scholarly journals A zero-inflated gamma model for post-deconvolved calcium imaging traces

2019 ◽  
Author(s):  
Xue-Xin Wei ◽  
Ding Zhou ◽  
Andres Grosmark ◽  
Zaki Ajabi ◽  
Fraser Sparks ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Motion Correction ◽  
Video Data ◽  
Imaging Analysis ◽  
Neural Encoding ◽  
Gamma Model ◽  
Neural Data ◽  
Processing Pipeline ◽  
Neural Populations ◽  
Bernoulli Models

AbstractCalcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing “pre-processing” tools applied to calcium video data, addressing the important issues of e.g., motion correction, denoising, compression, demixing, and deconvolution. However, computational modeling of deconvolved calcium signals (i.e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements. Surprisingly, these issues have to date received significantly less attention. To fill this gap, we examine the statistical properties of the deconvolved activity estimates, and propose several density models for these random signals. These models include a zero-inflated gamma (ZIG) model, which characterizes the calcium responses as a mixture of a gamma distribution and a point mass which serves to model zero responses. We apply the resulting models to neural encoding and decoding problems. We find that the ZIG model out-performs simpler models (e.g., Poisson or Bernoulli models) in the context of both simulated and real neural data, and can therefore play a useful role in bridging calcium imaging analysis methods with tools for analyzing activity in large neural populations.

scholarly journals Quantifying the signals contained in heterogeneous neural responses and determining their relationships with task performance

2014 ◽  
Vol 112 (6) ◽  
pp. 1584-1598 ◽  
Author(s):  
Marino Pagan ◽  
Nicole C. Rust
Keyword(s):  
Task Performance ◽  
Single Neuron ◽  
Weighted Sums ◽  
Neural Responses ◽  
Signal Modulation ◽  
Neural Data ◽  
Match To Sample ◽  
Neural Populations ◽  
Different Types ◽  
High Level

The responses of high-level neurons tend to be mixtures of many different types of signals. While this diversity is thought to allow for flexible neural processing, it presents a challenge for understanding how neural responses relate to task performance and to neural computation. To address these challenges, we have developed a new method to parse the responses of individual neurons into weighted sums of intuitive signal components. Our method computes the weights by projecting a neuron's responses onto a predefined orthonormal basis. Once determined, these weights can be combined into measures of signal modulation; however, in their raw form these signal modulation measures are biased by noise. Here we introduce and evaluate two methods for correcting this bias, and we report that an analytically derived approach produces performance that is robust and superior to a bootstrap procedure. Using neural data recorded from inferotemporal cortex and perirhinal cortex as monkeys performed a delayed-match-to-sample target search task, we demonstrate how the method can be used to quantify the amounts of task-relevant signals in heterogeneous neural populations. We also demonstrate how these intuitive quantifications of signal modulation can be related to single-neuron measures of task performance ( d′).

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 Precision Calcium Imaging of Dense Neural Populations via a Cell-Body-Targeted Calcium Indicator

Neuron ◽  
2020 ◽  
Vol 107 (3) ◽  
pp. 470-486.e11 ◽  
Author(s):  
Or A. Shemesh ◽  
Changyang Linghu ◽  
Kiryl D. Piatkevich ◽  
Daniel Goodwin ◽  
Orhan Tunc Celiker ◽  
...  
Keyword(s):  
Cell Body ◽  
Calcium Imaging ◽  
Calcium Indicator ◽  
Neural Populations ◽  
A Cell

scholarly journals Modeling the Correlated Activity of Neural Populations: A Review

2019 ◽  
Vol 31 (2) ◽  
pp. 233-269 ◽  
Author(s):  
Christophe Gardella ◽  
Olivier Marre ◽  
Thierry Mora
Keyword(s):  
Latent Variables ◽  
Collective Effects ◽  
Neural Function ◽  
Neural Encoding ◽  
Correlated Activity ◽  
Neural Populations ◽  
Large Populations ◽  
Single Spike ◽  
Highly Correlated ◽  
And Behavior

The principles of neural encoding and computations are inherently collective and usually involve large populations of interacting neurons with highly correlated activities. While theories of neural function have long recognized the importance of collective effects in populations of neurons, only in the past two decades has it become possible to record from many cells simultaneously using advanced experimental techniques with single-spike resolution and to relate these correlations to function and behavior. This review focuses on the modeling and inference approaches that have been recently developed to describe the correlated spiking activity of populations of neurons. We cover a variety of models describing correlations between pairs of neurons, as well as between larger groups, synchronous or delayed in time, with or without the explicit influence of the stimulus, and including or not latent variables. We discuss the advantages and drawbacks or each method, as well as the computational challenges related to their application to recordings of ever larger populations.

scholarly journals NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data

2017 ◽  
Author(s):  
Eftychios A. Pnevmatikakis ◽  
Andrea Giovannucci
Keyword(s):  
Calcium Imaging ◽  
Motion Correction ◽  
Rigid Motion ◽  
Motion Artifacts ◽  
Streaming Data ◽  
Field Of View ◽  
Large Field ◽  
Motion Field ◽  
Imaging Data ◽  
Two Photon

AbstractBackgroundMotion correction is a challenging pre-processing problem that arises early in the analysis pipeline of calcium imaging data sequences. The motion artifacts in two-photon microscopy recordings can be non-rigid, arising from the finite time of raster scanning and non-uniform deformations of the brain medium.New methodWe introduce an algorithm for fast Non-Rigid Motion Correction (NoRMCorre) based on template matching. NoRMCorre operates by splitting the field of view into overlapping spatial patches that are registered at a sub-pixel resolution for rigid translation against a continuously updated template. The estimated alignments are subsequently up-sampled to create a smooth motion field for each frame that can efficiently approximate non-rigid motion in a piecewise-rigid manner.Existing methodsExisting approaches either do not scale well in terms of computational performance or are targeted to motion artifacts arising from low speed scanning, whereas modern datasets with large field of view are more prone to non-rigid brain deformation issues.ResultsNoRMCorre can be run in an online mode resulting in comparable to or even faster than real time motion registration on streaming data. We evaluate the performance of the proposed method with simple yet intuitive metrics and compare against other non-rigid registration methods on two-photon calcium imaging datasets. Open source Matlab and Python code is also made available.ConclusionsThe proposed method and code provide valuable support to the community for solving large scale image registration problems in calcium imaging, especially when non-rigid deformations are present in the acquired data.

scholarly journals Mesmerize: A highly versatile platform for calcium imaging analysis and creation of self-contained FAIR datasets

2019 ◽  
Author(s):  
Kushal Kolar ◽  
Daniel Dondorp ◽  
Marios Chatzigeorgiou
Keyword(s):  
Calcium Imaging ◽  
Signal Extraction ◽  
Graphical Interface ◽  
Imaging Analysis ◽  
C Elegans ◽  
Analysis Process ◽  
Visual Cortex Neurons ◽  
Mouse Visual Cortex ◽  
Analysis Platform ◽  
Scientific Questions

AbstractWe present an efficient and expandable calcium imaging analysis platform that encapsulates the entire analysis process from raw data to interactive e-figures. It provides a graphical interface to the latest analysis methods for pre-processing, and signal extraction. We demonstrate how Mesmerize can be applied to a broad range of scientific questions by using datasets ranging from the mouse visual cortex, neurons, epidermis and TLCs of the protochordate C. intestinalis, and C. elegans.

scholarly journals moco: Fast Motion Correction for Calcium Imaging

2016 ◽  
Vol 10 ◽  
Author(s):  
Alexander Dubbs ◽  
James Guevara ◽  
Rafael Yuste
Keyword(s):  
Calcium Imaging ◽  
Motion Correction ◽  
Fast Motion

scholarly journals Transformation of a temporal speech cue to a spatial neural code in human auditory cortex

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Neal P Fox ◽  
Matthew Leonard ◽  
Matthias J Sjerps ◽  
Edward F Chang
Keyword(s):  
Auditory Cortex ◽  
Voice Onset Time ◽  
Onset Time ◽  
Neural Code ◽  
Neural Encoding ◽  
Spectral Cues ◽  
Temporal Cues ◽  
Neural Populations ◽  
Speech Cues ◽  
Simple Neural Network

In speech, listeners extract continuously-varying spectrotemporal cues from the acoustic signal to perceive discrete phonetic categories. Spectral cues are spatially encoded in the amplitude of responses in phonetically-tuned neural populations in auditory cortex. It remains unknown whether similar neurophysiological mechanisms encode temporal cues like voice-onset time (VOT), which distinguishes sounds like /b/ and/p/. We used direct brain recordings in humans to investigate the neural encoding of temporal speech cues with a VOT continuum from /ba/ to /pa/. We found that distinct neural populations respond preferentially to VOTs from one phonetic category, and are also sensitive to sub-phonetic VOT differences within a population’s preferred category. In a simple neural network model, simulated populations tuned to detect either temporal gaps or coincidences between spectral cues captured encoding patterns observed in real neural data. These results demonstrate that a spatial/amplitude neural code underlies the cortical representation of both spectral and temporal speech cues.

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.

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