scholarly journals ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

2017 ◽  
Author(s):  
Stephanie Reynolds ◽  
Therese Abrahamsson ◽  
Renaud Schuck ◽  
P. Jesper Sjöström ◽  
Simon R. Schultz ◽  
...  
Keyword(s):  
Level Set ◽  
Calcium Imaging ◽  
Cell Shape ◽  
Active Contours ◽  
Real Data ◽  
Imaging Data ◽  
Similar Time ◽  
Two Photon

AbstractWe present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and ex-terior, in which all pixels have maximally ‘similar’ time courses. This simple, local model allows contours to be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cell’s morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE achieves a 67.5% success rate.Significance statementTwo-photon calcium imaging enables the study of brain activity during learning and behaviour at single-cell resolution. To decode neuronal spiking activity from the data, algorithms are first required to detect the location of cells in the video. It is still common for scientists to perform this task manually, as the heterogeneity in cell shape and frequency of cellular overlap impede automatic segmentation algorithms. We developed a versatile algorithm based on a popular image segmentation approach (the Level Set Method) and demonstrated its capability to overcome these challenges. We include no assumptions on cell shape or stereotypical temporal activity. This lends our framework the flexibility to be applied to new datasets with minimal adjustment.

scholarly journals ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

eNeuro ◽  
2017 ◽  
Vol 4 (5) ◽  
pp. ENEURO.0012-17.2017 ◽  
Author(s):  
Stephanie Reynolds ◽  
Therese Abrahamsson ◽  
Renaud Schuck ◽  
P. Jesper Sjöström ◽  
Simon R. Schultz ◽  
...  
Keyword(s):  
Level Set ◽  
Calcium Imaging ◽  
Segmentation Algorithm ◽  
Imaging Data ◽  
Two Photon ◽  
Level Set Segmentation

scholarly journals AN EXTENSION OF THE FRI FRAMEWORK FOR CALCIUM TRANSIENT DETECTION

2015 ◽  
Author(s):  
Stephanie Reynolds ◽  
Caroline S Copeland ◽  
Simon R Schultz ◽  
Pier Luigi Dragotti
Keyword(s):  
Calcium Imaging ◽  
Real Data ◽  
Calcium Transient ◽  
High Temporal Resolution ◽  
Imaging Data ◽  
Spike Detection ◽  
Model Order ◽  
Two Photon ◽  
Calcium Indicators ◽  
Finite Rate Of Innovation

Two-photon calcium imaging of the brain allows the spatiotemporal activity of neuronal networks to be monitored at cellular resolution. In order to analyse this activity it must first be possible to detect, with high temporal resolution, spikes from the time series corresponding to single neurons. Previous work has shown that finite rate of innovation (FRI) theory can be used to reconstruct spike trains from noisy calcium imaging data. In this paper we extend the FRI framework for spike detection from calcium imaging data to encompass data generated by a larger class of calcium indicators, including the genetically encoded indicator GCaMP6s. Furthermore, we implement least squares model-order estimation and perform a noise reduction procedure ('pre-whitening') in order to increase the robustness of the algorithm. We demonstrate high spike detection performance on real data generated by GCaMP6s, detecting 90% of electrophysiologically-validated spikes.

scholarly journals CaImAn an open source tool for scalable calcium imaging data analysis

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Andrea Giovannucci ◽  
Johannes Friedrich ◽  
Pat Gunn ◽  
Jérémie Kalfon ◽  
Brandon L Brown ◽  
...  
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Calcium Imaging ◽  
High Performance ◽  
Human Performance ◽  
Streaming Data ◽  
Imaging Data ◽  
Two Photon ◽  
Real Time Analysis

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

scholarly journals Active Learning of Cortical Connectivity from Two-Photon Imaging Data

2018 ◽  
Author(s):  
Martín Bertrán ◽  
Natalia Martínez ◽  
Ye Wang ◽  
David Dunson ◽  
Guillermo Sapiro ◽  
...  
Keyword(s):  
Active Learning ◽  
Small World ◽  
Real Data ◽  
Sensory Stimulation ◽  
Imaging Data ◽  
Population Activity ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging

AbstractUnderstanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network.. Unlike existing system identification techniques, this “active learning” method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

scholarly journals CalmAn: An open source tool for scalable Calcium Imaging data Analysis

2018 ◽  
Author(s):  
Andrea Giovannucci ◽  
Johannes Friedrich ◽  
Pat Gunn ◽  
Jérémie Kalfon ◽  
Sue Ann Koay ◽  
...  
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Calcium Imaging ◽  
High Performance ◽  
Human Performance ◽  
Ground Truth ◽  
Streaming Data ◽  
Imaging Data ◽  
Two Photon

AbstractAdvances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

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.

scholarly journals In Vivo Calcium Imaging of Circuit Activity in Cerebellar Cortex

2005 ◽  
Vol 94 (2) ◽  
pp. 1636-1644 ◽  
Author(s):  
Megan R. Sullivan ◽  
Axel Nimmerjahn ◽  
Dmitry V. Sarkisov ◽  
Fritjof Helmchen ◽  
Samuel S.-H. Wang
Keyword(s):  
Cerebellar Cortex ◽  
Calcium Imaging ◽  
Time Course ◽  
Molecular Layer ◽  
Neuronal Cell ◽  
Calcium Transients ◽  
Two Photon ◽  
Neuronal Cell Bodies

In vivo two-photon calcium imaging provides the opportunity to monitor activity in multiple components of neural circuitry at once. Here we report the use of bulk-loading of fluorescent calcium indicators to record from axons, dendrites, and neuronal cell bodies in cerebellar cortex in vivo. In cerebellar folium crus IIa of anesthetized rats, we imaged the labeled molecular layer and identified all major cellular structures: Purkinje cells, interneurons, parallel fibers, and Bergmann glia. Using extracellular stimuli we evoked calcium transients corresponding to parallel fiber beam activity. This beam activity triggered prolonged calcium transients in interneurons, consistent with in vitro evidence for synaptic activation of N-methyl-d-aspartate receptors via glutamate spillover. We also observed spontaneous calcium transients in Purkinje cell dendrites that were identified as climbing-fiber-evoked calcium spikes by their size, time course, and sensitivity to AMPA receptor antagonist. Two-photon calcium imaging of bulk-loaded cerebellar cortex is thus well suited to optically monitor synaptic processing in the intact cerebellum.

scholarly journals Functional Microarchitecture of the Mouse Dorsal Inferior Colliculus Revealed through In Vivo Two-Photon Calcium Imaging

2015 ◽  
Vol 35 (31) ◽  
pp. 10927-10939 ◽  
Author(s):  
O. Barnstedt ◽  
P. Keating ◽  
Y. Weissenberger ◽  
A. J. King ◽  
J. C. Dahmen
Keyword(s):  
Inferior Colliculus ◽  
Calcium Imaging ◽  
Two Photon

NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data

2017 ◽  
Vol 223 (1) ◽  
pp. 519-533 ◽  
Author(s):  
Jiangheng Guan ◽  
Jingcheng Li ◽  
Shanshan Liang ◽  
Ruijie Li ◽  
Xingyi Li ◽  
...  
Keyword(s):  
Cell Detection ◽  
Imaging Data ◽  
Two Photon

Two-Photon Processor and SeNeCA: a freely available software package to process data from two-photon calcium imaging at speeds down to several milliseconds per frame

2013 ◽  
Vol 110 (1) ◽  
pp. 243-256 ◽  
Author(s):  
Jakub Tomek ◽  
Ondrej Novak ◽  
Josef Syka
Keyword(s):  
Calcium Imaging ◽  
Software Package ◽  
Motion Artifacts ◽  
Calcium Signal ◽  
Neural Cells ◽  
Neuronal System ◽  
Process Data ◽  
Entire Area ◽  
Two Photon

Two-Photon Processor (TPP) is a versatile, ready-to-use, and freely available software package in MATLAB to process data from in vivo two-photon calcium imaging. TPP includes routines to search for cell bodies in full-frame (Search for Neural Cells Accelerated; SeNeCA) and line-scan acquisition, routines for calcium signal calculations, filtering, spike-mining, and routines to construct parametric fields. Searching for somata in artificial in vivo data, our algorithm achieved better performance than human annotators. SeNeCA copes well with uneven background brightness and in-plane motion artifacts, the major problems in simple segmentation methods. In the fast mode, artificial in vivo images with a resolution of 256 × 256 pixels containing ∼100 neurons can be processed at a rate up to 175 frames per second (tested on Intel i7, 8 threads, magnetic hard disk drive). This speed of a segmentation algorithm could bring new possibilities into the field of in vivo optophysiology. With such a short latency (down to 5–6 ms on an ordinary personal computer) and using some contemporary optogenetic tools, it will allow experiments in which a control program can continuously evaluate the occurrence of a particular spatial pattern of activity (a possible correlate of memory or cognition) and subsequently inhibit/stimulate the entire area of the circuit or inhibit/stimulate a different part of the neuronal system. TPP will be freely available on our public web site. Similar all-in-one and freely available software has not yet been published.

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