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Guo X, Zhang X, Liu J, Zhai G, Zhang T, Zhou R, Lu H, Gao L. Resolving heterogeneity in dynamics of synchronization stability within the salience network in autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110956. [PMID: 38296155 DOI: 10.1016/j.pnpbp.2024.110956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 01/16/2024] [Accepted: 01/28/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Heterogeneity in resting-state functional connectivity (FC) are one of the characteristics of autism spectrum disorder (ASD). Traditional resting-state FC primarily focuses on linear correlations, ignoring the nonlinear properties involved in synchronization between networks or brain regions. METHODS In the present study, the cross-recurrence quantification analysis, a nonlinear method based on dynamical systems, was utilized to quantify the synchronization stability between brain regions within the salience network (SN) of ASD. Using the resting-state functional magnetic resonance imaging data of 207 children (ASD/typically-developing controls (TC): 105/102) in Autism Brain Imaging Data Exchange database, we analyzed the laminarity and trapping time differences of the synchronization stability between the ASD subtype derived by a K-means clustering analysis and the TC group, and examined the relationship between synchronization stability and the severity of clinical symptoms of the ASD subtypes. RESULTS Based on the synchronization stability within the SN of ASD, we identified two subtypes that showed opposite changes in synchronization stability relative to the TC group. In addition, the synchronization stability of ASD subtypes 1 and 2 can predict the social interaction and communication impairments, respectively. CONCLUSIONS These findings reveal that ASD subgroups with different patterns of synchronization stability within the SN appear distinct clinical symptoms, and highlight the importance of exploring the potential neural mechanism of ASD from a nonlinear perspective.
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Affiliation(s)
- Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
| | - Xia Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Junfeng Liu
- Department of Neurology, West China Hospital, Sichuan University, China, Chengdu, 610041, China
| | - Guangjin Zhai
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Tao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Rongjuan Zhou
- Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao 066000, China
| | - Huibin Lu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
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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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Li F, Yi C, Liao Y, Jiang Y, Si Y, Song L, Zhang T, Yao D, Zhang Y, Cao Z, Xu P. Reconfiguration of Brain Network Between Resting State and P300 Task. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.2965135] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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4
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Rajaei H, Adjouadi M, Cabrerizo M, Janwattanapong P, Pinzon A, Gonzales-Arias S, Barreto A, Andrian J, Rishe N, Yaylali I. Dynamics and Distant Effects of Frontal/Temporal Epileptogenic Focus Using Functional Connectivity Maps. IEEE Trans Biomed Eng 2020; 67:632-643. [DOI: 10.1109/tbme.2019.2919263] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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5
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A broadband method of quantifying phase synchronization for discriminating seizure EEG signals. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.10.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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6
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McCormick M, Reyna VF, Ball K, Katz JS, Deshpande G. Neural Underpinnings of Financial Decision Bias in Older Adults: Putative Theoretical Models and a Way to Reconcile Them. Front Neurosci 2019; 13:184. [PMID: 30930732 PMCID: PMC6427068 DOI: 10.3389/fnins.2019.00184] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 02/15/2019] [Indexed: 12/31/2022] Open
Affiliation(s)
- Michael McCormick
- Department of Psychology, Auburn University, Auburn, AL, United States
| | - Valerie F. Reyna
- Human Neuroscience Institute, Cornell University, Ithaca, NY, United States
- Department of Human Development, Cornell University, Ithaca, NY, United States
- Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, United States
- Magnetic Resonance Imaging Facility, Cornell University, Ithaca, NY, United States
| | - Karlene Ball
- Center for Research on Applied Gerontology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jeffrey S. Katz
- Department of Psychology, Auburn University, Auburn, AL, United States
- Department of Electrical Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
| | - Gopikrishna Deshpande
- Department of Psychology, Auburn University, Auburn, AL, United States
- Department of Electrical Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, United States
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7
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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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8
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Dynamic Causal Modeling and machine learning for effective connectivity in Auditory Hallucination. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2016.08.157] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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9
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Cheng L, Zhu Y, Sun J, Deng L, He N, Yang Y, Ling H, Ayaz H, Fu Y, Tong S. Principal States of Dynamic Functional Connectivity Reveal the Link Between Resting-State and Task-State Brain: An fMRI Study. Int J Neural Syst 2018; 28:1850002. [PMID: 29607681 DOI: 10.1142/s0129065718500028] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain’s dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter “dwell time” implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a “default mode” in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique insights in understanding how the brain reorganizes itself during rest and task states, and the ways in which the brain adaptively responds to the cognitive requirements of tasks.
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Affiliation(s)
- Lin Cheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
- School of Biomedical Engineering, Science & Health Systems, Drexel University, Philadelphia, Pennsylvania, USA
| | - Yang Zhu
- Department of Neurology, Shanghai Second People’s Hospital, Shanghai 200011, P. R. China
| | - Junfeng Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
| | - Lifu Deng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
| | - Naying He
- Department of Radiology, Rui Jin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P. R. China
| | - Yang Yang
- Department of Neurology, Shanghai Second People’s Hospital, Shanghai 200011, P. R. China
| | - Huawei Ling
- Department of Radiology, Rui Jin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P. R. China
| | - Hasan Ayaz
- School of Biomedical Engineering, Science & Health Systems, Drexel University, Philadelphia, Pennsylvania, USA
| | - Yi Fu
- Department of Neurology & Institute of Neurology, Rui Jin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P. R. China
| | - Shanbao Tong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, P. R. China
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Rangaprakash D, Wu GR, Marinazzo D, Hu X, Deshpande G. Parameterized hemodynamic response function data of healthy individuals obtained from resting-state functional MRI in a 7T MRI scanner. Data Brief 2018; 17:1175-1179. [PMID: 29876476 PMCID: PMC5988211 DOI: 10.1016/j.dib.2018.01.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 12/22/2017] [Accepted: 01/02/2018] [Indexed: 01/10/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI), being an indirect measure of brain activity, is mathematically defined as a convolution of the unmeasured latent neural signal and the hemodynamic response function (HRF). The HRF is known to vary across the brain and across individuals, and it is modulated by neural as well as non-neural factors. Three parameters characterize the shape of the HRF, which is obtained by performing deconvolution on resting-state fMRI data: response height, time-to-peak and full-width at half-max. The data provided here, obtained from 47 healthy adults, contains these three HRF parameters at every voxel in the brain, as well as HRF parameters from the default-mode network (DMN). In addition, we have provided functional connectivity (FC) data from the same DMN regions, obtained for two cases: data with deconvolution (HRF variability minimized) and data with no deconvolution (HRF variability corrupted). This would enable researchers to compare regional changes in HRF with corresponding FC differences, to assess the impact of HRF variability on FC. Importantly, the data was obtained in a 7T MRI scanner. While most fMRI studies are conducted at lower field strengths, like 3T, ours is the first study to report HRF data obtained at 7T. FMRI data at ultra-high fields contains larger contributions from small vessels, consequently HRF variability is lower for small vessels at higher field strengths. This implies that findings made from this data would be more conservative than from data acquired at lower fields, such as 3T. Results obtained with this data and further interpretations are available in our recent research study (Rangaprakash et al., in press) [1]. This is a valuable dataset for studying HRF variability in conjunction with FC, and for developing the HRF profile in healthy individuals, which would have direct implications for fMRI data analysis, especially resting-state connectivity modeling. This is the first public HRF data at 7T.
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Affiliation(s)
- D. Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | - Guo-Rong Wu
- Department of Data Analysis, University of Ghent, Ghent, Belgium
- Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China
| | | | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
- Department of Psychology, Auburn University, Auburn, AL, USA
- Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, AL, USA
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11
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Jiang S, Luo C, Gong J, Peng R, Ma S, Tan S, Ye G, Dong L, Yao D. Aberrant Thalamocortical Connectivity in Juvenile Myoclonic Epilepsy. Int J Neural Syst 2017; 28:1750034. [PMID: 28830309 DOI: 10.1142/s0129065717500344] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The purpose of this study was to investigate the functional connectivity (FC) of thalamic subdivisions in patients with juvenile myoclonic epilepsy (JME). Resting state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data were acquired from 22 JME and 25 healthy controls. We first divided the thalamus into eight subdivisions by performing independent component analysis on tracking fibers and clustering thalamus-related FC maps. We then analyzed abnormal FC in each subdivision in JME compared with healthy controls, and we investigated their associations with clinical features. Eight thalamic sub-regions identified in the current study showed unbalanced thalamic FC in JME: decreased FC with the superior frontal gyrus and enhanced FC with the supplementary motor area in the posterior thalamus increased thalamic FC with the salience network (SN) and reduced FC with the default mode network (DMN). Abnormalities in thalamo-prefrontocortical networks might be related to the propagation of generalized spikes with frontocentral predominance in JME, and the network connectivity differences with the SN and DMN might be implicated in emotional and cognitive defects in JME. JME was also associated with enhanced FC among thalamic sub-regions and with the basal ganglia and cerebellum, suggesting the regulatory role of subcortical nuclei and the cerebellum on the thalamo-cortical circuit. Additionally, increased FC with the pallidum was positive related with the duration of disease. The present study provides emerging evidence of FC to understand that specific thalamic subdivisions contribute to the abnormalities of thalamic-cortical networks in JME. Moreover, the posterior thalamus could play a crucial role in generalized epileptic activity in JME.
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Affiliation(s)
- S. Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - C. Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - J. Gong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - R. Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - S. Ma
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Neurology Department, Sichuan Provincial People’s Hospital, The affiliated Hospital of University of Electronic Science and Technology of China, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - S. Tan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- Neurology Department, Sichuan Provincial People’s Hospital, The affiliated Hospital of University of Electronic Science and Technology of China, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - G. Ye
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - L. Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
| | - D. Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
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Cheng L, Zhu H, Zhu Y, He N, Yang Y, Ling H, Tong S, Fu Y, Sun J. Decreased variability of dynamic phase synchronization in brain networks during hand movement. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:4155-4158. [PMID: 29060812 DOI: 10.1109/embc.2017.8037771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Dynamic functional connectivity analysis, a rapidly growing method, has been demonstrated to provide new spatiotemporal information about how brain motor network would reorganize from rest to motor tasks. Phase synchronization analysis, which has been widely applied in EEG-based FC analysis, is a promising alternative method in dynamic FC analysis. In this study, fMRI data were recorded from 28 healthy volunteers when they are resting and performing hand closing and opening (HCO) task. Dynamic FC was estimated by phase synchronization analysis. In addition, functional connectivity variability (FCV) was compared between rest and HCO to investigate the modulation induced by motor task on dynamics of motor-related FC and network. Results showed that the FCVs in network-of-interest, including default-mode network and motor network, decreased during HCO comparing with rest. Our results demonstrated that the unconstrained mental activities, which resulted in high FCV during rest, would focus on motor execution during HCO and thus led to decreased FCV during HCO.
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Graña M, Ozaeta L, Chyzhyk D. Resting State Effective Connectivity Allows Auditory Hallucination Discrimination. Int J Neural Syst 2017; 27:1750019. [DOI: 10.1142/s0129065717500198] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex. In this paper, we consider two methods to analyze resting state fMRI (rs-fMRI) data, in order to measure effective connections between the brain regions involved in the AH generation process. These measures are the Dynamic Causal Modeling (DCM) cross-covariance function (CCF) coefficients, and the partially directed coherence (PDC) coefficients derived from Granger Causality (GC) analysis. Effective connectivity measures are treated as input classifier features to assess their significance by means of cross-validation classification accuracy results in a wrapper feature selection approach. Experimental results using Support Vector Machine (SVM) classifiers on an rs-fMRI dataset of schizophrenia patients with and without a history of AH confirm that the main regions identified in the AH generative mechanism hypothesis have significant effective connection values, under both DCM and PDC evaluation.
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Affiliation(s)
- Manuel Graña
- Computational Intelligence Group, University of the Basque Country, UPV/EHU, Spain
- ACPySS, San Sebastian, Spain
| | - Leire Ozaeta
- Computational Intelligence Group, University of the Basque Country, UPV/EHU, Spain
| | - Darya Chyzhyk
- Computational Intelligence Group, University of the Basque Country, UPV/EHU, Spain
- CISE Department, University of Florida, Gainesville, USA
- ACPySS, San Sebastian, Spain
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14
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Gao ZK, Cai Q, Yang YX, Dong N, Zhang SS. Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG. Int J Neural Syst 2017; 27:1750005. [DOI: 10.1142/s0129065717500058] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant importance. Combining adaptive optimal kernel time-frequency representation and visibility graph, we develop a novel method for detecting epileptic seizure from EEG signals. We construct complex networks from EEG signals recorded from healthy subjects and epilepsy patients. Then we employ clustering coefficient, clustering coefficient entropy and average degree to characterize the topological structure of the networks generated from different brain states. In addition, we combine energy deviation and network measures to recognize healthy subjects and epilepsy patients, and further distinguish brain states during seizure free interval and epileptic seizures. Three different experiments are designed to evaluate the performance of our method. The results suggest that our method allows a high-accurate classification of epileptiform EEG signals.
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Affiliation(s)
- Zhong-Ke Gao
- School of Electrical Engineering and Automation, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China, Tianjin 300072, China
| | - Qing Cai
- School of Electrical Engineering and Automation, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China, Tianjin 300072, China
| | - Yu-Xuan Yang
- School of Electrical Engineering and Automation, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China, Tianjin 300072, China
| | - Na Dong
- School of Electrical Engineering and Automation, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China, Tianjin 300072, China
| | - Shan-Shan Zhang
- School of Electrical Engineering and Automation, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin, China, Tianjin 300072, China
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15
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Organization of the intrinsic functional network in the cervical spinal cord: A resting state functional MRI study. Neuroscience 2016; 336:30-38. [PMID: 27590264 DOI: 10.1016/j.neuroscience.2016.08.042] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2016] [Revised: 08/22/2016] [Accepted: 08/24/2016] [Indexed: 12/29/2022]
Abstract
Resting state functional magnetic resonance imaging (rsfMRI) has been extensively applied to investigate the organization of functional networks in the brain. As an essential part of the central nervous system (CNS), the spinal cord has not been well explored about its intrinsic functional network. In this study, we aim to thoroughly investigate the characteristics of the intrinsic functional network in the spinal cord using rsfMRI. Functional connectivity and graph theory analysis were employed to evaluate the organization of the functional network, including its topology and network communication properties. Furthermore, the reproducibility of rsfMRI analysis on the spinal cord was also examined by intra-class correlation (ICC). Comprehensive evaluation of the intrinsic functional organization presented a non-uniform distribution of topological characteristics of the functional network, in which the upper levels (C2 and C3 vertebral levels) of the cervical spinal cord showed high levels of connectivity. The present results revealed the significance of the upper cervical cord in the intrinsic functional network of the human cervical spinal cord. In addition, this study demonstrated the efficiency of the cervical spinal cord functional network and the reproducibility of rsfMRI analysis on the spinal cord was also confirmed. As knowledge expansion of intrinsic functional network from the brain to the spinal cord, this study shed light on the organization of the spinal cord functional network in both normal development and clinical disorders.
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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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17
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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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18
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Deshpande G, Wang P, Rangaprakash D, Wilamowski B. Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data. IEEE TRANSACTIONS ON CYBERNETICS 2015; 45:2668-2679. [PMID: 25576588 DOI: 10.1109/tcyb.2014.2379621] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Automated recognition and classification of brain diseases are of tremendous value to society. Attention deficit hyperactivity disorder (ADHD) is a diverse spectrum disorder whose diagnosis is based on behavior and hence will benefit from classification utilizing objective neuroimaging measures. Toward this end, an international competition was conducted for classifying ADHD using functional magnetic resonance imaging data acquired from multiple sites worldwide. Here, we consider the data from this competition as an example to illustrate the utility of fully connected cascade (FCC) artificial neural network (ANN) architecture for performing classification. We employed various directional and nondirectional brain connectivity-based methods to extract discriminative features which gave better classification accuracy compared to raw data. Our accuracy for distinguishing ADHD from healthy subjects was close to 90% and between the ADHD subtypes was close to 95%. Further, we show that, if properly used, FCC ANN performs very well compared to other classifiers such as support vector machines in terms of accuracy, irrespective of the feature used. Finally, the most discriminative connectivity features provided insights about the pathophysiology of ADHD and showed reduced and altered connectivity involving the left orbitofrontal cortex and various cerebellar regions in ADHD.
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Wang XH, Li L, Xu T, Ding Z. Investigating the Temporal Patterns within and between Intrinsic Connectivity Networks under Eyes-Open and Eyes-Closed Resting States: A Dynamical Functional Connectivity Study Based on Phase Synchronization. PLoS One 2015; 10:e0140300. [PMID: 26469182 PMCID: PMC4607488 DOI: 10.1371/journal.pone.0140300] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 09/23/2015] [Indexed: 01/19/2023] Open
Abstract
The brain active patterns were organized differently under resting states of eyes open (EO) and eyes closed (EC). The altered voxel-wise and regional-wise resting state active patterns under EO/EC were found by static analysis. More importantly, dynamical spontaneous functional connectivity has been observed in the resting brain. To the best of our knowledge, the dynamical mechanisms of intrinsic connectivity networks (ICNs) under EO/EC remain largely unexplored. The goals of this paper were twofold: 1) investigating the dynamical intra-ICN and inter-ICN temporal patterns during resting state; 2) analyzing the altered dynamical temporal patterns of ICNs under EO/EC. To this end, a cohort of healthy subjects with scan conditions of EO/EC were recruited from 1000 Functional Connectomes Project. Through Hilbert transform, time-varying phase synchronization (PS) was applied to evaluate the inter-ICN synchrony. Meanwhile, time-varying amplitude was analyzed as dynamical intra-ICN temporal patterns. The results found six micro-states of inter-ICN synchrony. The medial visual network (MVN) showed decreased intra-ICN amplitude during EC relative to EO. The sensory-motor network (SMN) and auditory network (AN) exhibited enhanced intra-ICN amplitude during EC relative to EO. Altered inter-ICN PS was found between certain ICNs. Particularly, the SMN and AN exhibited enhanced PS to other ICNs during EC relative to EO. In addition, the intra-ICN amplitude might influence the inter-ICN synchrony. Moreover, default mode network (DMN) might play an important role in information processing during EO/EC. Together, the dynamical temporal patterns within and between ICNs were altered during different scan conditions of EO/EC. Overall, the dynamical intra-ICN and inter-ICN temporal patterns could benefit resting state fMRI-related research, and could be potential biomarkers for human functional connectome.
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Affiliation(s)
- Xun-Heng Wang
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
- * E-mail: (XHW); (LL)
| | - Lihua Li
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
- * E-mail: (XHW); (LL)
| | - Tao Xu
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Zhongxiang Ding
- Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou,310014, China
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20
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Luo C, Zhang Y, Cao W, Huang Y, Yang F, Wang J, Tu S, Wang X, Yao D. Altered Structural and Functional Feature of Striato-Cortical Circuit in Benign Epilepsy with Centrotemporal Spikes. Int J Neural Syst 2015; 25:1550027. [PMID: 26126612 DOI: 10.1142/s0129065715500276] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Benign epilepsy with centrotemporal spikes (BECT) is the most common form of childhood idiopathic focal epilepsy syndrome. We investigated quantitative evidence regarding brain morphology and functional connectivity features to provide insight into the neuroanatomical foundation of this disorder, using high resolution T1-weighted magnetic resonance imaging (MRI) and resting state functional MRI in 21 patients with BECT and in 20 healthy children. The functional connectivity analysis, seeded at the regions with altered gray-matter (GM) volume in voxel-based morphometry (VBM) analysis, was further performed. Then, the observed structural and functional alteration were investigated for their association with the clinical and behavior manifestations. The increased GM volume in the striatum and fronto-temporo-parietal cortex (striato-cortical circuit) was observed in BECT. The decreased connections were found among the motor network and frontostriatal loop, and between the default mode network (DMN) and language regions. Additionally, the GM of striatum was negatively correlated with age at epilepsy onset. The current observations may contribute to the understanding of the altered structural and functional feature of striato-cortical circuit in patients with BECT. The findings also implied alterations of the motor network and DMN, which were associated with the epileptic activity in patients with BECT. This further suggested that the onset of BECT might have enduring structural and functional effects on brain maturation.
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Affiliation(s)
- Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging, Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yaodan Zhang
- Neurology Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Weifang Cao
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging, Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yue Huang
- Pediatric Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Fei Yang
- Neurology Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Jianjun Wang
- Pediatric Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Shipeng Tu
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging, Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiaoming Wang
- Neurology Department, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, NanChong 637007, China
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging, Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, China
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21
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Aydin S, Arica N, Ergul E, Tan O. Classification of Obsessive Compulsive Disorder by EEG Complexity and Hemispheric Dependency Measurements. Int J Neural Syst 2015; 25:1550010. [DOI: 10.1142/s0129065715500100] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In the present study, both single channel electroencephalography (EEG) complexity and two channel interhemispheric dependency measurements have newly been examined for classification of patients with obsessive–compulsive disorder (OCD) and controls by using support vector machine classifiers. Three embedding entropy measurements (approximate entropy, sample entropy, permutation entropy (PermEn)) are used to estimate single channel EEG complexity for 19-channel eyes closed cortical measurements. Mean coherence and mutual information are examined to measure the level of interhemispheric dependency in frequency and statistical domain, respectively for eight distinct electrode pairs placed on the scalp with respect to the international 10–20 electrode placement system. All methods are applied to short EEG segments of 2 s. The classification performance is measured 20 times with different 2-fold cross-validation data for both single channel complexity features (19 features) and interhemispheric dependency features (eight features). The highest classification accuracy of 85 ±5.2% is provided by PermEn at prefrontal regions of the brain. Even if the classification success do not provided by other methods as high as PermEn, the clear differences between patients and controls at prefrontal regions can also be obtained by using other methods except coherence. In conclusion, OCD, defined as illness of orbitofronto-striatal structures [Beucke et al., JAMA Psychiatry 70 (2013) 619–629; Cavedini et al., Psychiatry Res. 78 (1998) 21–28; Menzies et al., Neurosci. Biobehav. Rev. 32(3) (2008) 525–549], is caused by functional abnormalities in the pre-frontal regions. Particularly, patients are characterized by lower EEG complexity at both pre-frontal regions and right fronto-temporal locations. Our results are compatible with imaging studies that define OCD as a sub group of anxiety disorders exhibited a decreased complexity (such as anorexia nervosa [Toth et al., Int. J. Psychophysiol. 51(3) (2004) 253–260] and panic disorder [Bob et al., Physiol. Res. 55 (2006) S113–S119]).
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Affiliation(s)
- Serap Aydin
- Biomedical Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey
| | - Nafiz Arica
- Software Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey
| | - Emrah Ergul
- Electronics and Communications Engineering Department, Kocaeli University, Kocaeli, Turkey
| | - Oğuz Tan
- Uskudar University, Neuropsychiatry Health, Practice and Research Center Istanbul, Turkey
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22
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Chyzhyk D, Graña M, Öngür D, Shinn AK. Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI. Int J Neural Syst 2015; 25:1550007. [PMID: 25753600 DOI: 10.1142/s0129065715500070] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that computational intelligence approaches can provide robust tools for uncovering subtleties in complex neuroimaging data, and have the potential to advance the search for more neuroscience-based criteria for classifying mental illness in psychiatry research.
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Affiliation(s)
- Darya Chyzhyk
- Computational Intelligence Group, Universidad del Pais Vasco (UPV/EHU), San Sebastian 20018, Spain
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23
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Ferdowsi S, Sanei S, Abolghasemi V. A Predictive Modeling Approach to Analyze Data in EEG–fMRI Experiments. Int J Neural Syst 2015; 25:1440008. [DOI: 10.1142/s0129065714400085] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, a novel technique based on blind source extraction (BSE) using linear prediction is proposed to extract rolandic beta rhythm from electroencephalogram (EEG) recorded in a simultaneous EEG–fMRI experiment. We call this method CLP–BSE standing for constrained-linear-prediction BSE. Extracting event-related oscillations is a crucial task due to nonphase-locked nature and inter-trial variability of this event. The main objective of this work is to extract rolandic beta rhythm to measure event-related synchronization (ERS) with acceptable signal-to-noise ratio (SNR). The extracted rhythm is utilized for constructing a regressor to analyze functional magnetic resonance imaging (fMRI). The proposed method is a semi-blind technique which uses a spatio-temporal constraint for beta rhythm extraction. This constraint is derived from recorded EEG signals based on the prior knowledge about the frequency and location of the source of interest. The main reason of employing linear prediction as an effective algorithm to extract the EEG rhythm is the ability of extracting sources which have specific temporal structure. Performance of the proposed method is evaluated using both synthetic and real EEG data. The obtained results show that the proposed technique is able to extract ERS effectively. The maximum percentage of ERS obtained by filtering is 152% while the obtained ERS by CLP–BSE is 214%. In another experiment, the extracted event-related oscillations in beta band are used to make the necessary regressor for fMRI analysis. The results of EEG–fMRI coregistration confirm that there are correlation between the extracted rolandic beta rhythm and simultaneously recorded fMRI. This conclude that, the results of EEG–fMRI combination support the reliability of CLP–BSE output.
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Affiliation(s)
- Saideh Ferdowsi
- Faculty of Electrical Engineering, Shahrood University, Shahrood 3619995161, Iran
| | - Saeid Sanei
- Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Vahid Abolghasemi
- Faculty of Electrical Engineering, Shahrood University, Shahrood 3619995161, Iran
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24
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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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25
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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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26
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Detecting protein atom correlations using correlation of probability of recurrence. Proteins 2014; 82:2180-9. [DOI: 10.1002/prot.24574] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Accepted: 03/29/2014] [Indexed: 11/07/2022]
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27
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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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