scholarly journals Fast nonconvex deconvolution of calcium imaging data

Biostatistics ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 709-726 ◽  
Author(s):  
Sean W Jewell ◽  
Toby Dylan Hocking ◽  
Paul Fearnhead ◽  
Daniela M Witten
Keyword(s):  
Calcium Imaging ◽  
Scientific Paper ◽  
Imaging Data ◽  
Data Set ◽  
Data Resource ◽  
Allen Brain ◽  
Negative Spike ◽  
Large Populations ◽  
Deconvolution Problem ◽  
Exact Moment

Summary Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this article, we focus on a formulation recently proposed in Jewell and Witten (2018. Exact spike train inference via $\ell_{0} $ optimization. The Annals of Applied Statistics12(4), 2457–2482) that can accurately estimate not just the spike rate, but also the specific times at which the neuron spikes. We develop a much faster algorithm that can be used to deconvolve a fluorescence trace of 100 000 timesteps in less than a second. Furthermore, we present a modification to this algorithm that precludes the possibility of a “negative spike”. We demonstrate the performance of this algorithm for spike deconvolution on calcium imaging datasets that were recently released as part of the $\texttt{spikefinder}$ challenge (http://spikefinder.codeneuro.org/). The algorithm presented in this article was used in the Allen Institute for Brain Science’s “platform paper” to decode neural activity from the Allen Brain Observatory; this is the main scientific paper in which their data resource is presented. Our $\texttt{C++}$ implementation, along with $\texttt{R}$ and $\texttt{python}$ wrappers, is publicly available. $\texttt{R}$ code is available on $\texttt{CRAN}$ and $\texttt{Github}$, and $\texttt{python}$ wrappers are available on $\texttt{Github}$; see https://github.com/jewellsean/FastLZeroSpikeInference.

scholarly journals On the correspondence of electrical and optical physiology in in vivo population-scale two-photon calcium imaging

2019 ◽  
Author(s):  
Peter Ledochowitsch ◽  
Lawrence Huang ◽  
Ulf Knoblich ◽  
Michael Oliver ◽  
Jerome Lecoq ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Biophysical Model ◽  
Data Set ◽  
Two Photon ◽  
Simultaneous Imaging ◽  
Fluorescence Measurements ◽  
Large Populations ◽  
Intractable Problem ◽  
Mouse Lines

AbstractMultiphoton calcium imaging is commonly used to monitor the spiking of large populations of neurons. Recovering action potentials from fluorescence necessitates calibration experiments, often with simultaneous imaging and cell-attached recording. Here we performed calibration for imaging conditions matching those of the Allen Brain Observatory. We developed a novel crowd-sourced, algorithmic approach to quality control. Our final data set was 50 recordings from 35 neurons in 3 mouse lines. Our calibration indicated that 3 or more spikes were required to produce consistent changes in fluorescence. Moreover, neither a simple linear model nor a more complex biophysical model accurately predicted fluorescence for small numbers of spikes (1-3). We observed increases in fluorescence corresponding to prolonged depolarizations, particularly in Emx1-IRES-Cre mouse line crosses. Our results indicate that deriving spike times from fluorescence measurements may be an intractable problem in some mouse lines.

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 CaPTure: Calcium PeakToolbox for analysis of in vitro calcium imaging data

2021 ◽  
Author(s):  
Madhavi Tippani ◽  
Elizabeth A. Pattie ◽  
Brittany A. Davis ◽  
Claudia V. Nguyen ◽  
Yanhong Wang ◽  
...  
Keyword(s):  
High Throughput ◽  
Calcium Imaging ◽  
High Throughput Screening ◽  
Cultured Cells ◽  
Rapid Assessment ◽  
Imaging Data ◽  
User Input ◽  
Large Populations ◽  
Parameter Modification

ABSTRACTBackgroundCalcium imaging is a powerful technique for recording cellular activity across large populations of neurons. However, analysis methods capable of single-cell resolution in cultured neurons, especially for cultures derived from human induced pluripotent stem cells (hiPSCs), are lacking. Existing methods lack scalability to accommodate high-throughput comparisons between multiple lines, across developmental timepoints, or across pharmacological manipulations.ResultsWe developed a scalable, automated Ca2+ imaging analysis pipeline called CaPTure (https://github.com/LieberInstitute/CaPTure). This method detects neurons, classifies and quantifies spontaneous activity, quantifies synchrony metrics, and generates cell- and network-specific metrics that facilitate phenotypic discovery. The method is compatible with parallel processing on computing clusters without requiring significant user input or parameter modification.ConclusionCaPTure allows for rapid assessment of neuronal activity in cultured cells at cellular resolution, rendering it amenable to high-throughput screening and phenotypic discovery. The platform can be applied to both human- and rodent-derived neurons and is compatible with many imaging systems.

scholarly journals ESR white paper: blockchain and medical imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
◽  
Elmar Kotter ◽  
Luis Marti-Bonmati ◽  
Adrian P. Brady ◽  
Nandita M. Desouza
Keyword(s):  
Medical Imaging ◽  
Image Data ◽  
Distributed Database ◽  
Relevant Information ◽  
White Paper ◽  
Imaging Data ◽  
Data Set ◽  
Blockchain Technology ◽  
Potential Applications ◽  
Basic Technology

AbstractBlockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

Olfactory modulation of barrel cortex activity during active whisking and passive whisker stimulation

2021 ◽  
Author(s):  
Anthony Renard ◽  
Evan Harrell ◽  
Brice Bathallier
Keyword(s):  
Facial Nerve ◽  
Calcium Imaging ◽  
Tactile Stimulation ◽  
Barrel Cortex ◽  
Neuronal Responses ◽  
Population Activity ◽  
Tactile Information ◽  
Large Populations ◽  
Whisker Stimulation ◽  
The Impact

Abstract Rodents depend on olfaction and touch to meet many of their fundamental needs. The joint significance of these sensory systems is underscored by an intricate coupling between sniffing and whisking. However, the impact of simultaneous olfactory and tactile inputs on sensory representations in the cortex remains elusive. To study these interactions, we recorded large populations of barrel cortex neurons using 2-photon calcium imaging in head-fixed mice during olfactory and tactile stimulation. We find that odors alter barrel cortex activity in at least two ways, first by enhancing whisking, and second by central cross-talk that persists after whisking is abolished by facial nerve sectioning. Odors can either enhance or suppress barrel cortex neuronal responses, and while odor identity can be decoded from population activity, it does not interfere with the tactile representation. Thus, barrel cortex represents olfactory information which, in the absence of learned associations, is coded independently of tactile information.

Replication of Data Analyses

2017 ◽  
Author(s):  
Emery R. Boose ◽  
Barbara S. Lerner
Keyword(s):  
Original Work ◽  
Statistical Tests ◽  
Scientific Paper ◽  
Scientific Data ◽  
Data Sources ◽  
Narrative Form ◽  
Data Provenance ◽  
General Description ◽  
Data Set ◽  
Data Analyses

The metadata that describe how scientific data are created and analyzed are typically limited to a general description of data sources, software used, and statistical tests applied and are presented in narrative form in the methods section of a scientific paper or a data set description. Recognizing that such narratives are usually inadequate to support reproduction of the analysis of the original work, a growing number of journals now require that authors also publish their data. However, finer-scale metadata that describe exactly how individual items of data were created and transformed and the processes by which this was done are rarely provided, even though such metadata have great potential to improve data set reliability. This chapter focuses on the detailed process metadata, called “data provenance,” required to ensure reproducibility of analyses and reliable re-use of the data.

scholarly journals Inferring health conditions from fMRI-graph data

2018 ◽  
Author(s):  
PierGianLuca Porta Mana ◽  
Claudia Bachmann ◽  
Abigail Morrison
Keyword(s):  
Binary Classification ◽  
Disease Diagnosis ◽  
Health Condition ◽  
Automated Classification ◽  
Imaging Data ◽  
Data Set ◽  
Sufficient Statistics ◽  
Conjugate Priors ◽  
Healthy Control ◽  
Partial Exchangeability

Automated classification methods for disease diagnosis are currently in the limelight, especially for imaging data. Classification does not fully meet a clinician's needs, however: in order to combine the results of multiple tests and decide on a course of treatment, a clinician needs the likelihood of a given health condition rather than binary classification yielded by such methods. We illustrate how likelihoods can be derived step by step from first principles and approximations, and how they can be assessed and selected, using fMRI data from a publicly available data set containing schizophrenic and healthy control subjects, as a working example. We start from the basic assumption of partial exchangeability, and then the notion of sufficient statistics and the "method of translation" (Edgeworth, 1898) combined with conjugate priors. This method can be used to construct a likelihood that can be used to compare different data-reduction algorithms. Despite the simplifications and possibly unrealistic assumptions used to illustrate the method, we obtain classification results comparable to previous, more realistic studies about schizophrenia, whilst yielding likelihoods that can naturally be combined with the results of other diagnostic tests.

Advances in active acoustic ranging

2019 ◽  
Vol 38 (11) ◽  
pp. 843-849 ◽  
Author(s):  
Asbj⊘rn L. Johansen ◽  
William T. Allen ◽  
Roger Goobie ◽  
Nicholas Bennett ◽  
Benny Poedjono ◽  
...  
Keyword(s):  
Relative Position ◽  
Marked Improvement ◽  
Critical Parameter ◽  
Turnaround Time ◽  
Seismic Interpretation ◽  
Imaging Data ◽  
Data Set ◽  
Acoustic Ranging ◽  
Improve Accuracy ◽  
Logging Tool

Recent advances in the processing and interpretation of sonic imaging surveys warrant a fresh look at the performance of active acoustic ranging for locating wellbores. The interpretation of results from sonic imaging surveys typically has been done in workflows similar to classic seismic interpretation, where the data are projected into a 2D plane and reflective features are picked. These sonic imaging workflows require significant time and expertise to execute. The reflected arrival events typically are obscured by higher amplitude borehole modes, and the migration workflow needs numerous critical parameter choices that require interpreting the raypath type and azimuth of the reflected arrivals. When used for acoustic ranging, additional challenges are present, particularly in situations where the logging tool rotates and the relative position of the target well changes with depth. This may occur when the logging or target well trajectories have a curved shape, since determining the direction and distance to the target well then requires careful interpretation of migration image amplitudes. We demonstrate how a newly developed automated approach to the interpretation of sonic imaging data helps improve accuracy and removes interpreter bias while simplifying the processing chain and reducing turnaround time. We compare our results to what has been obtained previously by using the same data set. We achieve a marked improvement in accuracy and consistency using this new technique.

scholarly journals Hunting for brown dwarfs in the globular cluster M4: second epoch HST NIR observations

2019 ◽  
Vol 486 (2) ◽  
pp. 2254-2264 ◽  
Author(s):  
A Dieball ◽  
L R Bedin ◽  
C Knigge ◽  
M Geffert ◽  
R M Rich ◽  
...  
Keyword(s):  
Globular Cluster ◽  
White Dwarf ◽  
Near Infrared ◽  
Magnitude Estimate ◽  
Cluster Member ◽  
Imaging Data ◽  
Brown Dwarf ◽  
Data Set ◽  
Bona Fide ◽  
Low Mass

ABSTRACT We present an analysis of the second epoch Hubble Space TelescopeWide Field Camera 3 F110W near-infrared (NIR) imaging data of the globular cluster M 4. The new data set suggests that one of the previously suggested four brown dwarf candidates in this cluster is indeed a high-probability cluster member. The position of this object in the NIR colour–magnitude diagrams (CMDs) is in the white dwarf/brown dwarf area. The source is too faint to be a low-mass main-sequence (MS) star, but, according to theoretical considerations, also most likely somewhat too bright to be a bona-fide brown dwarf. Since we know that the source is a cluster member, we determined a new optical magnitude estimate at the position the source should have in the optical image. This new estimate places the source closer to the white dwarf sequence in the optical–NIR CMD and suggests that it might be a very cool (Teff ≤ 4500 K) white dwarf at the bottom of the white dwarf cooling sequence in M 4, or a white dwarf/brown dwarf binary. We cannot entirely exclude the possibility that the source is a very massive, bright brown dwarf, or a very low-mass MS star, however, we conclude that we still have not convincingly detected a brown dwarf in a globular cluster, but we expect to be very close to the start of the brown dwarf cooling sequence in this cluster. We also note that the MS ends at F110W ≈ 22.5 mag in the proper-motion cleaned CMDs, where completeness is still high.

scholarly journals Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity

2020 ◽  
Vol 33 (5) ◽  
pp. 393-399
Author(s):  
Rong Chen ◽  
Kyunghun Lee ◽  
Edward H Herskovits
Keyword(s):  
Diffusion Tensor ◽  
Inferior Frontal Gyrus ◽  
Anterior Cingulate ◽  
Impulsive Behavior ◽  
Cingulate Gyrus ◽  
Imaging Data ◽  
Anatomical Connectivity ◽  
Computational Framework ◽  
Data Set ◽  
Superior Frontal Gyrus

Many brain disorders – such as Alzheimer’s disease, Parkinson’s disease, schizophrenia and autism – are heterogeneous, that is, they may have several subtypes. Traditionally, clinicians have identified subtypes, such as subtypes of psychosis, using clinical criteria. Neuroimaging has the potential to detect subtypes based on objective biomarker-based criteria; however, there are no studies that evaluate the application of combining unsupervised machine learning and anatomical connectivity analysis to accomplish this goal. We propose a computational framework to detect subtypes based on anatomical connectivity computed from diffusion tensor imaging data, in a data-driven and fully automated way. The proposed method exhibits excellent performance on simulated data. We also applied this approach to a real-world dataset: the Nathan Kline Institute data set. The Nathan Kline Institute study consists of 137 normal adult subjects (mean age 41 years (standard deviation 18), male/female 85/52). We examined the association between detected subtypes and the impulsive behavior scale. We found that a subtype characterized by lower connectivity scores was associated with a higher positive urgency score; positive urgency is a vulnerability marker for drug addiction. The top-ranked connections characterizing subtypes involve several brain regions, including the anterior cingulate gyrus, median cingulate gyrus, thalamus, superior frontal gyrus (medial), middle frontal gyrus (orbital part), inferior frontal gyrus (triangular part), superior frontal gyrus, precuneus and putamen. The proposed framework is extendable, and can be used to detect subtypes from other features, including clinical and genomic biomarkers.

Sign in / Sign up

Export Citation Format

Share Document