Separation of hemodynamic signals from GCaMP fluorescence measured with widefield imaging

Abstract

Widefield calcium imaging is often used to measure brain dynamics in behaving mice. With a large field of view and a high sampling rate, widefield imaging can monitor activity from several distant cortical areas simultaneously, revealing cortical interactions. Interpretation of widefield images is complicated, however, by the absorption of light by hemoglobin, which can substantially affect the measured fluorescence. One approach to separating hemodynamics and calcium signals is to use multi-wavelength backscatter recordings to measure light absorption by hemoglobin. Following this approach, we develop a spatially-detailed regression-based method to estimate hemodynamics. The spatially-detailed model is based on a linear form of the Beer-Lambert relationship, but is fit at every pixel in the image and does not rely on the estimation of physical parameters. In awake mice of three transgenic lines, the Spatial Model offers improved separation of hemodynamics and changes in GCaMP fluorescence. The improvement is pronounced near blood vessels and, in contrast with other models based on regression or the Beer-Lambert law, can remove vascular artifacts along the sagittal midline. Compared to other separation approaches, the spatially-detailed model permits more accurate fluorescence-based determination of neuronal activity across the cortex.\n\nNEW & NOTEWORTHYThis manuscript addresses a well-known and strong source of contamination in widefield calcium imaging data: hemodynamics. To guide researchers towards the best method to separate calcium signals from hemodynamics, we compare the performance of several commonly used methods in three commonly-used Cre-driver lines, and we present a novel regression model that out-performs the other techniques we consider.