Optical recording of neuronal spiking activity from unbiased populations of neurons with high spike detection efficiency and high temporal precision

A simple MATLAB toolbox for analyzing calcium imaging data in vitro and in vivo

BACKGROUND

Fluorescence imaging of calcium dynamics in neuronal populations is powerful because it offers a way of relating the activity of individual cells to the broader population of nearby cells. The method's growth across neuroscience has particularly been driven by the introduction of sophisticated mathematical techniques related to motion correction, image registration, cell detection, spike estimation, and population characterization. However, for many researchers, making good use of these techniques has been difficult because they have been devised by different workers and impose differing - and sometimes stringent - technical requirements on those who seek to use them.

NEW METHOD

We have built a simple toolbox of analysis routines that encompass the complete workflow for analyzing calcium imaging data. The workflow begins with preprocessing of data, includes motion correction and longitudinal image registration, detects active cells using constrained non-negative matrix factorization, and offers multiple options for estimating spike times and characterizing population activity. The routines can be navigated through a simple graphical user interface. Although written in MATLAB, a standalone version for researchers who do not have access to MATLAB is included.

RESULTS

We have used the toolbox on two very different preparations: spontaneously active brain slices and microendoscopic imaging from deep structures in awake behaving mice. In both cases, the toolbox offered a seamless flow from raw data all the way through to prepared graphs.

CONCLUSION

The field of calcium imaging has benefited from the development of numerous innovative mathematical techniques. Here we offer a simple toolbox that allows ordinary researchers to fully exploit these techniques.

CADENCE - Neuroinformatics Tool for Supervised Calcium Events Detection

CADENCE is an open Python 3-written neuroinformatics tool with Qt6 graphic user interface for supervised calcium events detection. In neuronal ensembles recording during calcium imaging experiments, the output of instruments such as Celena X, Zeiss LSM 5 Live confocal microscope and Miniscope is a movie showing flashing cells somata. There are few pipelines to convert video to relative fluorescence ΔF/F, from simplest ImageJ plugins to sophisticated tools like MiniAn (Dong et al. in Elife 11, https://doi.org/10.7554/eLife.70661 , 2022). Minian, an open-source miniscope analysis pipeline. Elife, 11.). While in some areas of study relative fluorescence ΔF/F may be the desired result in itself, researchers of neuronal ensembles are typically interested in a more detailed analysis of calcium events as indirect proxy of neuronal electrical activity. For such analyses, researchers need a tool to infer calcium events from the continuous ΔF/F curve in order to create a raster representation of calcium events for later use in analysis software, such as Elephant (Denker, M., Yegenoglu, A., & Grün, S. (2018). Collaborative HPC-enabled workflows on the HBP Collaboratory using the Elephant framework. Neuroinformatics, 19.). Here we present such an open tool with supervised calcium events detection.

Two-Photon Voltage Imaging of Spontaneous Activity from Multiple Neurons Reveals Network Activity in Brain Tissue

Recording the electrical activity of multiple neurons simultaneously would greatly facilitate studies on the function of neuronal circuits. The combination of the fast scanning by random-access multiphoton microscopy (RAMP) and the latest two-photon-compatible high-performance fluorescent genetically encoded voltage indicators (GEVIs) has enabled action potential detection in deep layers in in vivo brain. However, neuron connectivity analysis on optically recorded action potentials from multiple neurons in brain tissue has yet to be achieved. With high expression of a two-photon-compatible GEVI, ASAP3, via in utero electroporation and RAMP, we achieved voltage recording of spontaneous activities from multiple neurons in brain slice. We provide evidence for the developmental changes in intralaminar horizontal connections in somatosensory cortex layer 2/3 with a greater sensitivity than calcium imaging. This method thus enables investigation of neuronal network connectivity at the cellular resolution in brain tissue.

Optimization of Stimulation Parameters for Targeted Activation of Multiple Neurons Using Closed-Loop Search Methods

Differential activation of neuronal populations can improve the efficacy of clinical devices such as sensory or cortical prostheses. Improving stimulus specificity will facilitate targeted neuronal activation to convey biologically realistic percepts. In order to deliver more complex stimuli to a neuronal population, stimulus optimization techniques must be developed that will enable a single electrode to activate subpopulations of neurons. However, determining the stimulus needed to evoke targeted neuronal activity is challenging. To find the most selective waveform for a particular population, we apply an optimization-based search routine, Powell's conjugate direction method, to systematically search the stimulus waveform space. This routine utilizes a 1-D sigmoid activation model and a 2-D strength-duration curve to measure neuronal activation throughout the stimulus waveform space. We implement our search routine in both an experimental study and a simulation study to characterize potential stimulus-evoked populations and the associated selective stimulus waveform spaces. We found that for a population of five neurons, seven distinct sub-populations could be activated. The stimulus waveform space and evoked neuronal activation curves vary with each new combination of neuronal culture and electrode array, resulting in a unique selectivity space. The method presented here can be used to efficiently uncover the selectivity space, focusing experiments in regions with the desired activation pattern.

Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo

Extracting neuronal spiking activity from large-scale two-photon recordings remains challenging, especially in mammals in vivo, where large noises often contaminate the signals. We propose a method, MLspike, which returns the most likely spike train underlying the measured calcium fluorescence. It relies on a physiological model including baseline fluctuations and distinct nonlinearities for synthetic and genetically encoded indicators. Model parameters can be either provided by the user or estimated from the data themselves. MLspike is computationally efficient thanks to its original discretization of probability representations; moreover, it can also return spike probabilities or samples. Benchmarked on extensive simulations and real data from seven different preparations, it outperformed state-of-the-art algorithms. Combined with the finding obtained from systematic data investigation (noise level, spiking rate and so on) that photonic noise is not necessarily the main limiting factor, our method allows spike extraction from large-scale recordings, as demonstrated on acousto-optical three-dimensional recordings of over 1,000 neurons in vivo.

Inferring Neuronal Dynamics from Calcium Imaging Data Using Biophysical Models and Bayesian Inference

Calcium imaging has been used as a promising technique to monitor the dynamic activity of neuronal populations. However, the calcium trace is temporally smeared which restricts the extraction of quantities of interest such as spike trains of individual neurons. To address this issue, spike reconstruction algorithms have been introduced. One limitation of such reconstructions is that the underlying models are not informed about the biophysics of spike and burst generations. Such existing prior knowledge might be useful for constraining the possible solutions of spikes. Here we describe, in a novel Bayesian approach, how principled knowledge about neuronal dynamics can be employed to infer biophysical variables and parameters from fluorescence traces. By using both synthetic and in vitro recorded fluorescence traces, we demonstrate that the new approach is able to reconstruct different repetitive spiking and/or bursting patterns with accurate single spike resolution. Furthermore, we show that the high inference precision of the new approach is preserved even if the fluorescence trace is rather noisy or if the fluorescence transients show slow rise kinetics lasting several hundred milliseconds, and inhomogeneous rise and decay times. In addition, we discuss the use of the new approach for inferring parameter changes, e.g. due to a pharmacological intervention, as well as for inferring complex characteristics of immature neuronal circuits.

Calcium imaging of single neurons enables the indirect observation of neuronal dynamics, for example action potential firing. In contrast to the precise timing of spike trains, the calcium trace is temporally rather smeared and measured as a fluorescence trace. Consequently, several methods have been proposed to reconstruct spikes from calcium imaging data. However, a common feature of these methods is that they are not based on the biophysics of how neurons fire spikes and bursts. We propose to introduce well-established biophysical models to create a direct link between neuronal dynamics, e.g. the membrane potential, and fluorescence traces. Using both synthetic and experimental data, we show that this approach not only provides a robust and accurate spike reconstruction but also a reliable inference about the biophysically relevant parameters and variables. This enables novel ways of analyzing calcium imaging experiments in terms of the underlying biophysical quantities.

Inference of neuronal network spike dynamics and topology from calcium imaging data

Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence ("spike trains") from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties.

Two-photon imaging of neural population activity in zebrafish

Rapidly developing imaging technologies including two-photon microscopy and genetically encoded calcium indicators have opened up new possibilities for recording neural population activity in awake, behaving animals. In the small, transparent zebrafish, it is even becoming possible to image the entire brain of a behaving animal with single-cell resolution, creating brain-wide functional maps. In this chapter, we comprehensively review past functional imaging studies in zebrafish, and the insights that they provide into the functional organization of neural circuits. We further offer a basic primer on state-of-the-art methods for in vivo calcium imaging in the zebrafish, including building a low-cost two-photon microscope and highlight possible challenges and technical considerations.

HelioScan: a software framework for controlling in vivo microscopy setups with high hardware flexibility, functional diversity and extendibility

Intravital microscopy such as in vivo imaging of brain dynamics is often performed with custom-built microscope setups controlled by custom-written software to meet specific requirements. Continuous technological advancement in the field has created a need for new control software that is flexible enough to support the biological researcher with innovative imaging techniques and provide the developer with a solid platform for quickly and easily implementing new extensions. Here, we introduce HelioScan, a software package written in LabVIEW, as a platform serving this dual role. HelioScan is designed as a collection of components that can be flexibly assembled into microscope control software tailored to the particular hardware and functionality requirements. Moreover, HelioScan provides a software framework, within which new functionality can be implemented in a quick and structured manner. A specific HelioScan application assembles at run-time from individual software components, based on user-definable configuration files. Due to its component-based architecture, HelioScan can exploit synergies of multiple developers working in parallel on different components in a community effort. We exemplify the capabilities and versatility of HelioScan by demonstrating several in vivo brain imaging modes, including camera-based intrinsic optical signal imaging for functional mapping of cortical areas, standard two-photon laser-scanning microscopy using galvanometric mirrors, and high-speed in vivo two-photon calcium imaging using either acousto-optic deflectors or a resonant scanner. We recommend HelioScan as a convenient software framework for the in vivo imaging community.

Optical recording of suprathreshold neural activity with single-cell and single-spike resolution

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs). Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue. Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (>97% spike detection efficiency), has a low rate of false positive spike detection (< 0.003 spikes/sec), and a high temporal precision (about 3 msec). This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo.

Impact of cortical plasticity on patterns of suprathreshold activity in the cerebral cortex

There are many cellular and synaptic mechanisms of plasticity in the vertebrate cortex. How the patterns of suprathreshold spiking activity in a population of neurons change because of this plasticity, however, has hardly been subjected to experimental studies. Here, we measured how evoked patterns of suprathreshold spiking activity in a cortical network were modified by cortical plasticity with single-cell and single-spike resolution. To record patterns of activity in the rodent barrel cortex, we used optical methods to detect suprathreshold activity from up to 40 neurons simultaneously. Pairing of two inputs resulted in a long-lasting modification of the cortical responses evoked by one of the inputs. The results indicate that plasticity rules on the network level inherit properties from synaptic plasticity rules but are also determined by the functional synaptic architecture, as well as the computations carried out in cortical networks. The largest determinants of the modified cortical responses were those observed when inducing changes by pairing the two inputs. On the single-neuron level, the modified responses only weakly reflected those observed when pairing the two inputs for induction of plasticity. Despite the weak reflection on the cellular level, however, the modified patterns reflected the pairing patterns to the degree that a simple decoding mechanism-a linear separator-correctly discriminated the modified responses from other patterns of activity.

Optical validation of in vitro extra-cellular neuronal recordings

Simultaneous calcium imaging and extra-cellular recordings from cultured cortical rat neurons were performed to directly map the efficacy of extra-cellular recordings with microelectrodes. For the first time, we can associate extra-cellular recordings with neuronal activity of specific neurons in the vicinity of the electrode. We demonstrate that recorded cells can be identified by correlating the electrical signals and the calcium response. Our data demonstrate that in sparse cultures, microelectrodes record exclusively from cells which reside at very close proximity to the recording electrode. Moreover, we show that recording appears to be limited to only a partial subset of the cells residing in this range. We further show that even in cases of strong neuron-electrode coupling, extra-cellular signals recorded from single, well-identified neurons vary in shape over time rendering spike sorting and network activity rate analysis incongruous. As multi-electrode array technology is becoming increasingly widespread, the visualization technique we report here will help users better understand the limits of this versatile and useful method.

Impact of cortical plasticity on information signaled by populations of neurons in the cerebral cortex

The performance of neural codes to represent attributes of sensory signals has been evaluated in the vertebrate peripheral and central nervous system. Here, we determine how information signaled by populations of neurons is modified by plasticity. Suprathreshold neuronal responses from a large number of neurons were recorded in the juvenile mouse barrel cortex using dithered random-access scanning. Pairing of one input with another resulted in a long-lasting, input-specific modification of the cortical responses. Mutual information analysis indicated that cortical plasticity efficiently changed information signaled by populations of neurons. The contribution of neural correlations to the change in mutual information was negative. The largest factor limiting fidelity of mutual information after pairing was a low reliability of the modified cortical responses.