scholarly journals Restoration of Two-Photon Ca2+ Imaging Data Through Model Blind Spatiotemporal Filtering

2021 ◽  
Vol 15 ◽  
Author(s):  
Liyong Luo ◽  
Yuanxu Xu ◽  
Junxia Pan ◽  
Meng Wang ◽  
Jiangheng Guan ◽  
...  
Keyword(s):  
Relevant Information ◽  
Imaging Data ◽  
Spatiotemporal Filtering ◽  
Biologically Relevant ◽  
Two Photon ◽  
Current State ◽  
Data Restoration ◽  
Invaluable Tool ◽  
Subcellular Resolution

Two-photon Ca2+ imaging is a leading technique for recording neuronal activities in vivo with cellular or subcellular resolution. However, during experiments, the images often suffer from corruption due to complex noises. Therefore, the analysis of Ca2+ imaging data requires preprocessing steps, such as denoising, to extract biologically relevant information. We present an approach that facilitates imaging data restoration through image denoising performed by a neural network combining spatiotemporal filtering and model blind learning. Tests with synthetic and real two-photon Ca2+ imaging datasets demonstrate that the proposed approach enables efficient restoration of imaging data. In addition, we demonstrate that the proposed approach outperforms the current state-of-the-art methods by evaluating the qualities of the denoising performance of the models quantitatively. Therefore, our method provides an invaluable tool for denoising two-photon Ca2+ imaging data by model blind spatiotemporal processing.

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

scholarly journals Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging

2019 ◽  
Vol 35 (17) ◽  
pp. 3208-3210 ◽  
Author(s):  
Yangzhen Wang ◽  
Feng Su ◽  
Shanshan Wang ◽  
Chaojuan Yang ◽  
Yonglu Tian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection Methods ◽  
Supplementary Information ◽  
Imaging Data ◽  
Two Photon ◽  
Processing Power ◽  
Fluctuation Method ◽  
The Brain

Abstract Motivation Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters. Results We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users. Availability and implementation ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals All-optical interrogation of neural circuits in behaving mice

2021 ◽  
Author(s):  
Lloyd E. Russell ◽  
Henry W.P. Dalgleish ◽  
Rebecca Nutbrown ◽  
Oliver Gauld ◽  
Dustin Herrmann ◽  
...  
Keyword(s):  
Neural Circuits ◽  
Neural Circuit ◽  
Barrel Cortex ◽  
Brain Area ◽  
Imaging Data ◽  
Temporal Precision ◽  
Two Photon ◽  
All Optical ◽  
The Impact

Recent advances combining two-photon calcium imaging and two-photon optogenetics with digital holography now allow us to read and write neural activity in vivo at cellular resolution with millisecond temporal precision. Such 'all-optical' techniques enable experimenters to probe the impact of functionally defined neurons on neural circuit function and behavioural output with new levels of precision. This protocol describes the experimental strategy and workflow for successful completion of typical all-optical interrogation experiments in awake, behaving head-fixed mice. We describe modular procedures for the setup and calibration of an all-optical system, the preparation of an indicator and opsin-expressing and task-performing animal, the characterization of functional and photostimulation responses and the design and implementation of an all-optical experiment. We discuss optimizations for efficiently selecting and targeting neuronal ensembles for photostimulation sequences, as well as generating photostimulation response maps from the imaging data that can be used to examine the impact of photostimulation on the local circuit. We demonstrate the utility of this strategy using all-optical experiments in three different brain areas - barrel cortex, visual cortex and hippocampus - using different experimental setups. This approach can in principle be adapted to any brain area for all-optical interrogation experiments to probe functional connectivity in neural circuits and for investigating the relationship between neural circuit activity and behaviour.

scholarly journals Robotic automation of “blind” and two-photon targeted patch-clamp electrophysiology in vivo

2021 ◽  
Author(s):  
Gema Vera Gonzalez ◽  
Phatsimo Kgwarae ◽  
Luca Annecchino ◽  
Simon Schultz
Keyword(s):  
Patch Clamp ◽  
Review Paper ◽  
State Of The Art ◽  
Two Photon ◽  
Current State ◽  
Patch Clamp Electrophysiology ◽  
Robotic Automation

A review paper on the current state of the art in robotic automation of in vivo patch-clamp electrophysiology

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 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 Robotic automation of “blind” and two-photon targeted patch-clamp electrophysiology in vivo

2021 ◽  
Author(s):  
Gema Vera Gonzalez ◽  
Phatsimo Kgwarae ◽  
Luca Annecchino ◽  
Simon Schultz
Keyword(s):  
Patch Clamp ◽  
Review Paper ◽  
State Of The Art ◽  
Two Photon ◽  
Current State ◽  
Patch Clamp Electrophysiology ◽  
Robotic Automation

A review paper on the current state of the art in robotic automation of in vivo patch-clamp electrophysiology

scholarly journals 3D quantification of zebrafish cerebrovascular architecture by automated image analysis of light sheet fluorescence microscopy datasets

2020 ◽  
Author(s):  
E. C. Kugler ◽  
J. Frost ◽  
V. Silva ◽  
K. Plant ◽  
K. Chhabria ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fluorescence Microscopy ◽  
Light Sheet ◽  
Relevant Information ◽  
Automated Image Analysis ◽  
Analysis Pipeline ◽  
Biologically Relevant ◽  
Branching Points ◽  
Light Sheet Fluorescence Microscopy

AbstractZebrafish transgenic lines and light sheet fluorescence microscopy allow in-depth insights into vascular development in vivo and 3D. However, robust quantification of the zebrafish cerebral vasculature in 3D remains a challenge, and would be essential to describe the vascular architecture. Here, we report an image analysis pipeline that allows 3D quantification of the total or regional zebrafish brain vasculature. This is achieved by landmark- or object-based inter-sample registration and extraction of quantitative parameters including vascular volume, surface area, density, branching points, length, radius, and complexity. Application of our analysis pipeline to a range of sixteen genetic or pharmacological manipulations shows that our quantification approach is robust, allows extraction of biologically relevant information, and provides novel insights into vascular biology. To allow dissemination, the code for quantification, a graphical user interface, and workflow documentation are provided. Together, we present the first 3D quantification approach to assess the whole 3D cerebrovascular architecture in zebrafish.

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