scholarly journals MIN1PIPE: A Miniscope 1-photon-based Calcium Imaging Signal Extraction Pipeline

2018 ◽  
Author(s):  
Jinghao Lu ◽  
Chunyuan Li ◽  
Jonnathan Singh-Alvarado ◽  
Zhe Charles Zhou ◽  
Flavio Fröhlich ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Parameter Tuning ◽  
Superior Performance ◽  
Imaging Method ◽  
Imaging Data ◽  
Neural Signals ◽  
Neural Activities ◽  
Wide Range ◽  
Photon Imaging

SUMMARYIn vivo calcium imaging using 1-photon based miniscope and 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 MINiscope 1-photon imaging PIPEline (MIN1PIPE) that contains several standalone 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 quantitatively compare MIN1PIPE with other existing partial methods using both synthetic and real datasets obtained from different animal models, and show that MIN1PIPE has a superior performance both in terms of efficiency and precision in analyzing noisy miniscope calcium imaging data.

scholarly journals Tracking calcium dynamics from individual neurons in behaving animals

2021 ◽  
Vol 17 (10) ◽  
pp. e1009432
Author(s):  
Thibault Lagache ◽  
Alison Hanson ◽  
Jesús E. Pérez-Ortega ◽  
Adrienne Fairhall ◽  
Rafael Yuste
Keyword(s):  
Visual Cortex ◽  
Calcium Imaging ◽  
Functional Characterization ◽  
Ground Truth ◽  
Time Lapse ◽  
Local Deformation ◽  
Neuron Activity ◽  
Imaging Data ◽  
Wide Range

Measuring the activity of neuronal populations with calcium imaging can capture emergent functional properties of neuronal circuits with single cell resolution. However, the motion of freely behaving animals, together with the intermittent detectability of calcium sensors, can hinder automatic monitoring of neuronal activity and their subsequent functional characterization. We report the development and open-source implementation of a multi-step cellular tracking algorithm (Elastic Motion Correction and Concatenation or EMC2) that compensates for the intermittent disappearance of moving neurons by integrating local deformation information from detectable neurons. We demonstrate the accuracy and versatility of our algorithm using calcium imaging data from two-photon volumetric microscopy in visual cortex of awake mice, and from confocal microscopy in behaving Hydra, which experiences major body deformation during its contractions. We quantify the performance of our algorithm using ground truth manual tracking of neurons, along with synthetic time-lapse sequences, covering a wide range of particle motions and detectability parameters. As a demonstration of the utility of the algorithm, we monitor for several days calcium activity of the same neurons in layer 2/3 of mouse visual cortex in vivo, finding significant turnover within the active neurons across days, with only few neurons that remained active across days. Also, combining automatic tracking of single neuron activity with statistical clustering, we characterize and map neuronal ensembles in behaving Hydra, finding three major non-overlapping ensembles of neurons (CB, RP1 and RP2) whose activity correlates with contractions and elongations. Our results show that the EMC2 algorithm can be used as a robust and versatile platform for neuronal tracking in behaving animals.

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 Ultra-fast accurate reconstruction of spiking activity from calcium imaging data

2018 ◽  
Vol 119 (5) ◽  
pp. 1863-1878 ◽  
Author(s):  
Vahid Rahmati ◽  
Knut Kirmse ◽  
Knut Holthoff ◽  
Stefan J. Kiebel
Keyword(s):  
Calcium Imaging ◽  
Reconstruction Method ◽  
Imaging Data ◽  
Decay Kinetics ◽  
Experimental Conditions ◽  
Model Free ◽  
Reconstruction Methods ◽  
Wide Range ◽  
Fast Processing ◽  
Spiking Activity

Calcium imaging provides an indirect observation of the underlying neural dynamics and enables the functional analysis of neuronal populations. However, the recorded fluorescence traces are temporally smeared, thus making the reconstruction of exact spiking activity challenging. Most of the established methods to tackle this issue are limited in dealing with issues such as the variability in the kinetics of fluorescence transients, fast processing of long-term data, high firing rates, and measurement noise. We propose a novel, heuristic reconstruction method to overcome these limitations. By using both synthetic and experimental data, we demonstrate the four main features of this method: 1) it accurately reconstructs both isolated spikes and within-burst spikes, and the spike count per fluorescence transient, from a given noisy fluorescence trace; 2) it performs the reconstruction of a trace extracted from 1,000,000 frames in less than 2 s; 3) it adapts to transients with different rise and decay kinetics or amplitudes, both within and across single neurons; and 4) it has only one key parameter, which we will show can be set in a nearly automatic way to an approximately optimal value. Furthermore, we demonstrate the ability of the method to effectively correct for fast and rather complex, slowly varying drifts as frequently observed in in vivo data. NEW & NOTEWORTHY Reconstruction of spiking activities from calcium imaging data remains challenging. Most of the established reconstruction methods not only have limitations in adapting to systematic variations in the data and fast processing of large amounts of data, but their results also depend on the user’s experience. To overcome these limitations, we present a novel, heuristic model-free-type method that enables an ultra-fast, accurate, near-automatic reconstruction from data recorded under a wide range of experimental conditions.

scholarly journals Image-based multi-scale modelling and validation of radio-frequency ablation in liver tumours

2011 ◽  
Vol 369 (1954) ◽  
pp. 4233-4254 ◽  
Author(s):  
Stephen Payne ◽  
Ronan Flanagan ◽  
Mika Pollari ◽  
Tuomas Alhonnoro ◽  
Claire Bost ◽  
...  
Keyword(s):  
Heat Treatment ◽  
Radio Frequency ◽  
Radio Frequency Ablation ◽  
High Intensity Focused Ultrasound ◽  
Imaging Data ◽  
Clinical Implementation ◽  
Liver Tumours ◽  
Advantages And Disadvantages ◽  
Wide Range

The treatment of cancerous tumours in the liver remains clinically challenging, despite the wide range of treatment possibilities, including radio-frequency ablation (RFA), high-intensity focused ultrasound and resection, which are currently available. Each has its own advantages and disadvantages. For non- or minimally invasive modalities, such as RFA, considered here, it is difficult to monitor the treatment in vivo . This is particularly problematic in the liver, where large blood vessels act as heat sinks, dissipating delivered heat and shrinking the size of the lesion (the volume damaged by the heat treatment) locally; considerable experience is needed on the part of the clinician to optimize the heat treatment to prevent recurrence. In this paper, we outline our work towards developing a simulation tool kit that could be used both to optimize treatment protocols in advance and to train the less-experienced clinicians for RFA treatment of liver tumours. This tool is based on a comprehensive mathematical model of bio-heat transfer and cell death. We show how simulations of ablations in two pigs, based on individualized imaging data, compare directly with experimentally measured lesion sizes and discuss the likely sources of error and routes towards clinical implementation. This is the first time that such a ‘loop’ of mathematical modelling and experimental validation in vivo has been performed in this context, and such validation enables us to make quantitative estimates of error.

scholarly journals μECoG Recordings Through a Thinned Skull

2019 ◽  
Author(s):  
Sarah K. Brodnick ◽  
Jared P. Ness ◽  
Thomas J. Richner ◽  
Sanitta Thongpang ◽  
Joseph Novello ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Neural Signals ◽  
Electrophysiological Recordings ◽  
Neuroscience Research ◽  
Optically Transparent ◽  
Chronic Study ◽  
Multiple Species ◽  
First Time ◽  
Stimulation Of

AbstractThe studies described in this paper for the first time characterize the acute and chronic performance of optically transparent thin-film µECoG grids implanted on a thinned skull as both an electrophysiological complement to existing thinned skull preparation for optical recordings/manipulations, and a less invasive alternative to epidural or subdurally placed µECoG arrays. In a longitudinal chronic study, µECoG grids placed on top of a thinned skull maintain impedances comparable to epidurally placed µECoG grids that are stable for periods of at least one month. Optogenetic activation of cortex is also reliably demonstrated through the optically transparent ECoG grids acutely placed on the thinned skull. Finally, spatially distinct electrophysiological recordings were evident on µECoG electrodes placed on a thinned skull separated by 500-750µm, as assessed by stimulation evoked responses using optogenetic activation of cortex as well as invasive and epidermal stimulation of the sciatic and median nerve at chronic time points. Neural signals were collected through a thinned skull in multiple species, demonstrating potential utility in neuroscience research applications such as in vivo imaging, optogenetics, calcium imaging, and neurovascular coupling.

scholarly journals Improvement of the Similarity Spectral Unmixing Approach for Multiplexed Two-Photon Imaging by Linear Dimension Reduction of the Mixing Matrix

2021 ◽  
Vol 22 (11) ◽  
pp. 6046
Author(s):  
Asylkhan Rakhymzhan ◽  
Andreas Acs ◽  
Anja E. Hauser ◽  
Thomas H. Winkler ◽  
Raluca A. Niesner
Keyword(s):  
Cellular Tissue ◽  
Spectral Unmixing ◽  
Superior Performance ◽  
Cellular Interactions ◽  
Two Photon ◽  
Mixing Matrix ◽  
Photon Imaging ◽  
Two Photon Imaging ◽  
Tissue Compartments

Two-photon microscopy enables monitoring cellular dynamics and communication in complex systems, within a genuine environment, such as living tissues and, even, living organisms. Particularly, its application to understand cellular interactions in the immune system has brought unique insights into pathophysiologic processes in vivo. Simultaneous multiplexed imaging is required to understand the dynamic orchestration of the multiple cellular and non-cellular tissue compartments defining immune responses. Here, we present an improvement of our previously developed method, which allowed us to achieve multiplexed dynamic intravital two-photon imaging, by using a synergistic strategy. This strategy combines a spectrally broad range of fluorophore emissions, a wave-mixing concept for simultaneous excitation of all targeted fluorophores, and an unmixing algorithm based on the calculation of spectral similarities with previously measured fluorophore fingerprints. The improvement of the similarity spectral unmixing algorithm here described is based on dimensionality reduction of the mixing matrix. We demonstrate its superior performance in the correct pixel-based assignment of probes to tissue compartments labeled by single fluorophores with similar spectral fingerprints, as compared to the full-dimensional similarity spectral unmixing approach.

scholarly journals Microendoscopic calcium imaging of the primary visual cortex of behaving macaques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mineki Oguchi ◽  
Jiang Jiasen ◽  
Toshihide W. Yoshioka ◽  
Yasuhiro R. Tanaka ◽  
Kenichi Inoue ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Calcium Imaging ◽  
Large Population ◽  
Fluorescent Microscopy ◽  
Orientation Tuning ◽  
Brain Hemispheres ◽  
Neural Activities ◽  
In Vivo Calcium Imaging

AbstractIn vivo calcium imaging with genetically encoded indicators has recently been applied to macaque brains to monitor neural activities from a large population of cells simultaneously. Microendoscopic calcium imaging combined with implantable gradient index lenses captures neural activities from deep brain areas with a compact and convenient setup; however, this has been limited to rodents and marmosets. Here, we developed miniature fluorescent microscopy to image neural activities from the primary visual cortex of behaving macaques. We found tens of clear fluorescent signals from three of the six brain hemispheres. A subset of these neurons showed clear retinotopy and orientation tuning. Moreover, we successfully decoded the stimulus orientation and tracked the cells across days. These results indicate that microendoscopic calcium imaging is feasible and reasonable for investigating neural circuits in the macaque brain by monitoring fluorescent signals from a large number of neurons.

scholarly journals Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

2018 ◽  
Author(s):  
E. Kelly Buchanan ◽  
Ian Kinsella ◽  
Ding Zhou ◽  
Rong Zhu ◽  
Pengcheng Zhou ◽  
...  
Keyword(s):  
Functional Imaging ◽  
Calcium Imaging ◽  
Matrix Decomposition ◽  
Video Data ◽  
Reproducible Research ◽  
Imaging Data ◽  
Voltage Imaging ◽  
Low Snr ◽  
Wide Range ◽  
Low Dimensional

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large (with scales of TB/hour in some cases), which has presented a barrier to routine open sharing of this data, slowing progress in reproducible research. State of the art methods for analyzing this data are based on non-negative matrix factorization (NMF); these approaches solve a non-convex optimization problem, and are highly effective when good initializations are available, but can break down e.g. in low-SNR settings where common initialization approaches fail. Here we introduce an improved approach to compressing and denoising functional imaging data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the video data.

scholarly journals Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline

2009 ◽  
Vol 15 (2) ◽  
pp. 103-117 ◽  
Author(s):  
S.K. Lahiri ◽  
K.C. Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Evolution ◽  
Parameter Tuning ◽  
Operating Conditions ◽  
Superior Performance ◽  
Slurry Flow ◽  
Wide Range ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

This paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters.

scholarly journals Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

2017 ◽  
Author(s):  
Philipp Berens ◽  
Jeremy Freeman ◽  
Thomas Deneux ◽  
Nicolay Chenkov ◽  
Thomas McColgan ◽  
...  
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Indirect Measurement ◽  
Generative Models ◽  
Imaging Data ◽  
Crowd Sourcing ◽  
Two Photon ◽  
Wide Range ◽  
Improved Performance ◽  
Highly Correlated

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

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