Detecting neuronal activity from calcium imaging data using FRI methods (Conference Presentation)

2017 ◽  
Author(s):  
Stephanie Reynolds ◽  
Jon Oñativia ◽  
Simon R. Schultz ◽  
Pier Luigi Dragotti
Keyword(s):  
Neuronal Activity ◽  
Calcium Imaging ◽  
Imaging Data

scholarly journals Network reconstruction from calcium imaging data of spontaneously bursting neuronal activity

2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Olav Stetter ◽  
Javier Orlandi ◽  
Jordi Soriano ◽  
Demian Battaglia ◽  
Theo Geisel
Keyword(s):  
Neuronal Activity ◽  
Calcium Imaging ◽  
Network Reconstruction ◽  
Imaging Data

scholarly journals Online analysis of microendoscopic 1-photon calcium imaging data streams

2021 ◽  
Vol 17 (1) ◽  
pp. e1008565
Author(s):  
Johannes Friedrich ◽  
Andrea Giovannucci ◽  
Eftychios A. Pnevmatikakis
Keyword(s):  
Neuronal Activity ◽  
Calcium Imaging ◽  
Computing Time ◽  
Video Data ◽  
Streaming Data ◽  
Live Streaming ◽  
Imaging Data ◽  
Online Analysis ◽  
Real Time Processing

In vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm, OnACID-E, presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on four previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. They can be used instead of CNMF-E to process pre-recorded data with boosted speeds and dramatically reduced memory requirements. Further, they newly enable online analysis of live-streaming data even on a laptop.

scholarly journals A probabilistic framework for decoding behavior from in vivo calcium imaging data

2019 ◽  
Author(s):  
Guillaume Etter ◽  
Frederic Manseau ◽  
Sylvain Williams
Keyword(s):  
Probability Density ◽  
Neuronal Activity ◽  
Calcium Imaging ◽  
Probability Density Functions ◽  
Density Functions ◽  
Probabilistic Framework ◽  
Imaging Data ◽  
Simple Method

AbstractUnderstanding the role of neuronal activity in cognition and behavior is a key question in neuroscience. Previously, in vivo studies have typically inferred behavior from electrophysiological data using probabilistic approaches including Bayesian decoding. While providing useful information on the role of neuronal subcircuits, electrophysiological approaches are often limited in the maximum number of recorded neurons as well as their ability to reliably identify neurons over time. This can be particularly problematic when trying to decode behaviors that rely on large neuronal assemblies or rely on temporal mechanisms, such as a learning task over the course of several days. Calcium imaging of genetically encoded calcium indicators has overcome these two issues. Unfortunately, because calcium transients only indirectly reflect spiking activity and calcium imaging is often performed at lower sampling frequencies, this approach suffers from uncertainty in exact spike timing and thus activity frequency, making rate-based decoding approaches used in electrophysiological recordings difficult to apply to calcium imaging data. Here we describe a probabilistic framework that can be used to robustly infer behavior from calcium imaging recordings and relies on a simplified implementation of a naive Baysian classifier. Our method discriminates between periods of activity and periods of inactivity to compute probability density functions (likelihood and posterior), significance and confidence interval, as well as mutual information. We next devise a simple method to decode behavior using these probability density functions and propose metrics to quantify decoding accuracy. Finally, we show that neuronal activity can be predicted from behavior, and that the accuracy of such reconstructions can guide the understanding of relationships that may exist between behavioral states and neuronal activity.

scholarly journals A Probabilistic Framework for Decoding Behavior From in vivo Calcium Imaging Data

2020 ◽  
Vol 14 ◽  
Author(s):  
Guillaume Etter ◽  
Frederic Manseau ◽  
Sylvain Williams
Keyword(s):  
Probability Density ◽  
Neuronal Activity ◽  
Calcium Imaging ◽  
Probability Density Functions ◽  
Density Functions ◽  
Probabilistic Framework ◽  
Imaging Data ◽  
Simple Method

Understanding the role of neuronal activity in cognition and behavior is a key question in neuroscience. Previously, in vivo studies have typically inferred behavior from electrophysiological data using probabilistic approaches including Bayesian decoding. While providing useful information on the role of neuronal subcircuits, electrophysiological approaches are often limited in the maximum number of recorded neurons as well as their ability to reliably identify neurons over time. This can be particularly problematic when trying to decode behaviors that rely on large neuronal assemblies or rely on temporal mechanisms, such as a learning task over the course of several days. Calcium imaging of genetically encoded calcium indicators has overcome these two issues. Unfortunately, because calcium transients only indirectly reflect spiking activity and calcium imaging is often performed at lower sampling frequencies, this approach suffers from uncertainty in exact spike timing and thus activity frequency, making rate-based decoding approaches used in electrophysiological recordings difficult to apply to calcium imaging data. Here we describe a probabilistic framework that can be used to robustly infer behavior from calcium imaging recordings and relies on a simplified implementation of a naive Baysian classifier. Our method discriminates between periods of activity and periods of inactivity to compute probability density functions (likelihood and posterior), significance and confidence interval, as well as mutual information. We next devise a simple method to decode behavior using these probability density functions and propose metrics to quantify decoding accuracy. Finally, we show that neuronal activity can be predicted from behavior, and that the accuracy of such reconstructions can guide the understanding of relationships that may exist between behavioral states and neuronal activity.

scholarly journals DeepCINAC: a deep-learning-based Python toolbox for inferring calcium imaging neuronal activity based on movie visualization

2019 ◽  
Author(s):  
Julien Denis ◽  
Robin F. Dard ◽  
Eleonora Quiroli ◽  
Rosa Cossart ◽  
Michel A. Picardo
Keyword(s):  
Neuronal Activity ◽  
Calcium Imaging ◽  
Human Performance ◽  
Short Term Memory ◽  
Ground Truth ◽  
Imaging Data ◽  
Tuning Parameters ◽  
Pyramidal Layer ◽  
Inference Algorithms ◽  
Analytical Tools

AbstractTwo-photon calcium imaging is now widely used to infer neuronal dynamics from changes in fluorescence of an indicator. However, state of the art computational tools are not optimized for the reliable detection of fluorescence transients from highly synchronous neurons located in densely packed regions such as the CA1 pyramidal layer of the hippocampus during early postnatal stages of development. Indeed, the latest analytical tools often lack proper benchmark measurements. To meet this challenge, we first developed a graphical user interface allowing for a precise manual detection of all calcium transients from imaged neurons based on the visualization of the calcium imaging movie. Then, we analyzed the movies using a convolutional neural network with an attention process and a bidirectional long-short term memory network. This method is able to reach human performance and offers a better F1 score (harmonic mean of sensitivity and precision) than CaImAn to infer neural activity in the developing CA1 without any user intervention. It also enables automatically identifying activity originating from GABAergic neurons. Overall, DeepCINAC offers a simple, fast and flexible open-source toolbox for processing a wide variety of calcium imaging datasets while providing the tools to evaluate its performance.Significance statementInferring neuronal activity from calcium imaging data remains a challenge due to the difficulty in obtaining a ground truth using patch clamp recordings and the problem of finding optimal tuning parameters of inference algorithms. DeepCINAC offers a flexible, fast and easy-to-use toolbox to infer neuronal activity from any kind of calcium imaging dataset through visual inspection.

scholarly journals Fiber photometry does not reflect spiking activity in the striatum

2021 ◽  
Author(s):  
Alex A. Legaria ◽  
Julia A. Licholai ◽  
Alexxai V. Kravitz
Keyword(s):  
Neuronal Activity ◽  
Calcium Imaging ◽  
Brain Activity ◽  
Neural Population ◽  
Fluorescence Signal ◽  
Calcium Signals ◽  
Cellular Events ◽  
Neural Populations ◽  
Spiking Activity

AbstractFiber photometry recordings are commonly used as a proxy for neuronal activity, based on the assumption that increases in bulk calcium fluorescence reflect increases in spiking of the underlying neural population. However, this assumption has not been adequately tested. Here, using endoscopic calcium imaging in the striatum we report that the bulk fluorescence signal correlates weakly with somatic calcium signals, suggesting that this signal does not reflect spiking activity, but may instead reflect subthreshold changes in neuropil calcium. Consistent with this suggestion, the bulk fluorescence photometry signal correlated strongly with neuropil calcium signals extracted from these same endoscopic recordings. We further confirmed that photometry did not reflect striatal spiking activity with simultaneous in vivo extracellular electrophysiology and fiber photometry recordings in awake behaving mice. We conclude that the fiber photometry signal should not be considered a proxy for spiking activity in neural populations in the striatum.Significance statementFiber photometry is a technique for recording brain activity that has gained popularity in recent years due to it being an efficient and robust way to record the activity of genetically defined populations of neurons. However, it remains unclear what cellular events are reflected in the photometry signal. While it is often assumed that the photometry signal reflects changes in spiking of the underlying cell population, this has not been adequately tested. Here, we processed calcium imaging recordings to extract both somatic and non-somatic components of the imaging field, as well as a photometry signal from the whole field. Surprisingly, we found that the photometry signal correlated much more strongly with the non-somatic than the somatic signals. This suggests that the photometry signal most strongly reflects subthreshold changes in calcium, and not spiking. We confirmed this point with simultaneous fiber photometry and extracellular spiking recordings, again finding that photometry signals relate poorly to spiking in the striatum. Our results may change interpretations of studies that use fiber photometry as an index of spiking output of neural populations.

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.

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