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Calcini N, Silva Lantyer AD, Zeldenrust F, Celikel T. Nonlinear super-resolution signal processing allows intracellular tracking of calcium dynamics. J Neural Eng 2024; 21:036008. [PMID: 38648784 DOI: 10.1088/1741-2552/ad417c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 04/22/2024] [Indexed: 04/25/2024]
Abstract
Objective.Traditional quantification of fluorescence signals, such asΔF/F, relies on ratiometric measures that necessitate a baseline for comparison, limiting their applicability in dynamic analyses. Our goal here is to develop a baseline-independent method for analyzing fluorescence data that fully exploits temporal dynamics to introduce a novel approach for dynamical super-resolution analysis, including in subcellular resolution.Approach.We introduce ARES (Autoregressive RESiduals), a novel method that leverages the temporal aspect of fluorescence signals. By focusing on the quantification of residuals following linear autoregression, ARES obviates the need for a predefined baseline, enabling a more nuanced analysis of signal dynamics.Main result.We delineate the foundational attributes of ARES, illustrating its capability to enhance both spatial and temporal resolution of calcium fluorescence activity beyond the conventional ratiometric measure (ΔF/F). Additionally, we demonstrate ARES's utility in elucidating intracellular calcium dynamics through the detailed observation of calcium wave propagation within a dendrite.Significance.ARES stands out as a robust and precise tool for the quantification of fluorescence signals, adept at analyzing both spontaneous and evoked calcium dynamics. Its ability to facilitate the subcellular localization of calcium signals and the spatiotemporal tracking of calcium dynamics-where traditional ratiometric measures falter-underscores its potential to revolutionize baseline-independent analyses in the field.
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Affiliation(s)
- Niccolò Calcini
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, Nijmegen 6525 HJ, The Netherlands
| | - Angelica da Silva Lantyer
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, Nijmegen 6525 HJ, The Netherlands
| | - Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, Nijmegen 6525 HJ, The Netherlands
| | - Tansu Celikel
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, Nijmegen 6525 HJ, The Netherlands
- School of Psychology, Georgia Institute of Technology, 654 Cherry Street, Atlanta, GA 30332, United States of America
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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.
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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
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Aldohbeyb AA, Vigh J, Lear KL. New methods for quantifying rapidity of action potential onset differentiate neuron types. PLoS One 2021; 16:e0247242. [PMID: 33831000 PMCID: PMC8032118 DOI: 10.1371/journal.pone.0247242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 02/03/2021] [Indexed: 11/23/2022] Open
Abstract
Two new methods for quantifying the rapidity of action potential onset have lower relative standard deviations and better distinguish neuron cell types than current methods. Action potentials (APs) in most central mammalian neurons exhibit sharp onset dynamics. The main views explaining such an abrupt onset differ. Some studies suggest sharp onsets reflect cooperative sodium channels activation, while others suggest they reflect AP backpropagation from the axon initial segment. However, AP onset rapidity is defined subjectively in these studies, often using the slope at an arbitrary value on the phase plot. Thus, we proposed more systematic methods using the membrane potential's second-time derivative ([Formula: see text]) peak width. Here, the AP rapidity was measured for four different cortical and hippocampal neuron types using four quantification methods: the inverse of full-width at the half maximum of the [Formula: see text] peak (IFWd2), the inverse of half-width at the half maximum of the [Formula: see text] peak (IHWd2), the phase plot slope, and the error ratio method. The IFWd2 and IHWd2 methods show the smallest variation among neurons of the same type. Furthermore, the AP rapidity, using the [Formula: see text] peak width methods, significantly differentiates between different types of neurons, indicating that AP rapidity can be used to classify neuron types. The AP rapidity measured using the IFWd2 method was able to differentiate between all four neuron types analyzed. Therefore, the [Formula: see text] peak width methods provide another sensitive tool to investigate the mechanisms impacting the AP onset dynamics.
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Affiliation(s)
- Ahmed A. Aldohbeyb
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States of America
- Department of Biomedical Technology, King Saud University, Riyadh, Kingdom of Saudi Arabia
| | - Jozsef Vigh
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States of America
- Department of Biomedical Sciences, Colorado State University, Fort Collins, CO, United States of America
| | - Kevin L. Lear
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States of America
- Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, United States of America
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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.
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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
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