1
|
Szechtman H, Dvorkin-Gheva A, Gomez-Marin A. A virtual library for behavioral performance in standard conditions-rodent spontaneous activity in an open field during repeated testing and after treatment with drugs or brain lesions. Gigascience 2022; 11:6756450. [PMID: 36261217 PMCID: PMC9581716 DOI: 10.1093/gigascience/giac092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/31/2022] [Accepted: 09/06/2022] [Indexed: 11/04/2022] Open
Abstract
Background Beyond their specific experiment, video records of behavior have future value—for example, as inputs for new experiments or for yet unknown types of analysis of behavior—similar to tissue or blood sample banks in life sciences where clinically derived or otherwise well-described experimental samples are stored to be available for some unknown potential future purpose. Findings Research using an animal model of obsessive-compulsive disorder employed a standardized paradigm where the behavior of rats in a large open field was video recorded for 55 minutes on each test. From 43 experiments, there are 19,976 such trials that amount to over 2 years of continuous recording. In addition to videos, there are 2 video-derived raw data objects: XY locomotion coordinates and plots of animal trajectory. To motivate future use, the 3 raw data objects are annotated with a general schema—one that abstracts the data records from their particular experiment while providing, at the same time, a detailed list of independent variables bearing on behavioral performance. The raw data objects are deposited as 43 datasets but constitute, functionally, a library containing 1 large dataset. Conclusions Size and annotation schema give the library high reuse potential: in applications using machine learning techniques, statistical evaluation of subtle factors, simulation of new experiments, or as educational resource. Ultimately, the library can serve both as the seed and as the test bed to create a machine-searchable virtual library of linked open datasets for behavioral performance in defined conditions.
Collapse
Affiliation(s)
- Henry Szechtman
- Correspondence address. Henry Szechtman, Department of Psychiatry and Behavioural Neurosciences, McMaster University, 1280 Main Street West, Health Science Centre, Room 4N82, Hamilton, Ontario, Canada L8S 4K1. E-mail:
| | - Anna Dvorkin-Gheva
- Department of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario L8S 4K1, Canada
| | - Alex Gomez-Marin
- Department of Systems Neurobiology, Instituto de Neurociencias (CSIC-UMH), 03550 Sant Joan d'Alacant, Alicante, Spain
| |
Collapse
|
2
|
Huang C, Zeldenrust F, Celikel T. Cortical Representation of Touch in Silico. Neuroinformatics 2022; 20:1013-1039. [PMID: 35486347 PMCID: PMC9588483 DOI: 10.1007/s12021-022-09576-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/19/2022] [Indexed: 12/31/2022]
Abstract
With its six layers and ~ 12,000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents'. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biologically inspired, computationally efficient network model of the somatosensory cortex's granular and supragranular layers after reconstructing the barrel cortex in soma resolution. Comparisons of the network activity to empirical observations showed that the in silico network replicates the known properties of touch representations and whisker deprivation-induced changes in synaptic strength induced in vivo. Simulations show that the history of the membrane potential acts as a spatial filter that determines the presynaptic population of neurons contributing to a post-synaptic action potential; this spatial filtering might be critical for synaptic integration of top-down and bottom-up information.
Collapse
Affiliation(s)
- Chao Huang
- grid.9647.c0000 0004 7669 9786Department of Biology, University of Leipzig, Leipzig, Germany
| | - Fleur Zeldenrust
- grid.5590.90000000122931605Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Tansu Celikel
- grid.5590.90000000122931605Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands ,grid.213917.f0000 0001 2097 4943School of Psychology, Georgia Institute of Technology, Atlanta, GA USA
| |
Collapse
|
3
|
Betting JHLF, Romano V, Al-Ars Z, Bosman LWJ, Strydis C, De Zeeuw CI. WhiskEras: A New Algorithm for Accurate Whisker Tracking. Front Cell Neurosci 2020; 14:588445. [PMID: 33281560 PMCID: PMC7705537 DOI: 10.3389/fncel.2020.588445] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/15/2020] [Indexed: 01/10/2023] Open
Abstract
Rodents engage in active touch using their facial whiskers: they explore their environment by making rapid back-and-forth movements. The fast nature of whisker movements, during which whiskers often cross each other, makes it notoriously difficult to track individual whiskers of the intact whisker field. We present here a novel algorithm, WhiskEras, for tracking of whisker movements in high-speed videos of untrimmed mice, without requiring labeled data. WhiskEras consists of a pipeline of image-processing steps: first, the points that form the whisker centerlines are detected with sub-pixel accuracy. Then, these points are clustered in order to distinguish individual whiskers. Subsequently, the whiskers are parameterized so that a single whisker can be described by four parameters. The last step consists of tracking individual whiskers over time. We describe that WhiskEras performs better than other whisker-tracking algorithms on several metrics. On our four video segments, WhiskEras detected more whiskers per frame than the Biotact Whisker Tracking Tool. The signal-to-noise ratio of the output of WhiskEras was higher than that of Janelia Whisk. As a result, the correlation between reflexive whisker movements and cerebellar Purkinje cell activity appeared to be stronger than previously found using other tracking algorithms. We conclude that WhiskEras facilitates the study of sensorimotor integration by markedly improving the accuracy of whisker tracking in untrimmed mice.
Collapse
Affiliation(s)
| | - Vincenzo Romano
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
| | - Zaid Al-Ars
- Department of Quantum & Computer Engineering, Delft University of Technology, Delft, Netherlands
| | | | - Christos Strydis
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
- Department of Quantum & Computer Engineering, Delft University of Technology, Delft, Netherlands
| | - Chris I. De Zeeuw
- Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, Amsterdam, Netherlands
| |
Collapse
|
4
|
Hughes S, Celikel T. Prominent Inhibitory Projections Guide Sensorimotor Computation: An Invertebrate Perspective. Bioessays 2019; 41:e1900088. [DOI: 10.1002/bies.201900088] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 07/17/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Samantha Hughes
- HAN BioCentreHAN University of Applied Sciences Nijmegen 6525EM The Netherlands
| | - Tansu Celikel
- Department of Neurophysiology, Donders Institute for Brain Cognition and BehaviourRadboud University Nijmegen 6525AJ The Netherlands
| |
Collapse
|
5
|
da Silva Lantyer A, Calcini N, Bijlsma A, Kole K, Emmelkamp M, Peeters M, Scheenen WJJ, Zeldenrust F, Celikel T. A databank for intracellular electrophysiological mapping of the adult somatosensory cortex. Gigascience 2018; 7:5232232. [PMID: 30521020 PMCID: PMC6302958 DOI: 10.1093/gigascience/giy147] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 11/18/2018] [Indexed: 02/04/2023] Open
Abstract
Background Neurons in the supragranular layers of the somatosensory cortex integrate sensory (bottom-up) and cognitive/perceptual (top-down) information as they orchestrate communication across cortical columns. It has been inferred, based on intracellular recordings from juvenile animals, that supragranular neurons are electrically mature by the fourth postnatal week. However, the dynamics of the neuronal integration in adulthood is largely unknown. Electrophysiological characterization of the active properties of these neurons throughout adulthood will help to address the biophysical and computational principles of the neuronal integration. Findings Here, we provide a database of whole-cell intracellular recordings from 315 neurons located in the supragranular layers (L2/3) of the primary somatosensory cortex in adult mice (9–45 weeks old) from both sexes (females, N = 195; males, N = 120). Data include 361 somatic current-clamp (CC) and 476 voltage-clamp (VC) experiments, recorded using a step-and-hold protocol (CC, N = 257; VC, N = 46), frozen noise injections (CC, N = 104) and triangular voltage sweeps (VC, 10 (N = 132), 50 (N = 146) and 100 ms (N = 152)), from regular spiking (N = 169) and fast-spiking neurons (N = 66). Conclusions The data can be used to systematically study the properties of somatic integration and the principles of action potential generation across sexes and across electrically characterized neuronal classes in adulthood. Understanding the principles of the somatic transformation of postsynaptic potentials into action potentials will shed light onto the computational principles of intracellular information transfer in single neurons and information processing in neuronal networks, helping to recreate neuronal functions in artificial systems.
Collapse
Affiliation(s)
- Angelica da Silva Lantyer
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Niccolò Calcini
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Ate Bijlsma
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Koen Kole
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Melanie Emmelkamp
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Manon Peeters
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Wim J J Scheenen
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Tansu Celikel
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| |
Collapse
|
6
|
Azarfar A, Zhang Y, Alishbayli A, Miceli S, Kepser L, van der Wielen D, van de Moosdijk M, Homberg J, Schubert D, Proville R, Celikel T. An open-source high-speed infrared videography database to study the principles of active sensing in freely navigating rodents. Gigascience 2018; 7:5168870. [PMID: 30418576 PMCID: PMC6283211 DOI: 10.1093/gigascience/giy134] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 10/21/2018] [Indexed: 11/12/2022] Open
Abstract
Background Active sensing is crucial for navigation. It is characterized by self-generated motor action controlling the accessibility and processing of sensory information. In rodents, active sensing is commonly studied in the whisker system. As rats and mice modulate their whisking contextually, they employ frequency and amplitude modulation. Understanding the development, mechanisms, and plasticity of adaptive motor control will require precise behavioral measurements of whisker position. Findings Advances in high-speed videography and analytical methods now permit collection and systematic analysis of large datasets. Here, we provide 6,642 videos as freely moving juvenile (third to fourth postnatal week) and adult rodents explore a stationary object on the gap-crossing task. The dataset includes sensory exploration with single- or multi-whiskers in wild-type animals, serotonin transporter knockout rats, rats received pharmacological intervention targeting serotonergic signaling. The dataset includes varying background illumination conditions and signal-to-noise ratios (SNRs), ranging from homogenous/high contrast to non-homogenous/low contrast. A subset of videos has been whisker and nose tracked and are provided as reference for image processing algorithms. Conclusions The recorded behavioral data can be directly used to study development of sensorimotor computation, top-down mechanisms that control sensory navigation and whisker position, and cross-species comparison of active sensing. It could also help to address contextual modulation of active sensing during touch-induced whisking in head-fixed vs freely behaving animals. Finally, it provides the necessary data for machine learning approaches for automated analysis of sensory and motion parameters across a wide variety of signal-to-noise ratios with accompanying human observer-determined ground-truth.
Collapse
Affiliation(s)
- Alireza Azarfar
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Heyendaalseweg 135, Nijmegen, 6525 HJ The Netherlands
| | - Yiping Zhang
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Heyendaalseweg 135, Nijmegen, 6525 HJ The Netherlands
| | - Artoghrul Alishbayli
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Heyendaalseweg 135, Nijmegen, 6525 HJ The Netherlands
| | - Stéphanie Miceli
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical School, Kapittelweg 29, Nijmegen, 6525 EN The Netherlands
| | - Lara Kepser
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical School, Kapittelweg 29, Nijmegen, 6525 EN The Netherlands
| | - Daan van der Wielen
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Heyendaalseweg 135, Nijmegen, 6525 HJ The Netherlands
| | - Mike van de Moosdijk
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Heyendaalseweg 135, Nijmegen, 6525 HJ The Netherlands
| | - Judith Homberg
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical School, Kapittelweg 29, Nijmegen, 6525 EN The Netherlands
| | - Dirk Schubert
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Heyendaalseweg 135, Nijmegen, 6525 HJ The Netherlands.,Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical School, Kapittelweg 29, Nijmegen, 6525 EN The Netherlands
| | - Rémi Proville
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Heyendaalseweg 135, Nijmegen, 6525 HJ The Netherlands
| | - Tansu Celikel
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Heyendaalseweg 135, Nijmegen, 6525 HJ The Netherlands
| |
Collapse
|