Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

DeepAction: a MATLAB toolbox for automated classification of animal behavior in video.

ABSTRACT: The identification of animal behavior in video is a critical but time-consuming task in many areas of research. Here, we introduce DeepAction, a deep learning-based toolbox for automatically annotating animal behavior in video. Our approach uses features extracted from raw video frames by a pretrained convolutional neural network to train a recurrent neural network classifier. We evaluate the classifier on two benchmark rodent datasets and one octopus dataset. We show that it achieves high accuracy, requires little training data, and surpasses both human agreement and most comparable existing methods. We also create a confidence score for classifier output, and show that our method provides an accurate estimate of classifier performance and reduces the time required by human annotators to review and correct automatically-produced annotations. We release our system and accompanying annotation interface as an open-source MATLAB toolbox.

SUBMITTER: Harris C

PROVIDER: S-EPMC9932075 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

DeepAction: a MATLAB toolbox for automated classification of animal behavior in video.

Harris Carl C Finn Kelly R KR Kieseler Marie-Luise ML Maechler Marvin R MR Tse Peter U PU

Scientific reports 20230215 1

The identification of animal behavior in video is a critical but time-consuming task in many areas of research. Here, we introduce DeepAction, a deep learning-based toolbox for automatically annotating animal behavior in video. Our approach uses features extracted from raw video frames by a pretrained convolutional neural network to train a recurrent neural network classifier. We evaluate the classifier on two benchmark rodent datasets and one octopus dataset. We show that it achieves high accur ...[more]

PMID: 36792716

Similar Datasets

DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data.

Project description:Detailed behavioral analysis is key to understanding the brain-behavior relationship. Here, we present deep learning-based methods for analysis of behavior imaging data in mice and humans. Specifically, we use three different convolutional neural network architectures and five different behavior tasks in mice and humans and provide detailed instructions for rapid implementation of these methods for the neuroscience community. We provide examples of three dimensional (3D) kinematic analysis in the food pellet reaching task in mice, three-chamber test in mice, social interaction test in freely moving mice with simultaneous miniscope calcium imaging, and 3D kinematic analysis of two upper extremity movements in humans (reaching and alternating pronation/supination). We demonstrate that the transfer learning approach accelerates the training of the network when using images from these types of behavior video recordings. We also provide code for post-processing of the data after initial analysis with deep learning. Our methods expand the repertoire of available tools using deep learning for behavior analysis by providing detailed instructions on implementation, applications in several behavior tests, and post-processing methods and annotated code for detailed behavior analysis. Moreover, our methods in human motor behavior can be used in the clinic to assess motor function during recovery after an injury such as stroke.

| S-EPMC6513883 | biostudies-literature

A Matlab-based toolbox for characterizing behavior of rodents engaged in string-pulling.

Project description:String-pulling by rodents is a behavior in which animals make rhythmical body, head, and bilateral forearm as well as skilled hand movements to spontaneously reel in a string. Typical analysis includes kinematic assessment of hand movements done by manually annotating frames. Here, we describe a Matlab-based software that allows whole-body motion characterization using optical flow estimation, descriptive statistics, principal component, and independent component analyses as well as temporal measures of Fano factor, entropy, and Higuchi fractal dimension. Based on image-segmentation and heuristic algorithms for object tracking, the software also allows tracking of body, ears, nose, and forehands for estimation of kinematic parameters such as body length, body angle, head roll, head yaw, head pitch, and path and speed of hand movements. The utility of the task and software is demonstrated by characterizing postural and hand kinematic differences in string-pulling behavior of two strains of mice, C57BL/6 and Swiss Webster.

| S-EPMC7347385 | biostudies-literature

Automated high throughput animal CO1 metabarcode classification.

Project description:We introduce a method for assigning names to CO1 metabarcode sequences with confidence scores in a rapid, high-throughput manner. We compiled nearly 1 million CO1 barcode sequences appropriate for classifying arthropods and chordates. Compared to our previous Insecta classifier, the current classifier has more than three times the taxonomic coverage, including outgroups, and is based on almost five times as many reference sequences. Unlike other popular rDNA metabarcoding markers, we show that classification performance is similar across the length of the CO1 barcoding region. We show that the RDP classifier can make taxonomic assignments about 19 times faster than the popular top BLAST hit method and reduce the false positive rate from nearly 100% to 34%. This is especially important in large-scale biodiversity and biomonitoring studies where datasets can become very large and the taxonomic assignment problem is not trivial. We also show that reference databases are becoming more representative of current species diversity but that gaps still exist. We suggest that it would benefit the field as a whole if all investigators involved in metabarocoding studies, through collaborations with taxonomic experts, also planned to barcode representatives of their local biota as a part of their projects.

| S-EPMC5844909 | biostudies-literature

Corrigendum: PSYCHOACOUSTICS: a comprehensive MATLAB toolbox for auditory testing.

Project description:[This corrects the article on p. 712 in vol. 5, PMID: 25101013.].

| S-EPMC5500961 | biostudies-other

A covariance NMR toolbox for MATLAB and OCTAVE.

Project description:The Covariance NMR Toolbox is a new software suite that provides a streamlined implementation of covariance-based analysis of multi-dimensional NMR data. The Covariance NMR Toolbox uses the MATLAB or, alternatively, the freely available GNU OCTAVE computer language, providing a user-friendly environment in which to apply and explore covariance techniques. Covariance methods implemented in the toolbox described here include direct and indirect covariance processing, 4D covariance, generalized indirect covariance (GIC), and Z-matrix transform. In order to provide compatibility with a wide variety of spectrometer and spectral analysis platforms, the Covariance NMR Toolbox uses the NMRPipe format for both input and output files. Additionally, datasets small enough to fit in memory are stored as arrays that can be displayed and further manipulated in a versatile manner within MATLAB or OCTAVE.

| S-EPMC3042881 | biostudies-literature

PSYCHOACOUSTICS: a comprehensive MATLAB toolbox for auditory testing.

Project description:PSYCHOACOUSTICS is a new MATLAB toolbox which implements three classic adaptive procedures for auditory threshold estimation. The first includes those of the Staircase family (method of limits, simple up-down and transformed up-down); the second is the Parameter Estimation by Sequential Testing (PEST); and the third is the Maximum Likelihood Procedure (MLP). The toolbox comes with more than twenty built-in experiments each provided with the recommended (default) parameters. However, if desired, these parameters can be modified through an intuitive and user friendly graphical interface and stored for future use (no programming skills are required). Finally, PSYCHOACOUSTICS is very flexible as it comes with several signal generators and can be easily extended for any experiment.

| S-EPMC4104800 | biostudies-literature

SBEToolbox: A Matlab Toolbox for Biological Network Analysis.

Project description:We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases.

| S-EPMC3767578 | biostudies-literature

A MATLAB toolbox for multivariate analysis of brain networks.

Project description:Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.

| S-EPMC6289822 | biostudies-literature

DynamicBC: a MATLAB toolbox for dynamic brain connectome analysis.

Project description:The brain connectome collects the complex network architectures, looking at both static and dynamic functional connectivity. The former normally requires stationary signals and connections. However, the human brain activity and connections are most likely time dependent and dynamic, and related to ongoing rhythmic activity. We developed an open-source MATLAB toolbox DynamicBC with user-friendly graphical user interfaces, implementing both dynamic functional and effective connectivity for tracking brain dynamics from functional MRI. We provided two strategies for dynamic analysis: (1) the commonly utilized sliding-window analysis and (2) the flexible least squares based time-varying parameter regression strategy. The toolbox also implements multiple functional measures including seed-to-voxel analysis, region of interest (ROI)-to-ROI analysis, and voxel-to-voxel analysis. We describe the principles of the implemented algorithms, and then present representative results from simulations and empirical data applications. We believe that this toolbox will help neuroscientists and neurologists to easily map dynamic brain connectomics.

| S-EPMC4268585 | biostudies-literature

A MATLAB toolbox for muscle diffusion-tensor MRI tractography.

Project description:Diffusion-tensor MRI fiber tractography has been used to reconstruct skeletal muscle architecture, but remains a specialized technique using custom-written data processing routines. In this work, we describe the public release of a software toolbox having the following design objectives: accomplish the pre-processing tasks of file input, image registration, denoising, and diffusion-tensor calculation; allow muscle-specific methods for defining seed points; make fiber-tract architectural measurements referenced to tendinous structures; visualize fiber tracts and other muscle structures of interest; analyze the goodness of outcomes; and provide a programming structure that allows the addition of new capabilities in future versions. The proper function of the code was verified using simulated datasets. The toolbox capabilities for characterizing human muscle structure in vivo were demonstrated in a case study. These capabilities included measurements of muscle morphology; contractile and non-contractile tissue volumes; fiber-tract length, pennation angle, curvature; and the physiological cross-sectional area,. The free public release of this software is a first step in creating of a community of users who use these tools in studies of muscle physiology and biomechanics. Users may further contribute to code development. Along with simulated and actual datasets for benchmarking, these tools will further create mechanisms for enhancing scientific rigor and developing and validating new code features. Planned future developments include additional options for image pre-processing, development of a graphical user interface, analysis of architectural patterns during muscle contraction, and integration of functional imaging data.

| S-EPMC9673086 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data