Data Processing of Functional Optical Microscopy for Neuroscience

Benisty, H.; Song, Alexander; Mishne, Gal; Charles, Adam S.

doi:10.48550/arxiv.2201.03537

Cited by 2 publications

(3 citation statements)

References 102 publications

(165 reference statements)

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“…More user-friendly algorithms are also included in the more mainstream packages, such as Suite2p, CaImAn and EZcalcium [96][97][98] (Supplementary Table 1). In this section, we summarize the typical aspects of these steps, but refer the reader to other works for a more detailed perspective of these analyses [99][100][101] . We do not discuss downstream analyses of 2PCI data, such as the use of dimensionality reduction and clustering techniques (using, for example, tSNE and PCA) or the use of classifiers/decoders to correlate, for example, neural activity with behavioural measures 102 .…”

Section: Discussionmentioning

confidence: 99%

“…The goal of this step is to reduce the inherent noise associated with data sets that have a low signal-to-noise ratio or to reduce the size of large data sets, and it can be done before or, most typically, after motion correction. Techniques range from unsupervised linear and non-linear spatio-temporal filtering or down-sampling operations 96,99,109 to supervised approaches 110 . However, drawbacks include the fact that filtering/down-sampling operations can reduce the temporal or spatial resolution and, secondly, that linear or non-linear filtering operations might modify the underlying signal properties and render some of the post-processing steps less accurate.…”

Section: Motion Correction Motion Artefacts During In Vivo 2pcimentioning

confidence: 99%

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Two-photon calcium imaging of neuronal activity

Grienberger

Giovannucci

Zeiger

et al. 2022

Nat Rev Methods Primers

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other to generate movements, emotions or cognition is a central goal of neuroscience, requiring tools to record neuronal activity. For most of the twentieth century, electrophysiology was the only method available to neuroscientists to record neural activity. Although electro physiology remains the gold standard to accurately record action potentials and sub-threshold changes in membrane potential, it suffers from important shortcomings. Electrodes must be inserted into brain tissue, which causes trauma and, potentially, triggers irritability of neurons. Furthermore, the number of neurons that can be recorded simultaneously is limited, and their identity cannot be readily identified -neurons are almost always targeted blindly, and therefore there is a bias towards very active neurons. Recent advances in silicon probe technology, which now allow for multiple probes to be inserted into the brain (each with hundreds of contact sites), have expanded the number of neurons and brain regions that can be sampled with electrophysiology, but with greater invasiveness, higher cost and the same limitations compared with single-neuron electrode recordings.Optical probing of neural activity, especially calcium imaging, has become a popular alternative to electrophysiology. Recording neural activity with fluorescence probes -primarily calcium and voltage sensors -has become mainstream in neuroscience 1,2 . In particular, calcium imaging with two-photon (2P) microscopy has become the preferred method for recording neuronal ensemble dynamics in the intact brain over the past two decades 3,4 , because it offers several advantages over in vivo electrophysiology. Principles of calcium imagingThe ability to conduct calcium imaging experiments with rigour and reproducibility requires an in-depth knowledge of what signal is being measured and how. This requires, in turn, a basic understanding of the experimental techniques and equipment necessary to detect that signal, and of the computational pipelines to analyse and interpret the signal. In general terms, calcium imaging relies on the detection of changes in intracellular calcium concentration ([Ca 2+ ] i ) using fluorescent calcium indicators. Calcium contributes to a range of complex signals within neurons, from triggering neurotransmitter release to long-lasting synaptic plasticity in dendritic branches and spines. Importantly, the [Ca 2+ ] i regulates these processes over vastly different timescales, from a few microseconds to several hours. Thus, the time course, the amplitude or the site of its action in distinct cellular compartments are all essential determinants for the function of intraneuronal calcium signals. However, when discussing calcium imaging as a means of probing neural activity, which is the principal focus of this Primer, we are referring to Ca 2+ ions entering the cell body (the soma) through voltage-gated calcium channels when a neuron fires an action potential (FiG. 1a). Therefore, because the [Ca 2+ ] i in the soma correlates with action potential firing, so...

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Section: Discussionmentioning

confidence: 99%

Section: Motion Correction Motion Artefacts During In Vivo 2pcimentioning

confidence: 99%

Two-photon calcium imaging of neuronal activity

Grienberger

Giovannucci

Zeiger

et al. 2022

Nat Rev Methods Primers

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“…We chose in this study to primarily analyze the normalized fluorescence traces (Δ𝐹 /𝐹) rather than using deconvolution or spike inference methods (see [Pnevmatikakis, 2019;Stringer and Pachitariu, 2019;Evans, Petersen, and Humphries, 2020] for a review). Deconvolution methods were developed in part due to the slow temporal dynamics of the calcium indica-tors relative to membrane potentials generating spiking activity [Yaksi and Friedrich, 2006;Benisty et al, 2022]. Deconvolution and other spike inference techniques attempt to mitigate this limitation for analyses that depend on more exact measures of spike timing, and developers note these methods should be avoided when temporal information is not relevant and the raw calcium traces provide "sufficient information" [Stringer and Pachitariu, 2019].…”

Section: Discussionmentioning

confidence: 99%

Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

Weiss

Bounds

Adesnik

et al. 2022

Preprint

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Divisive normalization, a prominent model of interactions between neurons, plays a key role in influential theories of neural coding. Testing the mechanistic basis of those theories requires understanding the relationship between normalization and the statistics of neural activity, particularly noise correlations which provide strong constrains on circuit models. Yet the relation between normalization and correlations remains unclear, because models of covariability typically ignore normalization despite empirical evidence suggesting it contributes to stimulus and state modulations of covariability. We propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability, and accurately fits Ca2+ imaging data from mouse primary visual cortex (V1). We show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we outline the data regime in which the corresponding model parameter is identifiable. Our analysis provides the first quantitative evidence that similarly tuned neurons in mouse V1 share their normalization signals, and explains our observation that stronger normalization decorrelates those pairs, particularly at low stimulus contrast. Our model could enable quantifying the role of normalization on information transmission and representation, and mapping the parameters of normalization onto circuit and cellular mechanisms.

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Data Processing of Functional Optical Microscopy for Neuroscience

Cited by 2 publications

References 102 publications

Two-photon calcium imaging of neuronal activity

Two-photon calcium imaging of neuronal activity

Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

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