Comprehensive imaging of cortical networks

https://doi.org/10.1016/j.conb.2015.03.016Get rights and content

Highlights

  • We review advances in imaging activity in large neural populations.

  • Calcium imaging promises comprehensive activity measurement in entire brain regions.

  • Red-shifted, faster, and more sensitive protein indicators will drive future advances.

  • Structural markers help segmentation, counting, unbiased sampling, and typing.

  • Standardized, well-vetted approaches for data processing are urgently needed.

Neural computations are implemented by activity in spatially distributed neural circuits. Cellular imaging fills a unique niche in linking activity of specific types of neurons to behavior, over spatial scales spanning single neurons to entire brain regions, and temporal scales from milliseconds to months. Imaging may soon make it possible to track activity of all neurons in a brain region, such as a cortical column. We review recent methodological advances that facilitate optical imaging of neuronal populations in vivo, with an emphasis on calcium imaging using protein indicators in mice. We point out areas that are particularly ripe for future developments.

Introduction

A fundamental question in neuroscience is how information relevant to behavior is processed in neural circuits. Even the simplest perceptual behaviors engage thousands of neurons across multiple regions of cortex [1, 2]. In contrast, typical electrophysiological studies sample only a handful of neurons in a single brain area [3]. Moreover, the type of neuron recorded and its position within the neural circuit are typically unknown [4] and long-term recording from the same neurons is inefficient [5]. As a result, the dynamics of neural circuits during behavior and learning are poorly understood.

Over the last decade, cellular calcium imaging has become widely used to image activity in neuronal populations [6]. In most neurons, action potentials (APs) are tightly coupled to large (20-fold) and rapid (rise time, 1 millisecond) increases in intracellular free calcium concentration, which can be used to read out neural activity [7, 8, 9]. Calcium imaging samples activity of all neurons in an imaging volume [10] and can readily be combined with visualization of cell type markers to analyze activity in specific nodes of neural circuits [11, 12, 13, 14, 15]. With genetically encoded calcium indicators, activity in the same neuronal populations has been imaged across days and weeks [16, 17•, 18, 19••].

Calcium imaging is now routinely used to measure the spatial organization of receptive fields [10, 20, 21] and to provide a relatively unbiased view of behavior-related activity in populations of neurons [13, 22]. Calcium imaging efficiently samples activity in relatively rare cell types [11] and measures changes in neural coding during learning [17•, 18, 19••, 23, 24].

The majority of studies still image only dozens to hundreds of neurons at a time. Here we review the challenges faced by attempts to produce comprehensive activity maps based on large-scale imaging. Our focus is on studies with single cell resolution based on two-photon laser scanning microscopy (TPLSM) in behaving head-fixed mice.

Section snippets

Fluorescent probes for neuronal function

The rapid development of protein sensors for neuronal function has been a major driver of new applications for imaging in vivo. In the past, experimenters had to choose between sensitive small-molecule sensors, which need to be loaded into brain tissue using invasive chemical methods [25], and less sensitive protein sensors, which can be delivered using the versatile tools of molecular genetics [26]. Recent efforts in protein engineering [16, 27••, 28•, 29, 30••] have boosted the sensitivity of

Gene delivery methods

In the mammalian brain, stable long-term expression of protein sensors for imaging remains challenging. Adeno-associated viruses (AAVs) and other viral vectors can produce the high intracellular GECI concentrations, typically 10–100 μM [17•, 42], required for in vivo imaging [16, 43] (Figure 2a). However, concentrations vary across neurons, within a cell type, and across cell types [19••]. Expression levels continue to rise over months until they cause aberrant cell health [16, 27••], limiting

Microscopy methods

The vast majority of cellular in vivo imaging studies have been performed using 2-photon laser scanning microscopy [50]. 2-Photon excitation provides localization of excitation in scattering tissue, which in turn produces three-dimensional contrast and resolution. As a result of localization of excitation, scattered and non-scattered photons both contribute to signal. This greatly boosts the image contrast and signal-to-noise ratio compared to wide-field microscopy, particularly when imaging in

From images to spikes

Imaging experiments produce stacks of images. Extracting quantitative and robust estimates of spike trains or time-varying spike rate is a complex computational problem.

The biophysics of calcium imaging implies that no data analysis trick will provide error-free spike trains. Furthermore, the performance of any algorithm depends on many factors relating to imaging conditions (Figure 1b,c). Even the spike rate of individual neurons matters. For neurons firing at very low rates, false-positive

Outlook: towards mesoscale imaging of neural networks

We are on the verge of a new kind of neurophysiology, bridging the gap between single neurons and brain areas. We recently demonstrated comprehensive measurement of behavior-related activity in the superficial layers of several cortical columns, producing a neural activity map comprising more than 10,000 neurons per animal [19••]. Microscopy schemes are in place to image multiple brain regions simultaneously [91•, 92]. It will soon be possible to track activity of all neurons in a brain region

Conflict of interest statement

Nothing declared.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

We thank Jeremy Freeman, Na Ji, Aaron Kerlin and Zengcai Guo for comments on the manuscript.

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