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Oprisan SA, Clementsmith X, Tompa T, Lavin A. Empirical mode decomposition of local field potential data from optogenetic experiments. Front Comput Neurosci 2023; 17:1223879. [PMID: 37476356 PMCID: PMC10354259 DOI: 10.3389/fncom.2023.1223879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 06/19/2023] [Indexed: 07/22/2023] Open
Abstract
Introduction This study investigated the effects of cocaine administration and parvalbumin-type interneuron stimulation on local field potentials (LFPs) recorded in vivo from the medial prefrontal cortex (mPFC) of six mice using optogenetic tools. Methods The local network was subject to a brief 10 ms laser pulse, and the response was recorded for 2 s over 100 trials for each of the six subjects who showed stable coupling between the mPFC and the optrode. Due to the strong non-stationary and nonlinearity of the LFP, we used the adaptive, data-driven, Empirical Mode Decomposition (EMD) method to decompose the signal into orthogonal Intrinsic Mode Functions (IMFs). Results Through trial and error, we found that seven is the optimum number of orthogonal IMFs that overlaps with known frequency bands of brain activity. We found that the Index of Orthogonality (IO) of IMF amplitudes was close to zero. The Index of Energy Conservation (IEC) for each decomposition was close to unity, as expected for orthogonal decompositions. We found that the power density distribution vs. frequency follows a power law with an average scaling exponent of ~1.4 over the entire range of IMF frequencies 2-2,000 Hz. Discussion The scaling exponent is slightly smaller for cocaine than the control, suggesting that neural activity avalanches under cocaine have longer life spans and sizes.
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Affiliation(s)
- Sorinel A. Oprisan
- Department of Physics and Astronomy, College of Charleston, Charleston, SC, United States
| | - Xandre Clementsmith
- Department of Computer Science, College of Charleston, Charleston, SC, United States
| | - Tamas Tompa
- Faculty of Healthcare, Department of Preventive Medicine, University of Miskolc, Miskolc, Hungary
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, United States
| | - Antonieta Lavin
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, United States
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Alegre-Cortés J, Sáez M, Montanari R, Reig R. Medium spiny neurons activity reveals the discrete segregation of mouse dorsal striatum. eLife 2021; 10:e60580. [PMID: 33599609 PMCID: PMC7924950 DOI: 10.7554/elife.60580] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 02/15/2021] [Indexed: 01/08/2023] Open
Abstract
Behavioral studies differentiate the rodent dorsal striatum (DS) into lateral and medial regions; however, anatomical evidence suggests that it is a unified structure. To understand striatal dynamics and basal ganglia functions, it is essential to clarify the circuitry that supports this behavioral-based segregation. Here, we show that the mouse DS is made of two non-overlapping functional circuits divided by a boundary. Combining in vivo optopatch-clamp and extracellular recordings of spontaneous and evoked sensory activity, we demonstrate different coupling of lateral and medial striatum to the cortex together with an independent integration of the spontaneous activity, due to particular corticostriatal connectivity and local attributes of each region. Additionally, we show differences in slow and fast oscillations and in the electrophysiological properties between striatonigral and striatopallidal neurons. In summary, these results demonstrate that the rodent DS is segregated in two neuronal circuits, in homology with the caudate and putamen nuclei of primates.
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Affiliation(s)
| | - María Sáez
- Instituto de Neurociencias CSIC-UMHSan Juan de AlicanteSpain
| | | | - Ramon Reig
- Instituto de Neurociencias CSIC-UMHSan Juan de AlicanteSpain
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Farfán FD, Soto-Sánchez C, Pizá AG, Albarracín AL, Soletta JH, Lucianna FA, Fernández E. Comparative study of extracellular recording methods for analysis of afferent sensory information: Empirical modeling, data analysis and interpretation. J Neurosci Methods 2019; 320:116-127. [PMID: 30849435 DOI: 10.1016/j.jneumeth.2019.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 03/02/2019] [Accepted: 03/04/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND Physiological studies of sensorial systems often require the acquisition and processing of data extracted from their multiple components to evaluate how the neural information changes in relation to the environment changes. In this work, a comparative study about methodological aspects of two electrophysiological approaches is described. NEW METHOD Extracellular recordings from deep vibrissal nerves were obtained by using a customized microelectrode Utah array during passive mechanical stimulation of rat´s whiskers. These recordings were compared with those obtained with bipolar electrodes. We also propose here a simplified empirical model of the electrophysiological activity obtained from a bundle of myelinated nerve fibers. RESULTS The peripheral activity of the vibrissal system was characterized through the temporal and spectral features obtained with both recording methods. The empirical model not only allows the correlation between anatomical structures and functional features, but also allows to predict changes in the CAPs morphology when the arrangement and the geometry of the electrodes changes. COMPARISON WITH EXISTING METHOD(S) This study compares two extracellular recording methods based on analysis techniques, empirical modeling and data processing of vibrissal sensory information. CONCLUSIONS This comparative study reveals a close relationship between the electrophysiological techniques and the processing methods necessary to extract sensory information. This relationship is the result of maximizing the extraction of information from recordings of sensory activity.
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Affiliation(s)
- F D Farfán
- Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina.
| | - C Soto-Sánchez
- Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain; Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain.
| | - A G Pizá
- Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina.
| | - A L Albarracín
- Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina.
| | - J H Soletta
- Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina.
| | - F A Lucianna
- Laboratorio de Medios e Interfases (LAMEIN), Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina; Instituto Superior de Investigaciones Biológicas (INSIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina.
| | - E Fernández
- Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain; Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain.
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Unmixing Oscillatory Brain Activity by EEG Source Localization and Empirical Mode Decomposition. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:5618303. [PMID: 31015827 PMCID: PMC6448348 DOI: 10.1155/2019/5618303] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 01/15/2019] [Accepted: 02/04/2019] [Indexed: 11/29/2022]
Abstract
Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.
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Lozano A, Soto-Sánchez C, Garrigós J, Martínez JJ, Ferrández JM, Fernández E. A 3D Convolutional Neural Network to Model Retinal Ganglion Cell's Responses to Light Patterns in Mice. Int J Neural Syst 2018; 28:1850043. [PMID: 30556459 DOI: 10.1142/s0129065718500430] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Deep Learning offers flexible powerful tools that have advanced our understanding of the neural coding of neurosensory systems. In this work, a 3D Convolutional Neural Network (3D CNN) is used to mimic the behavior of a population of mice retinal ganglion cells in response to different light patterns. For this purpose, we projected homogeneous RGB flashes and checkerboards stimuli with variable luminances and wavelength spectrum to mimic a more naturalistic stimuli environment onto the mouse retina. We also used white moving bars in order to localize the spatial position of the recorded cells. Then recorded spikes were smoothed with a Gaussian kernel and used as the output target when training a 3D CNN in a supervised way. To find a suitable model, two hyperparameter search stages were performed. In the first stage, a trial and error process allowed us to obtain a system that is able to fit the neurons firing rates. In the second stage, a systematic procedure was used to compare several gradient-based optimizers, loss functions and the model's convolutional layers number. We found that a three layered 3D CNN was able to predict the ganglion cells firing rates with high correlations and low prediction error, as measured with Mean Squared Error and Dynamic Time Warping in test sets. These models were either competitive or outperformed other models used already in neuroscience, as Feed Forward Neural Networks and Linear-Nonlinear models. This methodology allowed us to capture the temporal dynamic response patterns in a robust way, even for neurons with high trial-to-trial variable spontaneous firing rates, when providing the peristimulus time histogram as an output to our model.
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Affiliation(s)
- Antonio Lozano
- Dpto. Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain
| | - Cristina Soto-Sánchez
- Instituto de Bioingeniería, Universidad Miguel Hernández, Alicante, Spain
- CIBER-BBN, Madrid, Spain
| | - Javier Garrigós
- Dpto. Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain
| | - J. Javier Martínez
- Dpto. Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain
| | - J. Manuel Ferrández
- Dpto. Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain
| | - Eduardo Fernández
- Instituto de Bioingeniería, Universidad Miguel Hernández, Alicante, Spain
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Alegre-Cortés J, Soto-Sánchez C, Fernandez E. Multiscale dynamics of interstimulus interval integration in visual cortex. PLoS One 2018; 13:e0208822. [PMID: 30557375 PMCID: PMC6296521 DOI: 10.1371/journal.pone.0208822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 11/24/2018] [Indexed: 11/18/2022] Open
Abstract
Although the visual cortex receives information at multiple temporal patterns, much of the research in the field has focused only on intervals shorter than 1 second. Consequently, there is almost no information on what happens at longer temporal intervals. We have tried to address this question recording neuronal populations of the primary visual cortex during visual stimulation with repetitive grating stimuli and intervals ranging from 1 to 7 seconds. Our results showed that firing rate and response stability were dependent of interval duration. In addition, there were collective oscillations with different properties in response to changes in intervals duration. These results suggest that visual cortex could encode visual information at several time scales using oscillations at multiple frequencies.
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Affiliation(s)
- J. Alegre-Cortés
- Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain
| | - C. Soto-Sánchez
- Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain
- Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Biotechnology Department, University of Alicante (AU), Alicante, Spain
| | - E. Fernandez
- Bioengineering Institute, Miguel Hernández University (UMH), Alicante, Spain
- Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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Alegre-Cortés J, Soto-Sánchez C, Albarracín AL, Farfán FD, Val-Calvo M, Ferrandez JM, Fernandez E. Toward an Improvement of the Analysis of Neural Coding. Front Neuroinform 2018; 11:77. [PMID: 29375359 PMCID: PMC5767721 DOI: 10.3389/fninf.2017.00077] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 12/22/2017] [Indexed: 11/13/2022] Open
Abstract
Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.
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Affiliation(s)
- Javier Alegre-Cortés
- Neuroprosthetics and Visual Rehabilitation Research Unit, Bioengineering Institute, Miguel Hernández University, Alicante, Spain
| | - Cristina Soto-Sánchez
- Neuroprosthetics and Visual Rehabilitation Research Unit, Bioengineering Institute, Miguel Hernández University, Alicante, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain.,Biotechnology Department, University of Alicante, Alicante, Spain
| | - Ana L Albarracín
- Laboratorio de Medios e Interfases, Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina.,Departamento de Bioingeniería, Instituto Superior de Investigaciones Biológicas, Consejo Nacional de Investigaciones Científicas y Técnicas, Tucumán, Argentina
| | - Fernando D Farfán
- Laboratorio de Medios e Interfases, Departamento de Bioingeniería, Facultad de Ciencias Exactas y Tecnología, Universidad Nacional de Tucumán, Tucumán, Argentina.,Departamento de Bioingeniería, Instituto Superior de Investigaciones Biológicas, Consejo Nacional de Investigaciones Científicas y Técnicas, Tucumán, Argentina
| | - Mikel Val-Calvo
- Departamento de Electrónica, Tecnología de Computadoras, Universidad Politécnica de Cartagena, Cartagena, Spain
| | - José M Ferrandez
- Departamento de Electrónica, Tecnología de Computadoras, Universidad Politécnica de Cartagena, Cartagena, Spain
| | - Eduardo Fernandez
- Neuroprosthetics and Visual Rehabilitation Research Unit, Bioengineering Institute, Miguel Hernández University, Alicante, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
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