicotinamide adenine dinucleotide and flavin adenine dinucleotide provides information about cellular metabolism and can be a surrogate measurement of neural activity. Here, we are combining fast fluorescence microscopy techniques with modeling and machine learning to image autofluorescence dynamics in cells following exposure to infrared light.
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