Mesoscopic Imaging: Shining a Wide Light on Large-Scale Neural Dynamics

Abstract

Optical imaging has revolutionized our ability to monitor brain activity, spanning spatial scales from synapses to cells to circuits. Here, we summarize the rapid development and application of mesoscopic imaging, a widefield fluorescence-based approach that balances high spatiotemporal resolution with extraordinarily large fields of view. By leveraging the continued expansion of fluorescent reporters for neuronal activity and novel strategies for indicator expression, mesoscopic analysis enables measurement and correlation of network dynamics with behavioral state and task performance. Moreover, the combination of widefield imaging with cellular resolution methods such as two-photon microscopy and electrophysiology is bridging boundaries between cellular and network analyses. Overall, mesoscopic imaging provides a powerful option in the optical toolbox for investigation of brain function.

Figure 1.. Mesoscopic imaging of neural activity in the mouse neocortex.

A, Schematic illustrating the imaging system for acquiring mesoscopic fluorescence imaging data in the mouse. Ca2+ indicators are excited via output from an LED engine (violet and blue), and emitted (green) photons are collected through a large field-of-view objective and imaged via sCMOS camera. Reflected green light from a separate LED source can also be used to measure hemodynamic signals. The mouse is positioned on a running wheel for monitoring behavioral state, and a nearby display can deliver visual stimulation. B, Example mesoscopic imaging frames showing time-varying and spatially heterogeneous signals in the awake mouse. C, Time-series of activity corresponding to the indicated regions of interest indicated by circles in (B). Dashed lines indicate time of frames shown in (B). Bottom traces indicate simultaneously monitored pupil diameter and locomotion (wheel speed).

Figure 2.

Two examples for parcellating (segmenting) mesoscopic imaging data collected from a single mouse, showing the averaged response to a visual stimulus (arrow). Using a standard atlas based on averaged anatomical and molecular data sets, such as the Allen Institute CCFv3 (left)(Wang et al., 2020a), allows straightforward alignment and grouped analysis for multiple individual animals. A contrasting approach uses correlational analyses of activity to group pixels into parcels that may be unique for a given individual (right)(Mishne et al., 2018). Both methods can yield similar numbers of parcels, but often with markedly different boundaries.

Figure 3.

Example showing analysis of functional connectivity in cortical networks determined by dual 2-photon and mesoscopic imaging. A, Example image frames showing raw mesoscopic (upper) and 2-photon (lower) fields of view. The 2-photon data was collected through a microprism implanted on the cortical surface, highlighted by the white dashed box. The inner red box highlights the 2-photon field of view. B, Example traces showing three mesoscopic imaging frames aligned with the 2-photon imaged activity of a single neuron (orange) and the inferred neuronal spike data (black). C, Schematic showing the calculation of cell-centered networks (CCNs) using the dot product of neuronal spike data and each mesoscopic pixel. Each CCN represents the functional connectivity of a single neuron to the large-scale cortical network. (Adapted from Barson, Hamodi, et al., 2020).