Image and signal processing toolboxes

Imaging data were analyzed using MATLAB (R2014b) with the Image and Signal Processing Toolboxes (Mathworks Inc, USA). The voltage imaging signal was calculated as the ratio of mKate2 (FRET acceptor) to mCitrine (FRET donor) fluorescence, taken after equalization of heartbeat-related fluorescence modulation (Akemann et al., 2012 (link)). Notably this equalization procedure has been to shown to effectively discount vascular artifacts such as heart-beat oscillation and slow (<1 Hz) fluctuation due to hemodynamics in anesthetized (Akemann et al., 2012 (link)) and awake (Carandini et al., 2015 (link)) conditions. The resulting ratiometric sequences of voltage maps were then smoothed spatially (2D gaussian, σ = 49.5 μm) and temporally (Chebyshev Type II band-path filtering 0.5–9 Hz, function imfilter, MATLAB, Mathworks Inc, USA). We excluded the first 10 s of each sequence to remove any effects of sensory stimulation (e.g., shutter noise and excitation light onset) related to the initiation of imaging sequence acquisition.

All data were baseline normalized on a pixel-wise level, i.e., each pixel's baseline is the average over its values, for each 60s image sequence. Each 60s dataset was temporally smoothed using a sliding window to average pixel activity across 4 consecutive time points and then spatially smoothed using an 8 × 8 pixel averaging filter. Data were then high-pass filtered at 0.5 Hz in order to reduce the effect of slow trends in the baseline signal that may cause artificial (i.e., non-neural) correlations (Akemann et al. 2012 (link)). The first 10s of each image sequence were discarded to remove possible contribution from environmental cues present at the start of each imaging sequence (e.g., shutter noise and excitation light). Subsequent analyses were constrained to pixels within masks, drawn by hand for each mouse, which defined the extents of the bone window. We refined these masks by excluding regions with poor signal-to-noise ratios, defined as those pixels in which the protein expression (estimated as time-averaged absolute fluorescence intensity) was less than 50% of the maximum level across the field of view for each mouse. Imaging data were analyzed with Matlab using the Image and Signal Processing Toolboxes (Mathworks) and ImagePro 6.2 image processing software.