scholarly journals OnACID: Online Analysis of Calcium Imaging Data in Real Time*

2017 ◽  
Author(s):  
Andrea Giovannucci ◽  
Johannes Friedrich ◽  
Matt Kaufman ◽  
Anne Churchland ◽  
Dmitri Chklovskii ◽  
...  
Keyword(s):  
Real Time ◽  
Calcium Imaging ◽  
Large Scale ◽  
Motion Artifact ◽  
Imaging Data ◽  
Neuronal Populations ◽  
Calcium Indicators ◽  
Online Dictionary Learning ◽  
Recorded Data

AbstractOptical imaging methods using calcium indicators are critical for monitoring the activity of large neuronal populations in vivo. Imaging experiments typically generate a large amount of data that needs to be processed to extract the activity of the imaged neuronal sources. While deriving such processing algorithms is an active area of research, most existing methods require the processing of large amounts of data at a time, rendering them vulnerable to the volume of the recorded data, and preventing realtime experimental interrogation. Here we introduce OnACID, an Online framework for the Analysis of streaming Calcium Imaging Data, including i) motion artifact correction, ii) neuronal source extraction, and iii) activity denoising and deconvolution. Our approach combines and extends previous work on online dictionary learning and calcium imaging data analysis, to deliver an automated pipeline that can discover and track the activity of hundreds of cells in real time, thereby enabling new types of closed-loop experiments. We apply our algorithm on two large scale experimental datasets, benchmark its performance on manually annotated data, and show that it outperforms a popular offline approach.

scholarly journals Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning

2019 ◽  
Vol 116 (17) ◽  
pp. 8554-8563 ◽  
Author(s):  
Somayyeh Soltanian-Zadeh ◽  
Kaan Sahingur ◽  
Sarah Blau ◽  
Yiyang Gong ◽  
Sina Farsiu
Keyword(s):  
Real Time ◽  
Calcium Imaging ◽  
Large Scale ◽  
Cortical Layer ◽  
Fast Method ◽  
Active Neuron ◽  
Neuronal Coding ◽  
Two Photon ◽  
Neuron Density

Calcium imaging records large-scale neuronal activity with cellular resolution in vivo. Automated, fast, and reliable active neuron segmentation is a critical step in the analysis workflow of utilizing neuronal signals in real-time behavioral studies for discovery of neuronal coding properties. Here, to exploit the full spatiotemporal information in two-photon calcium imaging movies, we propose a 3D convolutional neural network to identify and segment active neurons. By utilizing a variety of two-photon microscopy datasets, we show that our method outperforms state-of-the-art techniques and is on a par with manual segmentation. Furthermore, we demonstrate that the network trained on data recorded at a specific cortical layer can be used to accurately segment active neurons from another layer with different neuron density. Finally, our work documents significant tabulation flaws in one of the most cited and active online scientific challenges in neuron segmentation. As our computationally fast method is an invaluable tool for a large spectrum of real-time optogenetic experiments, we have made our open-source software and carefully annotated dataset freely available online.

scholarly journals V-NeuroStack: 3D Time Stacks for Identifying Patterns in Calcium Imaging Data

2020 ◽  
Author(s):  
Ashwini G. Naik ◽  
Robert V. Kenyon ◽  
Aynaz Taheri ◽  
Tanya Berger-Wolf ◽  
Baher Ibrahim ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Cortical Neurons ◽  
Large Scale ◽  
Line Graph ◽  
Imaging Data ◽  
Web Interface ◽  
Activity Data ◽  
Spatio Temporal

AbstractBackgroundUnderstanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of cortical neurons in a mouse brain slice.New MethodV-NeuroStack creates 3D time stacks by stacking 2D time frames for a period of 600 seconds. It provides a web interface that enables exploration and analysis of data using a combination of 3D and 2D visualization techniques.Comparison with existing MethodsPrevious attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack can scale data sets with at least a few thousand temporal snapshots.ResultsV-NeuroStack’s 3D view is used to explore patterns in the dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of a single neuron or a functional cluster of neurons.ConclusionsV-NeuroStack enables easy exploration and analysis of large spatio-temporal datasets using two visualization paradigms: (a) Space-Time cube (b)Two-dimensional networks, via web interface. It will support future advancements in in vitro and in vivo data capturing techniques and can bring forth novel hypotheses by permitting unambiguous visualization of large-scale patterns in the neuronal activity data.

A Novel Real-Time Thermal Analysis and Layer Time Control Framework for Large Scale Additive Manufacturing

2020 ◽  
Author(s):  
Sepehr Fathizadan ◽  
Feng Ju ◽  
Kyle Rowe ◽  
Alex Fiechter ◽  
Nils Hofmann
Keyword(s):  
Additive Manufacturing ◽  
Surface Temperature ◽  
Real Time ◽  
Large Scale ◽  
Production Efficiency ◽  
Control Method ◽  
Data Extraction ◽  
Critical Role ◽  
Imaging Data ◽  
Time Data

Abstract Production efficiency and product quality need to be addressed simultaneously to ensure the reliability of large scale additive manufacturing. Specifically, print surface temperature plays a critical role in determining the quality characteristics of the product. Moreover, heat transfer via conduction as a result of spatial correlation between locations on the surface of large and complex geometries necessitates the employment of more robust methodologies to extract and monitor the data. In this paper, we propose a framework for real-time data extraction from thermal images as well as a novel method for controlling layer time during the printing process. A FLIR™ thermal camera captures and stores the stream of images from the print surface temperature while the Thermwood Large Scale Additive Manufacturing (LSAM™) machine is printing components. A set of digital image processing tasks were performed to extract the thermal data. Separate regression models based on real-time thermal imaging data are built on each location on the surface to predict the associated temperatures. Subsequently, a control method is proposed to find the best time for printing the next layer given the predictions. Finally, several scenarios based on the cooling dynamics of surface structure were defined and analyzed, and the results were compared to the current fixed layer time policy. It was concluded that the proposed method can significantly increase the efficiency by reducing the overall printing time while preserving the quality.

scholarly journals Automated cellular structure extraction in biological images with applications to calcium imaging data

2018 ◽  
Author(s):  
Gal Mishne ◽  
Ronald R. Coifman ◽  
Maria Lavzin ◽  
Jackie Schiller
Keyword(s):  
Calcium Imaging ◽  
Image Plane ◽  
Cellular Level ◽  
Full Potential ◽  
Imaging Data ◽  
Image Domain ◽  
Biological Images ◽  
Large Populations ◽  
In Vivo Calcium Imaging

AbstractRecent advances in experimental methods in neuroscience enable measuring in-vivo activity of large populations of neurons at cellular level resolution. To leverage the full potential of these complex datasets and analyze the dynamics of individual neurons, it is essential to extract high-resolution regions of interest, while addressing demixing of overlapping spatial components and denoising of the temporal signal of each neuron. In this paper, we propose a data-driven solution to these challenges, by representing the spatiotemporal volume as a graph in the image plane. Based on the spectral embedding of this graph calculated across trials, we propose a new clustering method, Local Selective Spectral Clustering, capable of handling overlapping clusters and disregarding clutter. We also present a new nonlinear mapping which recovers the structural map of the neurons and dendrites, and global video denoising. We demonstrate our approach on in-vivo calcium imaging of neurons and apical dendrites, automatically extracting complex structures in the image domain, and denoising and demixing their time-traces.

scholarly journals Fast Nonnegative Deconvolution for Spike Train Inference From Population Calcium Imaging

2010 ◽  
Vol 104 (6) ◽  
pp. 3691-3704 ◽  
Author(s):  
Joshua T. Vogelstein ◽  
Adam M. Packer ◽  
Timothy A. Machado ◽  
Tanya Sippy ◽  
Baktash Babadi ◽  
...  
Keyword(s):  
Spike Train ◽  
Calcium Imaging ◽  
Interior Point Method ◽  
Linear Time ◽  
Wiener Filter ◽  
Biological Data ◽  
Neuronal Populations ◽  
Calcium Indicators ◽  
Optimal Linear ◽  
Performance Gains

Fluorescent calcium indicators are becoming increasingly popular as a means for observing the spiking activity of large neuronal populations. Unfortunately, extracting the spike train of each neuron from a raw fluorescence movie is a nontrivial problem. This work presents a fast nonnegative deconvolution filter to infer the approximately most likely spike train of each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm runs in linear time, and is fast enough that even when simultaneously imaging >100 neurons, inference can be performed on the set of all observed traces faster than real time. Performing optimal spatial filtering on the images further refines the inferred spike train estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.

In Vivo Calcium Imaging of Neural Network Function

Physiology ◽  
2007 ◽  
Vol 22 (6) ◽  
pp. 358-365 ◽  
Author(s):  
Werner Göbel ◽  
Fritjof Helmchen
Keyword(s):  
Calcium Imaging ◽  
Activity Patterns ◽  
Network Activity ◽  
Network Function ◽  
Two Photon ◽  
Calcium Indicators ◽  
Recent Developments ◽  
In Vivo Calcium Imaging ◽  
Function Network

Spatiotemporal activity patterns in local neural networks are fundamental to brain function. Network activity can now be measured in vivo using two-photon imaging of cell populations that are labeled with fluorescent calcium indicators. In this review, we discuss basic aspects of in vivo calcium imaging and highlight recent developments that will help to uncover operating principles of neural circuits.

scholarly journals Inference of Synaptic Connectivity and External Variability in Neural Microcircuits

2019 ◽  
Author(s):  
Cody Baker ◽  
Emmanouil Froudarakis ◽  
Dimitri Yatsenko ◽  
Andreas S. Tolias ◽  
Robert Rosenbaum
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Network Models ◽  
Ground Truth ◽  
External Input ◽  
Synaptic Connectivity ◽  
Imaging Data ◽  
Common Input ◽  
Simplifying Assumptions

AbstractA major goal in neuroscience is to estimate neural connectivity from large scale extracellular recordings of neural activity in vivo. This is challenging in part because any such activity is modulated by the unmeasured external synaptic input to the network, known as the common input problem. Many different measures of functional connectivity have been proposed in the literature, but their direct relationship to synaptic connectivity is often assumed or ignored. For in vivo data, measurements of this relationship would require a knowledge of ground truth connectivity, which is nearly always unavailable. Instead, many studies use in silico simulations as benchmarks for investigation, but such approaches necessarily rely upon a variety of simplifying assumptions about the simulated network and can depend on numerous simulation parameters. We combine neuronal network simulations, mathematical analysis, and calcium imaging data to address the question of when and how functional connectivity, synaptic connectivity, and latent external input variability can be untangled. We show numerically and analytically that, even though the precision matrix of recorded spiking activity does not uniquely determine synaptic connectivity, it is often closely related to synaptic connectivity in practice under various network models. This relation becomes more pronounced when the spatial structure of neuronal variability is considered jointly with precision.

scholarly journals Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0

2019 ◽  
Vol 16 (10) ◽  
pp. 1054-1062 ◽  
Author(s):  
Venkatakaushik Voleti ◽  
Kripa B. Patel ◽  
Wenze Li ◽  
Citlali Perez Campos ◽  
Srinidhi Bharadwaj ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale
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