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Zhang T, Hua C, Chen J, He E, Wang H. Study of Human Tacit Knowledge Based on Electroencephalogram Signal Characteristics. Front Neurosci 2021; 15:690633. [PMID: 34335166 PMCID: PMC8317221 DOI: 10.3389/fnins.2021.690633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 06/15/2021] [Indexed: 11/13/2022] Open
Abstract
Tacit knowledge is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. In the mineral grinding process, the proficiency of the operators depends on the tacit knowledge gained from their experience and training rather than on knowledge learned from a handbook. This article proposed a method combining the electroencephalogram (EEG) signals and the industrial process to detect the proficiency of the operators in the mineral grinding process to reveal the effect of tacit knowledge on the functional cortical connection. The functional brain networks of operators were established based on partial direct coherence and directed transfer function of EEG, and the multi-classifiers were used with the graph-theoretic indexes of the FBNs as input to distinguish the trained operators (Hps) from the non-trained operators (Lps). The results showed that the brain networks of Hps had a better connectivity than those of Lps (p < 0.01), and the accuracy of classification was up to 94.2%. Our studies confirm that based on the performance of EEG features and the combination of industrial operational operation and cognitive processes, the proficiency of the operators can be detected.
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Affiliation(s)
- Tao Zhang
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang, China.,College of Applied Technology, Shenyang University, Shenyang, China
| | - Chengcheng Hua
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
| | - Jichi Chen
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China
| | - Enqiu He
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang, China
| | - Hong Wang
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
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2
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Hua C, Wang H, Wang H, Lu S, Liu C, Khalid SM. A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection. Int J Neural Syst 2019; 29:1850015. [DOI: 10.1142/s0129065718500156] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of this study is to detect the proficiency of operators during their mineral grinding process controlling based on FBN. To save the search time, a novel semi-data-driven method of computing functional brain connection based on stacked autoencoder (BCSAE) is proposed in this paper. This method uses stacked autoencoder (SAE) to encode the multi-channel EEG data into codes and then computes the dissimilarity between the codes from every pair of electrodes to build FBN. The highlight of this method is that the SAE has a multi-layered structure and is semi-supervised, which means it can dig deeper information and generate better features. Then an experiment was performed, the EEG of the operators were collected while they were operating and analyzed to detect their proficiency. The results show that the BCSAE method generated more number of separable features with less redundancy, and the average accuracy of classification (96.18%) is higher than that of the control methods: PLV (92.19%) and PLI (78.39%).
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Affiliation(s)
- Chengcheng Hua
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China
| | - Hong Wang
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China
| | - Hong Wang
- Control System Centre, The University of Manchester, Manchester, UK
| | - Shaowen Lu
- State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110189, P. R. China
| | - Chong Liu
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China
| | - Syed Madiha Khalid
- Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China
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3
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Aydin S, Demirtaş S, Ateş K, Tunga MA. Emotion Recognition with Eigen Features of Frequency Band Activities Embedded in Induced Brain Oscillations Mediated by Affective Pictures. Int J Neural Syst 2016; 26:1650013. [DOI: 10.1142/s0129065716500131] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
In this study, singular spectrum analysis (SSA) has been used for the first time in order to extract emotional features from well-defined electroencephalography (EEG) frequency band activities (BAs) so-called delta (0.5–4[Formula: see text]Hz), theta (4–8[Formula: see text]Hz), alpha (8–16[Formula: see text]Hz), beta (16–32[Formula: see text]Hz), gamma (32–64[Formula: see text]Hz). These five BAs were estimated by applying sixth-level multi-resolution wavelet decomposition (MRWD) with Daubechies wavelets (db-8) to single channel nonaveraged emotional EEG oscillations of 6 s for each scalp location over 16 recording sites (Fp1, Fp2, F3, F4, F7, F8, C3, C4, P3, P4, T3, T4, T5, T6, O1, O2). Every trial was mediated by different emotional stimuli which were selected from international affective picture system (IAPS) to induce emotional states such as pleasant (P), neutral (N), and unpleasant (UP). Largest principal components (PCs) of BAs were considered as emotional features and data mining approaches were used for the first time in order to classify both three different (P, N, UP) and two contrasting (P and UP) emotional states for 30 healthy controls. Emotional features extracted from gamma BAs (GBAs) for 16 recording sites provided the high classification accuracies of 87.1% and 100% for classification of three emotional states and two contrasting emotional states, respectively. In conclusion, we found the followings: (1) Eigenspectra of high frequency BAs in EEG are highly sensitive to emotional hemispheric activations, (2) emotional states are mostly mediated by GBA, (3) pleasant pictures induce the higher cortical activation in contrast to unpleasant pictures, (4) contrasting emotions induce opposite cortical activations, (5) cognitive activities are necessary for an emotion to occur.
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Affiliation(s)
- Serap Aydin
- Biomedical Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey
| | - Serdar Demirtaş
- Department of Biophysics, Gülhane Military Medical Academy, Ankara, Turkey
| | - Kahraman Ateş
- Department of Biophysics, Gülhane Military Medical Academy, Ankara, Turkey
| | - M. Alper Tunga
- Software Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey
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4
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Yuvaraj R, Murugappan M, Acharya UR, Adeli H, Ibrahim NM, Mesquita E. Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia. Behav Brain Res 2016; 298:248-60. [DOI: 10.1016/j.bbr.2015.10.036] [Citation(s) in RCA: 103] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2015] [Revised: 10/16/2015] [Accepted: 10/20/2015] [Indexed: 11/26/2022]
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5
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Acharya UR, Bhat S, Faust O, Adeli H, Chua ECP, Lim WJE, Koh JEW. Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection. Eur Neurol 2015; 74:268-87. [DOI: 10.1159/000441975] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 10/27/2015] [Indexed: 11/19/2022]
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6
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Yang Y, Solis-Escalante T, Yao J, Daffertshofer A, Schouten AC, van der Helm FCT. A General Approach for Quantifying Nonlinear Connectivity in the Nervous System Based on Phase Coupling. Int J Neural Syst 2015; 26:1550031. [PMID: 26404514 DOI: 10.1142/s0129065715500318] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Interaction between distant neuronal populations is essential for communication within the nervous system and can occur as a highly nonlinear process. To better understand the functional role of neural interactions, it is important to quantify the nonlinear connectivity in the nervous system. We introduce a general approach to measure nonlinear connectivity through phase coupling: the multi-spectral phase coherence (MSPC). Using simulated data, we compare MSPC with existing phase coupling measures, namely n : m synchronization index and bi-phase locking value. MSPC provides a system description, including (i) the order of the nonlinearity, (ii) the direction of interaction, (iii) the time delay in the system, and both (iv) harmonic and (v) intermodulation coupling beyond the second order; which are only partly revealed by other methods. We apply MSPC to analyze data from a motor control experiment, where subjects performed isotonic wrist flexions while receiving movement perturbations. MSPC between the perturbation, EEG and EMG was calculated. Our results reveal directional nonlinear connectivity in the afferent and efferent pathways, as well as the time delay (43 ± 8 ms) between the perturbation and the brain response. In conclusion, MSPC is a novel approach capable to assess high-order nonlinear interaction and timing in the nervous system.
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Affiliation(s)
- Yuan Yang
- 1 Department of Biomechanical Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands
| | - Teodoro Solis-Escalante
- 1 Department of Biomechanical Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands
| | - Jun Yao
- 2 Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Andreas Daffertshofer
- 3 Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdam, 1081 BT, The Netherlands
| | - Alfred C Schouten
- 1 Department of Biomechanical Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands.,4 MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, 7500 AE, The Netherlands
| | - Frans C T van der Helm
- 1 Department of Biomechanical Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands
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7
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Vahabi Z, Amirfattahi R, Shayegh F, Ghassemi F. Online Epileptic Seizure Prediction Using Wavelet-Based Bi-Phase Correlation of Electrical Signals Tomography. Int J Neural Syst 2015; 25:1550028. [DOI: 10.1142/s0129065715500288] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Considerable efforts have been made in order to predict seizures. Among these methods, the ones that quantify synchronization between brain areas, are the most important methods. However, to date, a practically acceptable result has not been reported. In this paper, we use a synchronization measurement method that is derived according to the ability of bi-spectrum in determining the nonlinear properties of a system. In this method, first, temporal variation of the bi-spectrum of different channels of electro cardiography (ECoG) signals are obtained via an extended wavelet-based time-frequency analysis method; then, to compare different channels, the bi-phase correlation measure is introduced. Since, in this way, the temporal variation of the amount of nonlinear coupling between brain regions, which have not been considered yet, are taken into account, results are more reliable than the conventional phase-synchronization measures. It is shown that, for 21 patients of FSPEEG database, bi-phase correlation can discriminate the pre-ictal and ictal states, with very low false positive rates (FPRs) (average: 0.078/h) and high sensitivity (100%). However, the proposed seizure predictor still cannot significantly overcome the random predictor for all patients.
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Affiliation(s)
- Zahra Vahabi
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Rasoul Amirfattahi
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Farzaneh Shayegh
- Department of Electrical Engineering, Payame Noor University (PNU), Isfahan, Iran
- Medical Image and Signal Processing Research Center, Medical University of Isfahan, Isfahan, Iran
| | - Fahimeh Ghassemi
- Department of Advanced Medical Technologies, Medical University of Isfahan, Isfahan, Iran
- Medical Image and Signal Processing Research Center, Medical University of Isfahan, Isfahan, Iran
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8
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Serletis D, Bulacio J, Bingaman W, Najm I, González-Martínez J. The stereotactic approach for mapping epileptic networks: a prospective study of 200 patients. J Neurosurg 2014; 121:1239-46. [DOI: 10.3171/2014.7.jns132306] [Citation(s) in RCA: 130] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Object
Stereoelectroencephalography (SEEG) is a methodology that permits accurate 3D in vivo electroclinical recordings of epileptiform activity. Among other general indications for invasive intracranial electroencephalography (EEG) monitoring, its advantages include access to deep cortical structures, its ability to localize the epileptogenic zone when subdural grids have failed to do so, and its utility in the context of possible multifocal seizure onsets with the need for bihemispheric explorations. In this context, the authors present a brief historical overview of the technique and report on their experience with 2 SEEG techniques (conventional Leksell frame-based stereotaxy and frameless stereotaxy under robotic guidance) for the purpose of invasively monitoring difficult-to-localize refractory focal epilepsy.
Methods
Over a period of 4 years, the authors prospectively identified 200 patients with refractory epilepsy who collectively underwent 2663 tailored SEEG electrode implantations for invasive intracranial EEG monitoring and extraoperative mapping. The first 122 patients underwent conventional Leksell frame-based SEEG electrode placement; the remaining 78 patients underwent frameless stereotaxy under robotic guidance, following acquisition of a stereotactic ROSA robotic device at the authors' institution. Electrodes were placed according to a preimplantation hypothesis of the presumed epileptogenic zone, based on a standardized preoperative workup including video-EEG monitoring, MRI, PET, ictal SPECT, and neuropsychological assessment. Demographic features, seizure semiology, number and location of implanted SEEG electrodes, and location of the epileptogenic zone were recorded and analyzed for all patients. For patients undergoing subsequent craniotomy for resection, the type of resection and procedure-related complications were prospectively recorded. These results were analyzed and correlated with pathological diagnosis and postoperative seizure outcomes.
Results
The epileptogenic zone was confirmed by SEEG in 154 patients (77%), of which 134 (87%) underwent subsequent craniotomy for epileptogenic zone resection. Within this cohort, 90 patients had a minimum follow-up of at least 12 months; therein, 61 patients (67.8%) remained seizure free, with an average follow-up period of 2.4 years. The most common pathological diagnosis was focal cortical dysplasia Type I (55 patients, 61.1%). Per electrode, the surgical complications included wound infection (0.08%), hemorrhagic complications (0.08%), and a transient neurological deficit (0.04%) in a total of 5 patients (2.5%). One patient (0.5%) ultimately died due to intracerebral hematoma directly ensuing from SEEG electrode placement.
Conclusions
Based on these results, SEEG methodology is safe, reliable, and effective. It is associated with minimal morbidity and mortality, and serves as a practical, minimally invasive approach to extraoperative localization of the epileptogenic zone in patients with refractory epilepsy.
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Affiliation(s)
- Demitre Serletis
- 1Department of Neurosurgery, University of Arkansas for Medical Sciences, Little Rock, Arkansas; and
| | - Juan Bulacio
- 2Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio
| | - William Bingaman
- 2Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio
| | - Imad Najm
- 2Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio
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Sharma P, Khan YU, Farooq O, Tripathi M, Adeli H. A Wavelet-Statistical Features Approach for Nonconvulsive Seizure Detection. Clin EEG Neurosci 2014; 45:274-284. [PMID: 24934269 DOI: 10.1177/1550059414535465] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2014] [Accepted: 04/21/2014] [Indexed: 11/16/2022]
Abstract
The detection of nonconvulsive seizures (NCSz) is a challenge because of the lack of physical symptoms, which may delay the diagnosis of the disease. Many researchers have reported automatic detection of seizures. However, few investigators have concentrated on detection of NCSz. This article proposes a method for reliable detection of NCSz. The electroencephalography (EEG) signal is usually contaminated by various nonstationary noises. Signal denoising is an important preprocessing step in the analysis of such signals. In this study, a new wavelet-based denoising approach using cubical thresholding has been proposed to reduce noise from the EEG signal prior to analysis. Three statistical features were extracted from wavelet frequency bands, encompassing the frequency range of 0 to 8, 8 to 16, 16 to 32, and 0 to 32 Hz. Extracted features were used to train linear classifier to discriminate between normal and seizure EEGs. The performance of the method was tested on a database of nine patients with 24 seizures in 80 hours of EEG recording. All the seizures were successfully detected, and false positive rate was found to be 0.7 per hour.
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Affiliation(s)
- Priyanka Sharma
- Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Yusuf Uzzaman Khan
- Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Omar Farooq
- Z. H. College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | | | - Hojjat Adeli
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH 43210
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10
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Abstract
Brain–computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson’s disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders.
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Affiliation(s)
- Alexis Burns
- Biomedical Engineering Graduate Program, The Ohio State University, Columbus, OH, USA
| | - Hojjat Adeli
- Departments of Biomedical Engineering, Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science, Electrical and Computer Engineering, and Neuroscience, and the Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA
| | - John A. Buford
- Physical Therapy Division, School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, OH, USA
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Porz S, Kiel M, Lehnertz K. Can spurious indications for phase synchronization due to superimposed signals be avoided? CHAOS (WOODBURY, N.Y.) 2014; 24:033112. [PMID: 25273192 DOI: 10.1063/1.4890568] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
We investigate the relative merit of phase-based methods-mean phase coherence, unweighted and weighted phase lag index-for estimating the strength of interactions between dynamical systems from empirical time series which are affected by common sources and noise. By numerically analyzing the interaction dynamics of coupled model systems, we compare these methods to each other with respect to their ability to distinguish between different levels of coupling for various simulated experimental situations. We complement our numerical studies by investigating consistency and temporal variations of the strength of interactions within and between brain regions using intracranial electroencephalographic recordings from an epilepsy patient. Our findings indicate that the unweighted and weighted phase lag index are less prone to the influence of common sources but that this advantage may lead to constrictions limiting the applicability of these methods.
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Affiliation(s)
- Stephan Porz
- Department of Epileptology, University of Bonn, Sigmund-Freud-Str. 25, 53105 Bonn, Germany
| | - Matthäus Kiel
- Department of Epileptology, University of Bonn, Sigmund-Freud-Str. 25, 53105 Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Sigmund-Freud-Str. 25, 53105 Bonn, Germany
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KALITZIN STILIYAN, KOPPERT MARCUS, PETKOV GEORGE, DA SILVA FERNANDOLOPES. MULTIPLE OSCILLATORY STATES IN MODELS OF COLLECTIVE NEURONAL DYNAMICS. Int J Neural Syst 2014; 24:1450020. [DOI: 10.1142/s0129065714500208] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In our previous studies, we showed that the both realistic and analytical computational models of neural dynamics can display multiple sustained states (attractors) for the same values of model parameters. Some of these states can represent normal activity while other, of oscillatory nature, may represent epileptic types of activity. We also showed that a simplified, analytical model can mimic this type of behavior and can be used instead of the realistic model for large scale simulations. The primary objective of the present work is to further explore the phenomenon of multiple stable states, co-existing in the same operational model, or phase space, in systems consisting of large number of interconnected basic units. As a second goal, we aim to specify the optimal method for state control of the system based on inducing state transitions using appropriate external stimulus. We use here interconnected model units that represent the behavior of neuronal populations as an effective dynamic system. The model unit is an analytical model (S. Kalitzin et al., Epilepsy Behav. 22 (2011) S102–S109) and does not correspond directly to realistic neuronal processes (excitatory–inhibitory synaptic interactions, action potential generation). For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models. To analyze the collective behavior of the system we applied phase synchronization analysis (PSA), principal component analysis (PCA) and stability analysis using Lyapunov exponent (LE) estimation. We obtained a large variety of stable states with different dynamic characteristics, oscillatory modes and phase relations between the units. These states can be initiated by appropriate initial conditions; transitions between them can be induced stochastically by fluctuating variables (noise) or by specific inputs. We propose a method for optimal reactive control, allowing forced transitions from one state (attractor) into another.
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Affiliation(s)
- STILIYAN KALITZIN
- Foundation Epilepsy Institute in The Netherlands (SEIN), Achterweg 5, Heemstede, The Netherlands
| | - MARCUS KOPPERT
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon EX4 4QF, UK
| | - GEORGE PETKOV
- Foundation Epilepsy Institute in The Netherlands (SEIN), Achterweg 5, Heemstede, The Netherlands
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon EX4 4QF, UK
| | - FERNANDO LOPES DA SILVA
- Swammerdam Institute for Life Sciences, Center of Neuroscience, University of Amsterdam, Amsterdam, The Netherlands
- Department of Bioengineering, Instituto Superior Técnico, Lisbon Technical University, Lisbon, Portugal
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STRACK BEATA, JACOBS KIMBERLEM, CIOS KRZYSZTOFJ. SIMULATING VERTICAL AND HORIZONTAL INHIBITION WITH SHORT-TERM DYNAMICS IN A MULTI-COLUMN MULTI-LAYER MODEL OF NEOCORTEX. Int J Neural Syst 2014; 24:1440002. [DOI: 10.1142/s0129065714400024] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The paper introduces a multi-layer multi-column model of the cortex that uses four different neuron types and short-term plasticity dynamics. It was designed with details of neuronal connectivity available in the literature and meets these conditions: (1) biologically accurate laminar and columnar flows of activity, (2) normal function of low-threshold spiking and fast spiking neurons, and (3) ability to generate different stages of epileptiform activity. With these characteristics the model allows for modeling lesioned or malformed cortex, i.e. examine properties of developmentally malformed cortex in which the balance between inhibitory neuron subtypes is disturbed.
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Affiliation(s)
- BEATA STRACK
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - KIMBERLE M. JACOBS
- Department of Anatomy and Neurobiology, Virginia Commonwealth University, Richmond, VA, USA
| | - KRZYSZTOF J. CIOS
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
- IITiS Polish Academy of Sciences, Poland
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14
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Ahmadlou M, Adeli A, Bajo R, Adeli H. Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task. Clin Neurophysiol 2014; 125:694-702. [DOI: 10.1016/j.clinph.2013.08.033] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 07/30/2013] [Accepted: 08/06/2013] [Indexed: 01/25/2023]
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