Fast near-whole–brain imaging in adult Drosophila during responses to stimuli and behavior

Whole-brain recordings give us a global perspective of the brain in action. In this study, we describe a method using light field microscopy to record near-whole brain calcium and voltage activity at high speed in behaving adult flies. We first obtained global activity maps for various stimuli and behaviors. Notably, we found that brain activity increased on a global scale when the fly walked but not when it groomed. This global increase with walking was particularly strong in dopamine neurons. Second, we extracted maps of spatially distinct sources of activity as well as their time series using principal component analysis and independent component analysis. The characteristic shapes in the maps matched the anatomy of subneuropil regions and, in some cases, a specific neuron type. Brain structures that responded to light and odor were consistent with previous reports, confirming the new technique’s validity. We also observed previously uncharacterized behavior-related activity as well as patterns of spontaneous voltage activity.Author summary: Whole-brain recordings give us a global perspective of the brain in action. This is already possible in humans, for which functional magnetic resonance imaging (fMRI) has opened a new chapter in the study of brain activity underlying behavior, but this technique has low spatial and temporal resolution. In animals, techniques for imaging a whole brain so far have allowed us to record activity at much higher spatial resolution, but these are still orders-of-magnitude slower than neuronal electrical activity. Here, we have developed a technique for ultra-fast imaging of whole-brain activity in fruit flies while they are behaving (walking, resting, or grooming) and when they perceive various stimuli. We find that there is a global increase in activity when the fly walks compared to when it rests, while only a small local increase is observed when the fly grooms compared to when it rests. We have also used computational techniques to extract activity from small brain regions or from specific neuron types and identified regions involved in turning left or right as well as regions with ongoing activity in the absence of stimuli or behavior.