Calcium Imaging Signals Research Articles

Automated denoising software for calcium imaging signals using deep learning

Dynamic Ca2+ signaling is crucial for cell survival and death, and Ca2+ imaging approaches are commonly used to study and measure cellular Ca2+ patterns within cells. However, the presence of image noise from instrumentation and experimentation protocols can impede the accurate extraction of Ca2+ signals. Removing noise from Ca2+ Spatio-Temporal Maps (STMaps) is essential for precisely analyzing Ca2+ datasets. Current methods for denoising STMaps can be time-consuming and subjective and rely mainly on image processing protocols. To address this, we developed CalDenoise, an automated software that employs robust image processing and deep learning models to remove noise and enhance Ca2+ signals in STMaps effectively. CalDenoise integrates four pipelines capable of efficiently removing salt-and-pepper, impulsive, and periodic noise and detecting and removing background noise. Comprising both an image-processing-based pipeline and three generative-adversarial-network-based (GAN) deep learning models, CalDenoise proficiently removes complex noise patterns. The software features adjustable parameters to enhance accuracy and is integrated into a user-friendly graphical interface for easy access and streamlined usage.CalDenoise can serve as a robust platform for denoising complex dynamic fluorescence signal images across diverse cell types, including Ca2+, voltage, ions, and pH signals.

A machine-learning tool to identify bistable states from calcium imaging data.

Mapping neuronal activation using calcium imaging in vivo during behavioural tasks has advanced our understanding of nervous system function. In almost all of these studies, calcium imaging is used to infer spike probabilities because action potentials activate voltage-gated calcium channels and increase intracellular calcium levels. However, neurons not only fire action potentials, but also convey information via intrinsic dynamics such as by generating bistable membrane potential states. Although a number of tools for spike inference have been developed and are currently being used, no tool exists for converting calcium imaging signals to maps of cellular state in bistable neurons. Purkinje neurons in the larval zebrafish cerebellum exhibit membrane potential bistability, firing either tonically or in bursts. Several studies have implicated the role of a population code in cerebellar function, with bistability adding an extra layer of complexity to this code. In the present study, we develop a tool, CaMLSort, which uses convolutional recurrent neural networks to classify calcium imaging traces as arising from either tonic or bursting cells. We validate this classifier using a number of different methods and find that it performs well on simulated event rasters as well as real biological data that it had not previously seen. Moreover, we find that CaMLsort generalizes to other bistable neurons, such as dopaminergic neurons in the ventral tegmental area of mice. Thus, this tool offers a new way of analysing calcium imaging data from bistable neurons to understand how they participate in network computation and natural behaviours. KEY POINTS: Calcium imaging, compriising the gold standard of inferring neuronal activity, does not report cellular state in neurons that are bistable, such as Purkinje neurons in the cerebellum of larval zebrafish. We model the relationship between Purkinje neuron electrical activity and its corresponding calcium signal to compile a dataset of state-labelled simulated calcium signals. We apply machine-learning methods to this dataset to develop a tool that can classify the state of a Purkinje neuron using only its calcium signal, which works well on real data even though it was trained only on simulated data. CaMLsort (Calcium imaging and Machine Learning based tool to sort intracellular state) also generalizes well to bistable neurons in a different brain region (ventral tegmental area) in a different model organism (mouse). This tool can facilitate our understanding of how these neurons carry out their functions in a circuit.

Representing the dynamics of high-dimensional data with non-redundant wavelets

SummaryA crucial question in data science is to extract meaningful information embedded in high-dimensional data into a low-dimensional set of features that can represent the original data at different levels. Wavelet analysis is a pervasive method for decomposing time-series signals into a few levels with detailed temporal resolution. However, obtained wavelets are intertwined and over-represented across levels for each sample and across different samples within one population. Here, using neuroscience data of simulated spikes, experimental spikes, calcium imaging signals, and human electrocorticography signals, we leveraged conditional mutual information between wavelets for feature selection. The meaningfulness of selected features was verified to decode stimulus or condition with high accuracy yet using only a small set of features. These results provide a new way of wavelet analysis for extracting essential features of the dynamics of spatiotemporal neural data, which then enables to support novel model design of machine learning with representative features.

LEARNING COMPACT DNN MODELS FOR BEHAVIOR PREDICTION FROM NEURAL ACTIVITY OF CALCIUM IMAGING.

In this paper, we develop methods for efficient and accurate information extraction from calcium-imaging-based neural signals. The particular form of information extraction we investigate involves predicting behavior variables linked to animals from which the calcium imaging signals are acquired. More specifically, we develop algorithms to systematically generate compact deep neural network (DNN) models for accurate and efficient calcium-imaging-based predictive modeling. We also develop a software tool, called NeuroGRS, to apply the proposed methods for compact DNN derivation with a high degree of automation. GRS stands for Greedy inter-layer order with Random Selection of intra-layer units, which describes the central algorithm developed in this work for deriving compact DNN structures. Through extensive experiments using NeuroGRS and calcium imaging data, we demonstrate that our methods enable highly streamlined information extraction from calcium images of the brain with minimal loss in accuracy compared to much more computationally expensive approaches.

Suppression of Inflammation Delays Hair Cell Regeneration and Functional Recovery Following Lateral Line Damage in Zebrafish Larvae.

Cochlear hair cells in human beings cannot regenerate after loss; however, those in fish and other lower species can. Recently, the role of inflammation in hair cell regeneration has been attracting the attention of scientists. In the present study, we investigated how suppression of inflammatory factors affects hair cell regeneration and the functional recovery of regenerated hair cells in zebrafish. We killed hair cells in the lateral line of zebrafish larvae with CuSO4 to induce an inflammatory response and coapplied BRS-28, an anti-inflammatory agent to suppress the inflammation. The recovery of the hair cell number and rheotaxis was slower when CuSO4 and BRS-28 were coapplied than when CuSO4 was applied alone. The recovery of hair cell count lagged behind that of the calcium imaging signal during the regeneration. The calcium imaging signal in the neuromasts in the inflammation-inhibited group was weaker than that in the noninflammation-inhibited group at the early stage of regeneration, although it returned to normal at the late stage. Our study demonstrates that suppressing inflammation by BRS-28 delays hair cell regeneration and functional recovery when hair cells are damaged. We suspect that BRS-28 inhibits pro-inflammatory factors and thereby reduces the migration of macrophages to delay the regeneration of hair cells.

Dynamic causal modeling for calcium imaging: Exploration of differential effective connectivity for sensory processing in a barrel cortical column

Multi-photon calcium imaging (CaI) is an important tool to assess activities of neural populations within a column in the sensory cortex. However, the complex asymmetrical interactions among neural populations, termed effective connectivity, cannot be directly assessed by measuring the activity of each neuron or neural population using CaI but calls for computational modeling. To estimate effective connectivity among neural populations, we proposed a dynamic causal model (DCM) for CaI by combining a convolution-based dynamic neural state model and a dynamic calcium ion concentration model for CaI signals. After conducting a simulation study to evaluate DCM for CaI, we applied it to an experimental CaI signals measured at the layer 2/3 of a barrel cortical column that differentially responds to hit and error whisking trials in mice. We first identified neural populations and constructed computational models with intrinsic connectivity of neural populations within the layer 2/3 of the barrel cortex and extrinsic connectivity with latent external modes. Bayesian model inversion and comparison shows that interactions with latent inhibitory and excitatory external modes explain the observed CaI signals within the barrel cortical column better than any other tested models, with a single external mode or without any latent modes. The best model also showed differential intrinsic and extrinsic effective connectivity between hit and error trials in the functional hierarchy. Both simulation and experimental results suggest the usefulness of DCM for CaI in terms of exploration of hierarchical interactions among neural populations observed in CaI.

Decoding with Calcium Signals from Layer 2/3 Motor Cortex during A Pressing Movement.

Brain-machine interfaces (BMIs) have been promising for not only neuroprosthesis research but also brain function investigation. Electrophysiological recording commonly used in traditional BMIs is spatially sparse and lack of information about neuron types and spatial organization. However, optical imaging methods might avoid these limitations by providing dense, spatially organized and annotated with genetic information over a large field of view. Here, we tried to demonstrate the potential of calcium imaging signals obtained through the one-photon microscope in neural decoding. When mice were trained to perform a lever press task to obtain water as rewards, the calcium signals of neurons in their layer 2/3 motor cortex were recorded by microscope. With the calcium signals, we analyzed the neural activity at both single individual neuron and neuronal population level. We found two typical classes of pressing-related neurons and distinct ensemble activity patterns between a pressing movement and baseline. The decoding results further demonstrated that the movement-related information could be more completely specified by population response structure. Our results suggested that neural signals from more types and a larger amount of neurons, are crucial for accurate decoding in BMI applications.

Prediction of Forelimb Reach Results From Motor Cortex Activities Based on Calcium Imaging and Deep Learning.

Brain-wide activities revealed by neuroimaging and recording techniques have been used to predict motor and cognitive functions in both human and animal models. However, although studies have shown the existence of micrometer-scale spatial organization of neurons in the motor cortex relevant to motor control, two-photon microscopy (TPM) calcium imaging at cellular resolution has not been fully exploited for the same purpose. Here, we ask if calcium imaging data recorded by TPM in rodent brain can provide enough information to predict features of upcoming movement. We collected calcium imaging signal from rostral forelimb area in layer 2/3 of the motor cortex while mice performed a two-dimensional lever reaching task. Images of average calcium activity collected during motion preparation period and inter-trial interval (ITI) were used to predict the forelimb reach results. The evaluation was based on a deep learning model that had been applied for object recognition. We found that the prediction accuracy for both maximum reaching location and trial outcome based on motion preparation period but not ITI were higher than the probabilities governed by chance. Our study demonstrated that imaging data encompassing information on the spatial organization of functional neuronal clusters in the motor cortex is useful in predicting motor acts even in the absence of detailed dynamics of neural activities.

Imaging and analysis of genetically encoded calcium indicators linking neural circuits and behaviors.

Confirming the direct link between neural circuit activity and animal behavior has been a principal aim of neuroscience. The genetically encoded calcium indicator (GECI), which binds to calcium ions and emits fluorescence visualizing intracellular calcium concentration, enables detection of in vivo neuronal firing activity. Various GECIs have been developed and can be chosen for diverse purposes. These GECI-based signals can be acquired by several tools including two-photon microscopy and microendoscopy for precise or wide imaging at cellular to synaptic levels. In addition, the images from GECI signals can be analyzed with open source codes including constrained non-negative matrix factorization for endoscopy data (CNMF_E) and miniscope 1-photon-based calcium imaging signal extraction pipeline (MIN1PIPE), and considering parameters of the imaged brain regions (e.g., diameter or shape of soma or the resolution of recorded images), the real-time activity of each cell can be acquired and linked with animal behaviors. As a result, GECI signal analysis can be a powerful tool for revealing the functions of neuronal circuits related to specific behaviors.

MIN1PIPE: A Miniscope 1-Photon-Based Calcium Imaging Signal Extraction Pipeline

Invivo calcium imaging using a 1-photon-based miniscope and a microendoscopic lens enables studies of neural activities in freely behaving animals. However, the high and fluctuating background, the inevitable movements and distortions of imaging field, and the extensive spatial overlaps of fluorescent signals emitted from imaged neurons inherent in this 1-photon imaging method present major challenges for extracting neuronal signals reliably and automatically from the raw imaging data. Here, we develop a unifying algorithm called the miniscope 1-photon imaging pipeline (MIN1PIPE), which contains several stand-alone modules and can handle a wide range of imaging conditions and qualities with minimal parameter tuning and automatically and accurately isolate spatially localized neural signals. We have quantitatively compared MIN1PIPE with other existing partial methods using both synthetic and real datasets obtained from different animal models and show that MIN1PIPE has superior efficiency and precision in analyzing noisy miniscope calcium imaging data.

Abstract 20919: Manipulation-Free Particle Image Velocimetry Method of Screening Drugs Using Human iPSC-Derived Cardiomyocytes

Introduction: Currently available drug screening models are not efficient in detecting cardiac toxicity. Here we demonstrate the in vitro Particle Image Velocimetry (PIV) assays followed by fourier spectrum and heatmap analyses for measuring the impact of different cardiac drugs on the spontaneous beat frequency (SBF) and spontaneous beat amplitude (SBA) of beating cardiomyocytes (CMCs). Hypothesis: Our PIV method of measuring CMCs contractility could be beneficial for drug testing and preclinical screening to detect cardio-toxicity. Methods and Results: The novelty of our system is that we can record the spatiotemporal maturation of human iPSC-derived CMCs at specific positions. We have evaluated the contractility of CMCs with the response to various chemical compounds such as Verapamil (Ca2+ blocker), Isoproterenol (Ca2+ activator), E-4031 (K+ inhibitor) and NS-1643 (K+ activator). Our optically recorded beat patterns were in good agreement with the calcium-imaging signal ( Fig 1 ). Our data also show that Isoproterenol, the β adrenergic agonist influencing intracellular Ca2+ dynamics, induced a substantial and sustained increase in SBF without triggering arrhythmias (Fig 2A, B) . Fourier analysis clearly indicates a shift in the SBF (Fig. 2C , dotted line). Power spectra of consecutive recordings are shown as heat maps ( Fig. 2D ). The reversibility of the Isoproterenol effect is indicated by the SBF returning to the baseline value after drug removal (Fig 2B, E ). Conclusions: We report for the first time the applications of PIV assays for prospective cardiac drug efficacy screening by measuring the contractility of CMCs from the video image without jeopardizing the biology of cells.

An overview of Bayesian methods for neural spike train analysis.

Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.

State-dependent network reconstruction from calcium imaging signals

Calcium imaging has become a standard technique for the measurement of the activity of a population of cultured neurons. Typically these recordings are slow compared to the cell dynamics and display a low signal-to-noise ratio, but they allow for the simultaneous recording of hundreds of neurons. We are interested in reconstructing an approximation of the structural connectivity of a culture of neurons. This would allow for characterization of the bulk properties of these networks, such as the dependence of connection probability of two nodes on the distance between them, the degree distribution or the clustering coefficient, which are currently inaccessible with single-cell or even typical multi-electrode techniques. In order to benchmark our connectivity inference methods, we first study simulations of fluorescence signals and examine established methods of inferring the topology. It turns out that we can improve on these methods if we turn to measures from information theory, which do not rely on a linearity assumption. Because we are interested in directed networks, our measure of choice is Transfer Entropy [1,2]. It turns out that we can achieve a high quality of the reconstruction if we allow for novel extensions of this measure. Specifically, we need to take into account the ability of the network to display different dynamical states (fig. ​(fig.1).1). We need to focus on phases of activity where the dynamics in the network are dominated by direct monosynaptic interactions, and where therefore the effective connectivity corresponds closely to the structural connectivity. Additionally, we need to correct for the slow acquisition rate of the recording by allowing for instantaneous interactions between nodes in addition to interactions from different image frames. Figure 1 The averaged fluorescence signal of the nodes in our network (A) reveals the existence of quiet phases and network spikes, which is then represented in the histogram over time (B). The effective connectivity calculated from the data points when the averaged ... We demonstrate post-processing improvements of the reconstruction using the Data Processing Inequality that are only possible in the case of information theoretical measures. These methods, already applied with success in the reconstruction of gene regulatory networks [3], help to discriminate indirect from direct interactions. We then apply our algorithm to real data from large cultures of hippocampal neurons in vitro stained with Fluo-4 AM dye. We probe and quantify the distance-dependent probability of connection and other topological properties of the reconstructed network, finding deviations from a random topology. Finally we point out and quantify which experimental parameters would be most relevant for an improved reconstruction using our method.

Network reconstruction from calcium imaging signals

Network reconstruction from calcium imaging signals

Quantitative estimation of Ca2+ concentrations with simple state space model of calcium imaging signals

Quantitative estimation of Ca2+ concentrations with simple state space model of calcium imaging signals

Mapping receptive fields on mouse primary visual cortex: reverse correlation using two-photon calcium imaging signals

Mapping receptive fields on mouse primary visual cortex: reverse correlation using two-photon calcium imaging signals

Analysis of Calcium Imaging Signals from the Honeybee Brain by Nonlinear Models

Recent Ca2+-imaging studies on the antennal lobe of the honeybee (Apis mellifera) have shown that olfactory stimuli evoke complex spatiotemporal changes of the intracellular Ca2+ concentration, in which stimulus-dependent subsets of glomeruli are highlighted. In this work we use nonlinear models for the quantitative identification of the spatial and temporal properties of the Ca2+-dependent fluorescence signal. This technique describes time series of the Ca2+ signal as a superposition of biophysically motivated model functions for photobleaching and Ca2+ dynamics and provides optimal estimates of their amplitudes (signal strengths) and time constants together with error measures. Using this method, we can reliably identify two different stimulus-dependent signal components. Their delays and rise times, δc1 = (0.4 ± 0.3) s, τc1 = (3.8 ± 1.2) s for the fast component and δc2 = (2.4 ± 0.6) s, τc2 = (10.3 ± 3.2) s for the slow component, are constant over space and across different odors and animals. In chronological experiments, the amplitude of the fast (slow) component often decreases (increases) with time. The pattern of the Ca2+ dynamics in space and time can be reliably described as a superposition of only two spatiotemporally separable patterns based on the fast and slow components. However, the distributions of both components over space turn out to differ from each other, and more work has to be done in order to specify their relationship with neuronal activity.