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Singh J, Singh S, Gupta S, Chavan BS. Cognitive Remediation and Schizophrenia: Effects on Brain Complexity. Neurosci Lett 2023; 808:137268. [PMID: 37100222 DOI: 10.1016/j.neulet.2023.137268] [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: 09/22/2021] [Revised: 04/10/2023] [Accepted: 04/20/2023] [Indexed: 04/28/2023]
Abstract
The objective of this study is to investigate nonlinear neural dynamics of chronic patients with schizophrenia following 3 months of cognitive remediation and to find correlations with neuropsychological measures of cognition. Twenty nine patients were randomized to Cognitive Training (CT) and Treatment as Usual (TAU) group. The system complexity is estimated by Correlation Dimension (D2) and Largest Lyapunov Exponent (LLE) from the reconstructed attractor of the underlying system. Significant increase in dimensional complexity (D2) over time is observed in prefrontal and medial frontal-central regions in eyes open and arithmetic condition; and posterior parietal-occipital region under eyes closed after 3 months. Dynamical complexity (LLE) significantly decreased over time in medial left central region under eyes closed and eyes open condition; prefrontal region in eyes open and lateral right temporal region in arithmetic condition. Interaction is significant for medial left central region with TAU group exhibiting greater decrease in LLE compared to CT group. The CT group showed significant correlation of increased D2 with focused attention. In this study it is found that patients with schizophrenia exhibit higher dimensional and lower dynamical complexity over time indicating improvement in neurodynamics of underlying physiological system.
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Affiliation(s)
- Jaskirat Singh
- Computational Neuroscience Lab, UIET, Panjab University, Chandigarh Pincode: 160014, India
| | - Sukhwinder Singh
- Computational Neuroscience Lab, UIET, Panjab University, Chandigarh Pincode: 160014, India.
| | - Savita Gupta
- Computational Neuroscience Lab, UIET, Panjab University, Chandigarh Pincode: 160014, India.
| | - B S Chavan
- Department of Psychiatry, Government Medical College and Hospital, Sector 32, Chandigarh, Pincode:160032, India.
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2
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Balasubramanian K, Ramya K, Gayathri Devi K. Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals. Cogn Neurodyn 2023; 17:133-151. [PMID: 36704627 PMCID: PMC9871147 DOI: 10.1007/s11571-022-09817-y] [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: 12/07/2021] [Revised: 03/23/2022] [Accepted: 04/27/2022] [Indexed: 02/01/2023] Open
Abstract
Schizophrenia is a chronic mental disorder that impairs a person's thinking capacity, feelings and emotions, behavioural traits, etc., Emotional distortions, delusions, hallucinations, and incoherent speech are all some of the symptoms of schizophrenia, and cause disruption of routine activities. Computer-assisted diagnosis of schizophrenia is significantly needed to give its patients a higher quality of life. Hence, an improved adaptive neuro-fuzzy inference system based on the Hybrid Grey Wolf-Bat Algorithm for accurate prediction of schizophrenia from multi-channel EEG signals is presented in this study. The EEG signals are pre-processed using a Butterworth band pass filter and wICA initially, from which statistical, time-domain, frequency-domain, and spectral features are extracted. Discriminating features are selected using the ReliefF algorithm and are then forwarded to ANFIS for classification into either schizophrenic or normal. ANFIS is optimized by the Hybrid Grey Wolf-Bat Algorithm (HWBO) for better efficiency. The method is experimented on two separate EEG datasets-1 and 2, demonstrating an accuracy of 99.54% and 99.35%, respectively, with appreciable F1-score and MCC. Further experiments reveal the efficiency of the Hybrid Wolf-Bat algorithm in optimizing the ANFIS parameters when compared with traditional ANFIS model and other proven algorithms like genetic algorithm-ANFIS, particle optimization-ANFIS, crow search optimization algorithm-ANFIS and ant colony optimization algorithm-ANFIS, showing high R2 value and low RSME value. To provide a bias free classification, tenfold cross validation is performed which produced an accuracy of 97.8% and 98.5% on the two datasets respectively. Experimental outcomes demonstrate the superiority of the Hybrid Grey Wolf-Bat Algorithm over the similar techniques in predicting schizophrenia.
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Affiliation(s)
| | - K. Ramya
- PA College of Engineering and Technology, Pollachi, India
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3
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Schizophrenia Diagnosis by Weighting the Entropy Measures of the Selected EEG Channel. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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4
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Walter J. Consciousness as a multidimensional phenomenon: implications for the assessment of disorders of consciousness. Neurosci Conscious 2021; 2021:niab047. [PMID: 34992792 PMCID: PMC8716840 DOI: 10.1093/nc/niab047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 10/19/2021] [Accepted: 12/10/2021] [Indexed: 01/10/2023] Open
Abstract
Disorders of consciousness (DoCs) pose a significant clinical and ethical challenge because they allow for complex forms of conscious experience in patients where intentional behaviour and communication are highly limited or non-existent. There is a pressing need for brain-based assessments that can precisely and accurately characterize the conscious state of individual DoC patients. There has been an ongoing research effort to develop neural measures of consciousness. However, these measures are challenging to validate not only due to our lack of ground truth about consciousness in many DoC patients but also because there is an open ontological question about consciousness. There is a growing, well-supported view that consciousness is a multidimensional phenomenon that cannot be fully described in terms of the theoretical construct of hierarchical, easily ordered conscious levels. The multidimensional view of consciousness challenges the utility of levels-based neural measures in the context of DoC assessment. To examine how these measures may map onto consciousness as a multidimensional phenomenon, this article will investigate a range of studies where they have been applied in states other than DoC and where more is known about conscious experience. This comparative evidence suggests that measures of conscious level are more sensitive to some dimensions of consciousness than others and cannot be assumed to provide a straightforward hierarchical characterization of conscious states. Elevated levels of brain complexity, for example, are associated with conscious states characterized by a high degree of sensory richness and minimal attentional constraints, but are suboptimal for goal-directed behaviour and external responsiveness. Overall, this comparative analysis indicates that there are currently limitations to the use of these measures as tools to evaluate consciousness as a multidimensional phenomenon and that the relationship between these neural signatures and phenomenology requires closer scrutiny.
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Affiliation(s)
- Jasmine Walter
- Cognition and Philosophy Lab, 21 Chancellor’s Walk, Monash University, Melbourne, VIC 3800, Australia
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5
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Singh K, Singh S, Malhotra J. Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proc Inst Mech Eng H 2020; 235:167-184. [PMID: 33124526 DOI: 10.1177/0954411920966937] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.
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Affiliation(s)
- Kuldeep Singh
- Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Sukhjeet Singh
- Machinery Fault Diagnostics & Signal Processing Laboratory, Department of Mechanical Engineering, University Institute of Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Jyoteesh Malhotra
- Department of Electronics and Communication Engineering, Guru Nanak Dev University, Jalandhar, Punjab, India
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6
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Tai YC, Lin SH, Chen KC, Lee IH, Chen PS, Lee LT, Tsai HC, Yeh TL, Yang YK. Heart Rate Variability with Deep Breathing in Drug-Naïve Patients with Schizophrenia. Appl Psychophysiol Biofeedback 2020; 45:275-282. [PMID: 32997269 DOI: 10.1007/s10484-020-09489-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2020] [Indexed: 01/03/2023]
Abstract
Abnormal autonomic nervous system (ANS) function may result in poor outcomes in patients with schizophrenia. Altered cardio-respiratory coupling, which indicates suppression of vagal activity, was identified as an important trait in patients with schizophrenia and their unaffected relatives. Heart rate variability (HRV) in standardized bedside reflex tests has been studied, mostly in medicated patients with schizophrenia whose ANS function could be influenced by medication. Our study aimed to explore the autonomic function differences between drug-naïve patients with schizophrenia and healthy individuals during challenge tests combining respiration and HRV analysis. Forty-two drug-naïve patients with schizophrenia were matched with 42 healthy controls in terms of age and gender. Their beat-to-beat blood pressure and heart rate were monitored in the supine position as a survey of ANS function, and the mean heart rate range (MHRR) was measured under deep-breathing challenge. A decreased MHRR, a sensitive sign indicating an impaired parasympathetic response, during the deep-breathing challenge among the drug-naïve patients with schizophrenia was found. Drug-naïve patients with schizophrenia may have a parasympathetic dysfunction in the early stages of schizophrenia before medication is introduced, which could be considered a neurobiological marker in the pathophysiology of schizophrenia.
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Affiliation(s)
- Ying Chun Tai
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, North Dist., Tainan, 704, Taiwan
| | - Shih-Hsien Lin
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, North Dist., Tainan, 704, Taiwan.,Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Kao Chin Chen
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, North Dist., Tainan, 704, Taiwan.
| | - I Hui Lee
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, North Dist., Tainan, 704, Taiwan
| | - Po See Chen
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, North Dist., Tainan, 704, Taiwan.,Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Lan-Ting Lee
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, North Dist., Tainan, 704, Taiwan
| | - Hsin Chun Tsai
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, North Dist., Tainan, 704, Taiwan
| | - Tzung Lieh Yeh
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, North Dist., Tainan, 704, Taiwan
| | - Yen Kuang Yang
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng Li Road, North Dist., Tainan, 704, Taiwan.,Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Psychiatry, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan
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7
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Wen Yean C, Wan Ahmad WK, Mustafa WA, Murugappan M, Rajamanickam Y, Adom AH, Omar MI, Zheng BS, Junoh AK, Razlan ZM, Bakar SA. An Emotion Assessment of Stroke Patients by Using Bispectrum Features of EEG Signals. Brain Sci 2020; 10:E672. [PMID: 32992930 PMCID: PMC7601112 DOI: 10.3390/brainsci10100672] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 09/11/2020] [Accepted: 09/22/2020] [Indexed: 01/06/2023] Open
Abstract
Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8-13) Hz, beta (13-30) Hz and gamma (30-49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.
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Affiliation(s)
- Choong Wen Yean
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia; (C.W.Y.); (M.I.O.); (B.S.Z.)
| | - Wan Khairunizam Wan Ahmad
- Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia; (W.A.M.); (A.H.A.)
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia; (W.A.M.); (A.H.A.)
| | - Murugappan Murugappan
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Doha Area, 7th Ring Road, Kuwait City 13133, Kuwait;
| | - Yuvaraj Rajamanickam
- School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), 50 Nanyang Avenue, Singapore 639798, Singapore;
| | - Abdul Hamid Adom
- Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia; (W.A.M.); (A.H.A.)
| | - Mohammad Iqbal Omar
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia; (C.W.Y.); (M.I.O.); (B.S.Z.)
| | - Bong Siao Zheng
- Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia; (C.W.Y.); (M.I.O.); (B.S.Z.)
| | - Ahmad Kadri Junoh
- Institute of Engineering Mathematics, Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia;
| | - Zuradzman Mohamad Razlan
- Faculty of Mechanical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia; (Z.M.R.); (S.A.B.)
| | - Shahriman Abu Bakar
- Faculty of Mechanical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia; (Z.M.R.); (S.A.B.)
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8
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Iglesias-Parro S, Soriano MF, Prieto M, Rodríguez I, Aznarte JI, Ibáñez-Molina AJ. Introspective and Neurophysiological Measures of Mind Wandering in Schizophrenia. Sci Rep 2020; 10:4833. [PMID: 32179815 PMCID: PMC7076020 DOI: 10.1038/s41598-020-61843-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 03/04/2020] [Indexed: 01/26/2023] Open
Abstract
Patients with schizophrenia have often been considered to be “in their own world”. However, this casual observation has not been proven by scientific evidence so far. This can be explained because scientific research has usually addressed cognition related to the processing of external stimuli, but only recently have efforts been made to explain thoughts, images and feelings not directly related to the external environment. This internally directed cognition has been called mind wandering. In this paper, we have explored mind wandering in schizophrenia under the hypothesis that a predominance of mind wandering would be a core dysfunction in this disorder. To this end, we collected verbal reports and measured electrophysiological signals from patients with schizophrenia spectrum disorders and matched healthy controls while they were presented with segments of films. The results showed that mind wandering was more frequent in patients than in controls. This higher frequency of mind wandering did not correlate with deficits in attentional, memory or executive functioning. In addition, mind wandering in patients was characterized by a different pattern of Electroencephalography (EEG) complexity in patients than in controls, leading to the suggestion that mind wandering in schizophrenia could be of a different nature. These findings could have relevant implications for the conceptualization of this severe mental disorder.
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Affiliation(s)
| | - M F Soriano
- Mental Health Unit, St. Agustín Universitary Hospital, Linares, Jaén, Spain
| | - M Prieto
- Psychology Department, University of Jaén, Jaén, Spain
| | - I Rodríguez
- Psychology Department, University of Jaén, Jaén, Spain
| | - J I Aznarte
- Mental Health Unit, St. Agustín Universitary Hospital, Linares, Jaén, Spain
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9
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Harmah DJ, Li C, Li F, Liao Y, Wang J, Ayedh WMA, Bore JC, Yao D, Dong W, Xu P. Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy. Front Comput Neurosci 2020; 13:85. [PMID: 31998105 PMCID: PMC6966771 DOI: 10.3389/fncom.2019.00085] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 12/04/2019] [Indexed: 12/31/2022] Open
Abstract
People living with schizophrenia (SCZ) experience severe brain network deterioration. The brain is constantly fizzling with non-linear causal activities measured by electroencephalogram (EEG) and despite the variety of effective connectivity methods, only few approaches can quantify the direct non-linear causal interactions. To circumvent this problem, we are motivated to quantitatively measure the effective connectivity by multivariate transfer entropy (MTE) which has been demonstrated to be able to capture both linear and non-linear causal relationships effectively. In this work, we propose to construct the EEG effective network by MTE and further compare its performance with the Granger causal analysis (GCA) and Bivariate transfer entropy (BVTE). The simulation results quantitatively show that MTE outperformed GCA and BVTE under varied signal-to-noise conditions, edges recovered, sensitivity, and specificity. Moreover, its applications to the P300 task EEG of healthy controls (HC) and SCZ patients further clearly show the deteriorated network interactions of SCZ, compared to that of the HC. The MTE provides a novel tool to potentially deepen our knowledge of the brain network deterioration of the SCZ.
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Affiliation(s)
- Dennis Joe Harmah
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Cunbo Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiuju Wang
- Institute of Mental Health, Peking University Sixth Hospital, National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Walid M. A. Ayedh
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Joyce Chelangat Bore
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Wentian Dong
- Institute of Mental Health, Peking University Sixth Hospital, National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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10
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Goshvarpour A, Goshvarpour A. Schizophrenia diagnosis using innovative EEG feature-level fusion schemes. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2020; 43:10.1007/s13246-019-00839-1. [PMID: 31898243 DOI: 10.1007/s13246-019-00839-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 12/21/2019] [Indexed: 11/25/2022]
Abstract
Electroencephalogram (EEG) has become a practical tool for monitoring and diagnosing pathological/psychological brain states. To date, an increasing number of investigations considered differences between brain dynamic of patients with schizophrenia and healthy controls. However, insufficient studies have been performed to provide an intelligent and accurate system that detects the schizophrenia using EEG signals. This paper concerns this issue by providing new feature-level fusion algorithms. Firstly, we analyze EEG dynamics using three well-known nonlinear measures, including complexity (Cx), Higuchi fractal dimension (HFD), and Lyapunov exponents (Lya). Next, we propose some innovative feature-level fusion strategies to combine the information of these indices. We evaluate the effect of the classifier parameter (σ) adjustment and the cross-validation partitioning criteria on classification accuracy. The performance of EEG classification using combined features was compared with the non-combined attributes. Experimental results showed higher classification accuracy when feature-level features were utilized, compared to when each feature was used individually or all fed to the classifier simultaneously. Using the proposed algorithm, the classification accuracy increased up to 100%. These results establish the suggested framework as a superior scheme compared to the state-of-the-art EEG schizophrenia diagnosis tool.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, PO. BOX: 91735-553, Rezvan Campus (Female Students), Phalestine Sq., Mashhad, Razavi Khorasan, Iran.
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11
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Murphy M, Stickgold R, Öngür D. Electroencephalogram Microstate Abnormalities in Early-Course Psychosis. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 5:35-44. [PMID: 31543456 DOI: 10.1016/j.bpsc.2019.07.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 07/11/2019] [Accepted: 07/16/2019] [Indexed: 12/25/2022]
Abstract
BACKGROUND Microstates are periods of characteristic electroencephalographic signal topography that are related to activity in brain networks. Previous work has identified abnormal microstate parameters in individuals with psychotic disorders. We combined microstate analysis with sample entropy analysis to study the dynamics of resting-state networks in patients with early-course psychosis. METHODS We used microstate analysis to transform resting-state high-density electroencephalography data from 22 patients with early-course psychosis and 22 healthy control subjects into sequences of characteristic scalp topographies. Sample entropy was used to calculate the complexity of microstate sequences across a range of template lengths. RESULTS Patients and control subjects produced similar sets of 4 microstates that agree with a widely reported canonical set (A, B, C, and D). Relative to control subjects, patients had decreased frequency of microstate A. In control subjects, sample entropy decreased as template length increased, suggesting that sequence of microstate transitions is self-similar across multiple transitions. In patients, sample entropy did not decrease, suggesting a lack of self-similarity in transition sequences. This finding was unrelated to data length or microstate topography. Entropy was elevated in unmedicated patients, and it decreased in patients who were administered medication. We identified patterns of transitions between microstates that were overrepresented in control data compared with representation in patient data. CONCLUSIONS Our findings suggest that patients with early-course psychosis have abnormally chaotic transitions between brain networks. This chaos may reflect an underlying abnormality in allocating neural resources and effecting appropriate transitions between distinct activity states in psychosis.
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Affiliation(s)
- Michael Murphy
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Schizophrenia and Bipolar Disorder Research Program, McLean Hospital, Belmont, Massachusetts.
| | - Robert Stickgold
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Dost Öngür
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; Schizophrenia and Bipolar Disorder Research Program, McLean Hospital, Belmont, Massachusetts
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12
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Vaghefi M, Nasrabadi AM, Hashemi Golpayegani SMR, Mohammadi MR, Gharibzadeh S. Nonlinear Analysis of Electroencephalogram Signals while Listening to the Holy Quran. JOURNAL OF MEDICAL SIGNALS & SENSORS 2019; 9:100-110. [PMID: 31316903 PMCID: PMC6601225 DOI: 10.4103/jmss.jmss_37_18] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Background: Electrical activity of the brain, resulting from electrochemical signaling between neurons, is recorded by electroencephalogram (EEG). The neural network has complex behavior at different levels that strongly confirms the nonlinear nature of interactions in the human brain. This study has been designed and implemented with the aim of determining the effects of religious beliefs and the effect of listening to Holy Quran on electrical activity of the brain of the Iranian Persian-speaking Muslim volunteers. Methods: The brain signals of 47 Persian-speaking Muslim volunteers while listening to the Holy Quran consciously, and while listening to the Holy Quran and the Arabic text unconsciously were used. Therefore, due to the nonlinear nature of EEG signals, these signals are studied using approximate entropy, sample entropy, Hurst exponent, and Detrended Fluctuation Analysis. Results: Statistical analysis of the results has shown that listening to the Holy Quran consciously increases approximate entropy and sample entropy, and decreases Hurst Exponent and Detrended Fluctuation Analysis compared to other cases. Conclusion: Consciously listening to the Holy Quran decreases self-similarity and correlation of brain signal and instead increases complexity and dynamicity in the brain.
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Affiliation(s)
- Mahsa Vaghefi
- Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Ali Motie Nasrabadi
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
| | | | - Mohammad Reza Mohammadi
- Department of Child and Adolescent Psychiatry, Tehran University of Medical Sciences, Tehran, Iran
| | - Shahriar Gharibzadeh
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
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Ibáñez-Molina AJ, Lozano V, Soriano MF, Aznarte JI, Gómez-Ariza CJ, Bajo MT. EEG Multiscale Complexity in Schizophrenia During Picture Naming. Front Physiol 2018; 9:1213. [PMID: 30245636 PMCID: PMC6138007 DOI: 10.3389/fphys.2018.01213] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 08/13/2018] [Indexed: 11/13/2022] Open
Abstract
Introduction: Patients with schizophrenia show cognitive deficits that are evident both behaviourally and with EEG recordings. Recent studies have suggested that non-linear analyses of EEG might more adequately reflect the complex, irregular, non-stationary behavior of neural processes than more traditional ERP measures. Non-linear analyses have been mainly applied to EEGs from patients at rest, whereas differences in complexity might be more evident during task performance. Objective: We aimed to investigate changes in non-linear brain dynamics of patients with schizophrenia during cognitive processing. Method: 18 patients and 17 matched healthy controls were asked to name pictures. EEG data were collected at rest and while they were performing a naming task. EEGs were analyzed with the classical Lempel-Ziv Complexity (LZC) and with the Multiscale LZC. Electrodes were grouped in seven regions of interest (ROI). Results: As expected, controls had fewer naming errors than patients. Regarding EEG complexity, the interaction between Group, Task and ROI indicated that patients showed higher complexity values in right frontal regions only at rest, where no differences in complexity between patients and controls were found during the naming task. EEG complexity increased from rest to task in controls in left temporal-parietal regions, while no changes from rest to task were observed in patients. Finally, differences in complexity between patients and controls depended on the frequency bands: higher values of complexity in patients at rest were only observed in fast bands, indicating greater heterogeneity in patients in local dynamics of neuronal assemblies. Conclusion: Consistent with previous studies, schizophrenic patients showed higher complexity than controls in frontal regions at rest. Interestingly, we found different modulations of brain complexity during a simple cognitive task between patients and controls. These data can be interpreted as indicating schizophrenia-related failures to adapt brain functioning to the task, which is reflected in poorer behavioral performance.
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Affiliation(s)
| | - Vanessa Lozano
- Department of Experimental Psychology, University of Granada, Granada, Spain
| | | | | | | | - M T Bajo
- Department of Experimental Psychology, University of Granada, Granada, Spain
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Ibáñez-Molina AJ, Iglesias-Parro S, Escudero J. Differential Effects of Simulated Cortical Network Lesions on Synchrony and EEG Complexity. Int J Neural Syst 2018; 29:1850024. [PMID: 29938549 DOI: 10.1142/s0129065718500247] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of brain activity is the characterization of the synchrony among these areas and the resulting complexity of the system. However, the variety of ways to define and, hence, measure brain synchrony and complexity has sometimes led to inconsistent results. Here, we study the relationship between synchrony and commonly used complexity estimators of electroencephalogram (EEG) activity and we explore how simulated lesions in anatomically based cortical networks would affect key functional measures of activity. We explored this question using different types of neural network lesions while the brain dynamics was modeled with a time-delayed set of 66 Kuramoto oscillators. Each oscillator modeled a region of the cortex (node), and the connectivity and spatial location between different areas informed the creation of a network structure (edges). Each type of lesion consisted on successive lesions of nodes or edges during the simulation of the neural dynamics. For each type of lesion, we measured the synchrony among oscillators and three complexity estimators (Higuchi's Fractal Dimension, Sample Entropy and Lempel-Ziv Complexity) of the simulated EEGs. We found a general negative correlation between EEG complexity metrics and synchrony but Sample Entropy and Lempel-Ziv showed a positive correlation with synchrony when the edges of the network were deleted. This suggests an intricate relationship between synchrony of the system and its estimated complexity. Hence, complexity seems to depend on the multiple states of interaction between the oscillators of the system. Our results can contribute to the interpretation of the functional meaning of EEG complexity.
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Affiliation(s)
| | - Sergio Iglesias-Parro
- 2 Department of Psychology, University of Jaén, Paraje las Lagunillas s/n, Jaén, 23071, Spain
| | - Javier Escudero
- 3 School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh, EH9 3FB, United Kingdom
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Cerquera A, Gjini K, Bowyer SM, Boutros N. Comparing EEG Nonlinearity in Deficit and Nondeficit Schizophrenia Patients: Preliminary Data. Clin EEG Neurosci 2017; 48:376-382. [PMID: 28618836 DOI: 10.1177/1550059417715388] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Electroencephalogram (EEG) contains valuable information obtained noninvasively that can be used for assessment of brain's processing capacity of patients with psychiatric disorders. The purpose of the present work was to evaluate possible differences in EEG complexity between deficit (DS) and nondeficit (NDS) subtypes of schizophrenia as a reflection of the cognitive processing capacities in these groups. A particular nonlinear metric known as Lempel-Ziv complexity (LZC) was used as a computational tool in order to determine the randomness in EEG alpha band time series from 3 groups (deficit schizophrenia [n = 9], nondeficit schizophrenia [n = 10], and healthy controls [n = 10]) according to time series randomness. There was a significant difference in frontal EEG complexity between the DS and NDS subgroups ( p = .013), with DS group showing less complexity. A significant positive correlation was found between LZC values and Positive and Negative Syndrome Scale (PANSS) general psychopathology scores (ie, larger frontal EEG complexity correlated with more severe psychopathology), explained partially by the emotional component subscore of the PANSS. These findings suggest that cognitive processing occurring in the frontal networks in DS is less complex compared to NDS patients as reflected by EEG complexity measures. The data also suggest that there may be a relationship between the degree of emotionality and the complexity of the frontal EEG signal.
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Affiliation(s)
- Alexander Cerquera
- 1 Facultad de Ingeniería Electrónica y Biomédica-Research Group Complex Systems, Universidad Antonio Nariño, Bogota, Colombia
| | - Klevest Gjini
- 2 Division of Neurosurgery, Seton Brain and Spine Institute, Austin, TX, USA
| | - Susan M Bowyer
- 3 Department of Neurology, Henry Ford Hospital and Wayne State University, Detroit, MI, USA
| | - Nash Boutros
- 4 Department of Psychiatry, University of Missouri-Kansas City, Kansas City, USA
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Yu Y, Zhao Y, Si Y, Ren Q, Ren W, Jing C, Zhang H. Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients. Biomed Eng Online 2016; 15:131. [PMID: 27884145 PMCID: PMC5123362 DOI: 10.1186/s12938-016-0282-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 11/16/2016] [Indexed: 11/10/2022] Open
Abstract
Background In schizophrenia, executive dysfunction is the most critical cognitive impairment, and is associated with abnormal neural activities, especially in the frontal lobes. Complexity estimation using electroencephalogram (EEG) recording based on nonlinear dynamics and task performance tests have been widely used to estimate executive dysfunction in schizophrenia. Methods The present study estimated the cool executive function based on fractal dimension (FD) values of EEG data recorded from first-episode schizophrenia patients and healthy controls during the performance of three cool executive function tasks, namely, the Trail Making Test-A (TMT-A), Trail Making Test-B (TMT-B), and Tower of Hanoi tasks. Results The results show that the complexity of the frontal EEG signals that were measured using FD was different in first-episode schizophrenia patients during the manipulation of executive function. However, no differences between patients and controls were found in the FD values of the EEG data that was recorded during the performance of the Tower of Hanoi task. Conclusions These results suggest that cool executive function exhibits little impairment in first-episode schizophrenia patients.
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Affiliation(s)
- Yi Yu
- Department of Biomedical Engineering, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Yun Zhao
- Department of Biomedical Engineering, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Yajing Si
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Qiongqiong Ren
- Department of Biomedical Engineering, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Wu Ren
- Department of Biomedical Engineering, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Changqin Jing
- Department of Life Sciences and Technology, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.
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17
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Akar SA, Kara S, Latifoğlu F, Bilgiç V. Analysis of the Complexity Measures in the EEG of Schizophrenia Patients. Int J Neural Syst 2015; 26:1650008. [PMID: 26762866 DOI: 10.1142/s0129065716500088] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients' clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2 min was partitioned into identical epochs of 20 s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel-Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics' brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics' brain.
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Affiliation(s)
- S. Akdemir Akar
- Institute of Biomedical Engineering, Fatih University, Buyukcekmece, İstanbul 34500, Turkey
| | - S. Kara
- Institute of Biomedical Engineering, Fatih University, Buyukcekmece, İstanbul 34500, Turkey
| | - F. Latifoğlu
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
| | - V. Bilgiç
- Psychiatry Department, Faculty of Medicine, Fatih University, İstanbul 34500, Turkey
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18
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Akar S, Kara S, Latifoğlu F, Bilgiç V. Estimation of nonlinear measures of schizophrenia patients' EEG in emotional states. Ing Rech Biomed 2015. [DOI: 10.1016/j.irbm.2015.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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19
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Complexity measures in magnetoencephalography: measuring "disorder" in schizophrenia. PLoS One 2015; 10:e0120991. [PMID: 25886553 PMCID: PMC4401778 DOI: 10.1371/journal.pone.0120991] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 02/09/2015] [Indexed: 11/19/2022] Open
Abstract
This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of 'disorder' in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices).
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20
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Investigation of the noise effect on fractal dimension of EEG in schizophrenia patients using wavelet and SSA-based approaches. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2014.11.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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21
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Yang AC, Hong CJ, Liou YJ, Huang KL, Huang CC, Liu ME, Lo MT, Huang NE, Peng CK, Lin CP, Tsai SJ. Decreased resting-state brain activity complexity in schizophrenia characterized by both increased regularity and randomness. Hum Brain Mapp 2015; 36:2174-86. [PMID: 25664834 DOI: 10.1002/hbm.22763] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 01/27/2015] [Accepted: 01/30/2015] [Indexed: 11/10/2022] Open
Abstract
Schizophrenia is characterized by heterogeneous pathophysiology. Using multiscale entropy (MSE) analysis, which enables capturing complex dynamics of time series, we characterized MSE patterns of blood-oxygen-level-dependent (BOLD) signals across different time scales and determined whether BOLD activity in patients with schizophrenia exhibits increased complexity (increased entropy in all time scales), decreased complexity toward regularity (decreased entropy in all time scales), or decreased complexity toward uncorrelated randomness (high entropy in short time scales followed by decayed entropy as the time scale increases). We recruited 105 patients with schizophrenia with an age of onset between 18 and 35 years and 210 age- and sex-matched healthy volunteers. Results showed that MSE of BOLD signals in patients with schizophrenia exhibited two routes of decreased BOLD complexity toward either regular or random patterns. Reduced BOLD complexity toward regular patterns was observed in the cerebellum and temporal, middle, and superior frontal regions, and reduced BOLD complexity toward randomness was observed extensively in the inferior frontal, occipital, and postcentral cortices as well as in the insula and middle cingulum. Furthermore, we determined that the two types of complexity change were associated differently with psychopathology; specifically, the regular type of BOLD complexity change was associated with positive symptoms of schizophrenia, whereas the randomness type of BOLD complexity was associated with negative symptoms of the illness. These results collectively suggested that resting-state dynamics in schizophrenia exhibit two routes of pathologic change toward regular or random patterns, which contribute to the differences in syndrome domains of psychosis in patients with schizophrenia.
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Affiliation(s)
- Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chungli, Taiwan; Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts
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Abstract
The observation that antagonists of the N-methyl-D-aspartate receptor (NMDAR), such as phencyclidine (PCP) and ketamine, transiently induce symptoms of acute schizophrenia had led to a paradigm shift from dopaminergic to glutamatergic dysfunction in pharmacological models of schizophrenia. The glutamate hypothesis can explain negative and cognitive symptoms of schizophrenia better than the dopamine hypothesis, and has the potential to explain dopamine dysfunction itself. The pharmacological and psychomimetic effects of ketamine, which is safer for human subjects than phencyclidine, are herein reviewed. Ketamine binds to a variety of receptors, but principally acts at the NMDAR, and convergent genetic and molecular evidence point to NMDAR hypofunction in schizophrenia. Furthermore, NMDAR hypofunction can explain connectional and oscillatory abnormalities in schizophrenia in terms of both weakened excitation of inhibitory γ-aminobutyric acidergic (GABAergic) interneurons that synchronize cortical networks and disinhibition of principal cells. Individuals with prenatal NMDAR aberrations might experience the onset of schizophrenia towards the completion of synaptic pruning in adolescence, when network connectivity drops below a critical value. We conclude that ketamine challenge is useful for studying the positive, negative, and cognitive symptoms, dopaminergic and GABAergic dysfunction, age of onset, functional dysconnectivity, and abnormal cortical oscillations observed in acute schizophrenia.
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Affiliation(s)
- Joel Frohlich
- Neuroscience Research Program, 1506D Gonda Center, University of California, Los Angeles Box 951761, Los Angeles, CA 90095-1761
| | - John Darrell Van Horn
- The Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street – SSB1-102, Los Angeles, CA 90032, Phone: (323) 442-7246
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Fernández A, Gómez C, Hornero R, López-Ibor JJ. Complexity and schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45:267-76. [PMID: 22507763 DOI: 10.1016/j.pnpbp.2012.03.015] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Revised: 03/27/2012] [Accepted: 03/31/2012] [Indexed: 11/17/2022]
Abstract
Complexity estimators have been broadly utilized in schizophrenia investigation. Early studies reported increased complexity in schizophrenia patients, associated with a higher variability or "irregularity" of their brain signals. However, further investigations showed reduced complexities, thus introducing a clear divergence. Nowadays, both increased and reduced complexity values are reported. The explanation of such divergence is a critical issue to understand the role of complexity measures in schizophrenia research. Considering previous arguments a complementary hypothesis is advanced: if the increased irregularity of schizophrenia patients' neurophysiological activity is assumed, a "natural" tendency to increased complexity in EEG and MEG scans should be expected, probably reflecting an abnormal neuronal firing pattern in some critical regions such as the frontal lobes. This "natural" tendency to increased complexity might be modulated by the interaction of three main factors: medication effects, symptomatology, and age effects. Therefore, young, medication-naïve, and highly symptomatic (positive symptoms) patients are expected to exhibit increased complexities. More importantly, the investigation of these interacting factors by means of complexity estimators might help to elucidate some of the neuropathological processes involved in schizophrenia.
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Affiliation(s)
- Alberto Fernández
- Departamento de Psiquiatría y Psicología Médica, Facultad de Medicina, Universidad Conmplutense, Madrid, Spain.
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24
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Takahashi T. Complexity of spontaneous brain activity in mental disorders. Prog Neuropsychopharmacol Biol Psychiatry 2013; 45:258-66. [PMID: 22579532 DOI: 10.1016/j.pnpbp.2012.05.001] [Citation(s) in RCA: 92] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2012] [Revised: 04/05/2012] [Accepted: 05/01/2012] [Indexed: 11/17/2022]
Abstract
Recent reports of functional and anatomical studies have provided evidence that aberrant neural connectivity lies at the heart of many mental disorders. Information related to neural networks has elucidated the nonlinear dynamical complexity in brain signals over a range of temporal scales. The recent advent of nonlinear analytic methods, which have served for the quantitative description of the brain signal complexity, has provided new insights into aberrant neural connectivity in many mental disorders. Although many studies have underpinned aberrant neural connectivity, findings related to complexity behavior are still inconsistent. This inconsistency might result from (i) heterogeneity in mental disorders, (ii) analytical issues, (iii) interference of typical development and aging. First, most mental disorders are heterogeneous in their clinical feature or intrinsic pathological mechanisms. Second, neurophysiologic output signals from complex brain connectivity might be characterized with multiple time scales or frequencies. Finally, age-related brain complexity changes must be considered when investigating pathological brain because typical brain complexity is not constant across generations. Future systematic studies addressing these issues will greatly expand our knowledge of neural connections and dynamics related to mental disorders.
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Affiliation(s)
- Tetsuya Takahashi
- Department of Neuropsychiatry, Faculty of Medical Sciences, University of Fukui, 23-3 Matsuokashimoaizuki, Eiheiji-cho, Yoshida-gun, Fukui 910-1193, Japan.
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25
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Yi G, Wang J, Bian H, Han C, Deng B, Wei X, Li H. Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects. Cogn Neurodyn 2012; 7:79-88. [PMID: 24427193 DOI: 10.1007/s11571-012-9221-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2012] [Revised: 07/25/2012] [Accepted: 09/17/2012] [Indexed: 11/30/2022] Open
Abstract
To explore the effects of manual acupuncture (MA) on brain activities, we design an experiment that acupuncture at acupoint ST36 of right leg with four different frequencies to obtain electroencephalograph (EEG) signals. Many studies have demonstrated that the complexity of EEG can reflect the states of brain function, so we propose to adopt order recurrence quantification analysis combined with discrete wavelet transform, to analyze the dynamical characteristics of different EEG rhythms under acupuncture, further to explore the effects of MA on the complexity of brain activities from multi-scale point of view. By analyzing the complexity of five EEG rhythms, it is found that the complexity of delta rhythm during acupuncture is lower than before acupuncture, and for alpha rhythm that is higher, but for beta, theta and gamma rhythms there are no obvious changes. All of those effects are especially obvious during acupuncture with frequency of 200 times/min. Furthermore, the determinism extracted from delta, alpha and gamma rhythms can be regarded as a characteristic parameter to distinguish the state acupuncture at 200 times/min and the state before acupuncture. These results can provide a theoretical support for selecting appropriate acupuncture frequency for patients in clinical, and the proposed methods have the potential of exploring the effects of acupuncture on brain activities.
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Affiliation(s)
- Guosheng Yi
- School of Electrical and Automation Engineering, Tianjin University, No. 92 Weijin Road, Tianjin, 300072 China
| | - Jiang Wang
- School of Electrical and Automation Engineering, Tianjin University, No. 92 Weijin Road, Tianjin, 300072 China
| | - Hongrui Bian
- No. 707 Research Institute, China Shipbuilding Industry Corporation, Tianjin, 300131 China
| | - Chunxiao Han
- School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222 China
| | - Bin Deng
- School of Electrical and Automation Engineering, Tianjin University, No. 92 Weijin Road, Tianjin, 300072 China
| | - Xile Wei
- School of Electrical and Automation Engineering, Tianjin University, No. 92 Weijin Road, Tianjin, 300072 China
| | - Huiyan Li
- School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222 China
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26
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Carlino E, Sigaudo M, Pollo A, Benedetti F, Mongini T, Castagna F, Vighetti S, Rocca P. Nonlinear analysis of electroencephalogram at rest and during cognitive tasks in patients with schizophrenia. J Psychiatry Neurosci 2012; 37:259-66. [PMID: 22353633 PMCID: PMC3380097 DOI: 10.1503/jpn.110030] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
BACKGROUND In spite of the large number of studies on schizophrenia, a full understanding of its core pathology still eludes us. The application of the nonlinear theory of electroencephalography (EEG) analysis provides an interesting tool to differentiate between physiologic conditions (e.g., resting state and mathematical task) and normal and pathologic brain activities. The aim of the present study was to investigate nonlinear EEG activity in patients with schizophrenia. METHODS We recorded 19-lead EEGs in patients with stable schizophrenia and healthy controls under 4 different conditions: eyes closed, eyes open, forward counting and backward counting. A nonlinear measure of complexity was calculated by means of correlation dimension (D2). RESULTS We included 17 patients and 17 controls in our analysis. Comparing the 2 populations, we observed greater D2 values in the patient group. In controls, increased D2 values were observed during active states (eyes open and the 2 cognitive tasks) compared with baseline conditions. This increase of brain complexity, which can be interpreted as an increase of information processing and integration, was not preserved in the patient population. LIMITATIONS Patients with schizophrenia were taking antipsychotic medications, so the presence of medication effects cannot be excluded. CONCLUSION Our results suggest that patients with schizophrenia present changes in brain activity compared with healthy controls, and this pathologic alteration can be successfully studied with nonlinear EEG analysis.
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Affiliation(s)
- Elisa Carlino
- Department of Neuroscience, University of Turin Medical School, and National Institute of Neuroscience, Turin, Italy.
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27
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Bystritsky A, Nierenberg AA, Feusner JD, Rabinovich M. Computational non-linear dynamical psychiatry: a new methodological paradigm for diagnosis and course of illness. J Psychiatr Res 2012; 46:428-35. [PMID: 22261550 DOI: 10.1016/j.jpsychires.2011.10.013] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2011] [Accepted: 10/28/2011] [Indexed: 10/14/2022]
Abstract
The goal of this article is to highlight the significant potential benefits of applying computational mathematical models to the field of psychiatry, specifically in relation to diagnostic conceptualization. The purpose of these models is to augment the current diagnostic categories that utilize a "snapshot" approach to describing mental states. We hope to convey to researchers and clinicians that non-linear dynamics can provide an additional useful longitudinal framework to understand mental illness. Psychiatric phenomena are complex processes that evolve in time, similar to many other processes in nature that have been successfully described and understood within deterministic chaos and non-linear dynamic computational models. Dynamical models describe mental processes and phenomena that change over time, more like a movie than a photograph, with multiple variables interacting over time. The use of these models may help us understand why and how current diagnostic categories are insufficient. They may also provide a new, more descriptive and ultimately more predictive approach leading to better understanding of the interrelationship between psychological, neurobiological, and genetic underpinnings of mental illness.
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Affiliation(s)
- A Bystritsky
- Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States.
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28
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Azulay DOD, Renoux B, Ivarsson M. Evidence of a pharmacodynamic EEG profile in rats following clonidine administration using a nonlinear analysis. NONLINEAR BIOMEDICAL PHYSICS 2011; 5:4. [PMID: 21703022 PMCID: PMC3141322 DOI: 10.1186/1753-4631-5-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2011] [Accepted: 06/26/2011] [Indexed: 05/31/2023]
Abstract
BACKGROUND Changes caused by clonidine in rodent electroencephalograms (EEG) have been reported with some inconsistency. For this reason, a pre-clinical study was conducted in order to confirm previous findings with both a standard spectral analysis and a sleep stage scoring procedure. In addition, a nonlinear technique for analysing the time-varying signals was implemented to compare its performance against conventional approaches. RESULTS The nonlinear method succeeds in quantifying all dose-related responses from the data set relying solely on the EEG trace. CONCLUSIONS Nonlinear approaches can deliver a suitable alternative to the sleep-stage scoring methods commonly used for drug effect detection.
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Affiliation(s)
| | - Benjamin Renoux
- Ecole des Mines d'Alès, Avenue Clavières, 30319, Alès, France
| | - Magnus Ivarsson
- Pfizer Global Research and Development, Ramsgate Road, Sandwich, CT13 9NJ, UK
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Abstract
The brain is a complex non-linear dynamical system that is associated with a wide repertoire of behaviours. There is an ongoing debate as to whether low-intensity radio frequency (RF) bioelectromagnetic interactions induce a biological response. If they do, it is reasonable to expect that the interaction is non-linear. Contradictory reports are found in the literature and attempts to reproduce the subtle effects have often proved difficult. Researchers have already speculated that low-intensity RF radiation may offer therapeutic potential and millimetre-wave therapy is established in the countries of the former Soviet Union. A recent study using transgenic mice that exhibit Alzheimer's-like cognitive impairment shows that microwave radiation may possibly have therapeutic application. By using a highly dynamic stimulus and feedback it may be possible to augment the small effects that have been reported using static parameters. If a firm connection between low-intensity RF radiation and biological effects is established then the possibility arises for its psychotherapeutic application. Low intensity millimetre-wave and peripheral nervous system interactions also merit further investigation. Controlled RF exposure could be associated with quite novel characteristics and dynamics when compared to those associated with pharmacotherapy.
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Affiliation(s)
- D T Pooley
- Institute of Medical Engineering and Medical Physics, Cardiff School of Engineering, Cardiff University, Queen's Buildings, The Parade, CARDIFF CF24 3AA, Wales, UK.
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Balli T, Palaniappan R. Classification of biological signals using linear and nonlinear features. Physiol Meas 2010; 31:903-20. [DOI: 10.1088/0967-3334/31/7/003] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage 2010; 51:173-82. [PMID: 20149880 DOI: 10.1016/j.neuroimage.2010.02.009] [Citation(s) in RCA: 168] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2009] [Revised: 01/14/2010] [Accepted: 02/03/2010] [Indexed: 01/29/2023] Open
Abstract
Multiscale entropy (MSE) analysis is a novel entropy-based approach for measuring dynamical complexity in physiological systems over a range of temporal scales. To evaluate this analytic approach as an aid to elucidating the pathophysiologic mechanisms in schizophrenia, we examined MSE in EEG activity in drug-naive schizophrenia subjects pre- and post-treatment with antipsychotics in comparison with traditional EEG analysis. We recorded eyes-closed resting-state EEG from frontal, temporal, parietal, and occipital regions in drug-naive 22 schizophrenia and 24 age-matched healthy control subjects. Fifteen patients were re-evaluated within 2-8 weeks after the initiation of antipsychotic treatment. For each participant, MSE was calculated on one continuous 60-s epoch for each experimental session. Schizophrenia subjects showed significantly higher complexity at higher time scales (lower frequencies) than did healthy controls in fronto-centro-temporal, but not in parieto-occipital regions. Post-treatment, this higher complexity decreased to healthy control subject levels selectively in fronto-central regions, while the increased complexity in temporal sites remained higher. Comparative power analysis identified spectral slowing in frontal regions in pre-treatment schizophrenia subjects, consistent with previous findings, whereas no antipsychotic treatment effect was observed. In summary, multiscale entropy measures identified abnormal dynamical EEG signal complexity in anterior brain areas in schizophrenia that normalized selectively in fronto-central areas with antipsychotic treatment. These findings show that entropy-based analytic methods may serve as a novel approach for characterizing and understanding abnormal cortical dynamics in schizophrenia and elucidating the therapeutic mechanisms of antipsychotics.
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Sabeti M, Katebi S, Boostani R. Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif Intell Med 2009; 47:263-74. [PMID: 19403281 DOI: 10.1016/j.artmed.2009.03.003] [Citation(s) in RCA: 181] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2008] [Revised: 12/21/2008] [Accepted: 03/14/2009] [Indexed: 11/18/2022]
Affiliation(s)
- Malihe Sabeti
- Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran.
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Raghavendra BS, Dutt DN, Halahalli HN, John JP. Complexity analysis of EEG in patients with schizophrenia using fractal dimension. Physiol Meas 2009; 30:795-808. [DOI: 10.1088/0967-3334/30/8/005] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Spatiotemporal nonlinearity in resting-state fMRI of the human brain. Neuroimage 2008; 40:1672-85. [DOI: 10.1016/j.neuroimage.2008.01.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2007] [Revised: 01/04/2008] [Accepted: 01/11/2008] [Indexed: 10/22/2022] Open
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Li Y, Tong S, Liu D, Gai Y, Wang X, Wang J, Qiu Y, Zhu Y. Abnormal EEG complexity in patients with schizophrenia and depression. Clin Neurophysiol 2008; 119:1232-41. [PMID: 18396454 DOI: 10.1016/j.clinph.2008.01.104] [Citation(s) in RCA: 111] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2006] [Revised: 01/14/2008] [Accepted: 01/28/2008] [Indexed: 10/22/2022]
Abstract
OBJECTIVE Schizophrenics are usually unable to perform well on cognitive tasks due to disturbances in cortical information processing that are observable as abnormalities in electroencephalogram (EEG) signals. However, whether such cortical disturbances can be assessed by quantitative EEG analysis remains unclear. The purpose of this study was to characterize EEG disturbances, using the Lempel-Ziv complexity (LZC), in the subjects with schizophrenia at rest or while performing mental arithmetic tasks. The results were compared to those from the subjects with depression and with healthy controls. METHODS The subjects included 62 schizophrenia patients, 48 depression patients and 26 age-matched healthy controls. EEG was recorded under two conditions: (i) resting with eyes closed, and (ii) a mentally active condition wherein the subjects were asked to subtract 7 from 100 iteratively with their eyes closed. EEG signals were analyzed by LZC and conventional spectral methods. RESULTS In all the groups, LZC of EEG decreased during the mental arithmetic compared with those under the resting conditions. Both the schizophrenia and the depression groups had a higher LZC (p<0.05) than the controls. Also, the schizophrenia group had a lower LZC (p<0.05) than the depression group during the mental arithmetic task as well as during the resting state. Significant differences in LZC, at some symmetrically located loci (FP1/FP2, F7/F8), between the two hemispheres were found in all the patient groups only during the arithmetic task. CONCLUSIONS Compared with conventional spectral analysis, LZC was more sensitive to both the power spectrum and the temporal amplitude distribution. LZC was associated with the ability to attend to the task and adapt the information processing system to the cognitive challenge. Thus, it would be useful in studying the disturbances in the cortical information processing patients with depression or schizophrenia. SIGNIFICANCE LZC of EEG is associated with mental activity. Thus, LZC analysis can be an important tool in understanding the pathophysiology of schizophrenia and depression in future studies.
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Affiliation(s)
- Yingjie Li
- School of Communication and Information Engineering, Shanghai University, P.O. #01, 149 Yanchang Road, Shanghai 200072, PR China.
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Reijneveld JC, Ponten SC, Berendse HW, Stam CJ. The application of graph theoretical analysis to complex networks in the brain. Clin Neurophysiol 2007; 118:2317-31. [PMID: 17900977 DOI: 10.1016/j.clinph.2007.08.010] [Citation(s) in RCA: 307] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2007] [Revised: 08/20/2007] [Accepted: 08/23/2007] [Indexed: 02/07/2023]
Abstract
Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in network sciences, such as the discovery of small world and scale free networks, to data on anatomical and functional connectivity in the brain. In this review we start with some background knowledge on the history and recent advances in network theories in general. We emphasize the correlation between the structural properties of networks and the dynamics of these networks. We subsequently demonstrate through evidence from computational studies, in vivo experiments, and functional MRI, EEG and MEG studies in humans, that both the functional and anatomical connectivity of the healthy brain have many features of a small world network, but only to a limited extent of a scale free network. The small world structure of neural networks is hypothesized to reflect an optimal configuration associated with rapid synchronization and information transfer, minimal wiring costs, resilience to certain types of damage, as well as a balance between local processing and global integration. Eventually, we review the current knowledge on the effects of focal and diffuse brain disease on neural network characteristics, and demonstrate increasing evidence that both cognitive and psychiatric disturbances, as well as risk of epileptic seizures, are correlated with (changes in) functional network architectural features.
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Affiliation(s)
- Jaap C Reijneveld
- Department of Neurology, VU University Medical Center, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands.
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Feng Z, Chen H. Analyze the dynamic features of rat EEG using wavelet entropy. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2007; 2006:833-6. [PMID: 17282313 DOI: 10.1109/iembs.2005.1616544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Wavelet entropy (WE), a new method of complexity measure for non-stationary signals, was used to investigate the dynamic features of rat EEGs under three vigilance states. The EEGs of the freely moving rats were recorded with implanted electrodes and were decomposed into four components of delta, theta, alpha and beta by using multi-resolution wavelet transform. Then, the wavelet entropy curves were calculated as a function of time. The results showed that there were significant differences among the average WEs of EEGs recorded under the vigilance states of waking, slow wave sleep (SWS) and rapid eye movement (REM) sleep. The changes of WE had different relationships with the four power components under different states. Moreover, there was evident rhythm in EEG WEs of SWS sleep for most experimental rats, which indicated a reciprocal relationship between slow waves and sleep spindles in the micro-states of SWS sleep. Therefore, WE can be used not only to distinguish the long-term changes in EEG complexity, but also to reveal the short-term changes in EEG micro-state.
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Affiliation(s)
- Zhouyan Feng
- Department of Biotechnology, College of Life Science, Zhejiang University, Hangzhou, 310027, P.R. China
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Li YJ, Fan FY, Zhu YS. Detecting the determinism of EEG time series using a nonlinear forecasting method. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2007; 2005:4538-40. [PMID: 17281248 DOI: 10.1109/iembs.2005.1615478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The determinism of time series is investigated using a nonlinear non-parametric forecasting method. The goodness of prediction was estimated in terms of the prediction error of the predicted time series. A new definition of the prediction effect was made in the present study. Three typical kinds of time series were detected using our new method. In deterministic chaotic time series, good prediction was obtained in the new definition. However, for Gaussian random noise and schizophrenia EEG signal, the predictability could not found. We concluded that EEGs in schizophrenic patients were not deterministic.
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Loganovsky KN, Volovik SV, Manton KG, Bazyka DA, Flor-Henry P. Whether ionizing radiation is a risk factor for schizophrenia spectrum disorders? World J Biol Psychiatry 2006; 6:212-30. [PMID: 16272077 DOI: 10.1080/15622970510029876] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The neural diathesis-stressor hypothesis of schizophrenia, where neurobiological genetic predisposition to schizophrenia can be provoked by environmental stressors is considered as a model of the effects of exposure to ionizing radiation. Analysis of information from electronic databases (MEDLINE, PsycINFO, EMBASE, Current Contents, Elsevier BIOBASE) and hand-made search was carried out. There are comparable reports on increases in schizophrenia spectrum disorders following exposure to ionizing radiation as a result of atomic bombing, nuclear weapons testing, the Chernobyl accident, environmental contamination by radioactive waste, radiotherapy, and also in areas with high natural radioactive background. The results of experimental radioneurobiological studies support the hypothesis of schizophrenia as a neurodegenerative disease. Exposure to ionizing radiation causes brain damage with limbic (cortical-limbic) system dysfunction and impairment of informative processes at the molecular level that can trigger schizophrenia in predisposed individuals or cause schizophrenia-like disorders. It is supposed that ionizing radiation can be proposed as a risk factor for schizophrenia spectrum disorders. The hypothesis that ionizing radiation is a risk factor for schizophrenia spectrum disorders can be tested using data from the Chernobyl accident aftermath. Implementation of a study on schizophrenia spectrum disorders in Chernobyl accident victims is of significance for both clinical medicine and neuroscience.
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Wang ZH, Chang MH, Yang JW, Sun JJ, Lee HC, Shyu BC. Layer IV of the primary somatosensory cortex has the highest complexity under anesthesia and cortical complexity is modulated by specific thalamic inputs. Brain Res 2006; 1082:102-14. [PMID: 16500629 DOI: 10.1016/j.brainres.2006.01.070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2005] [Revised: 01/18/2006] [Accepted: 01/18/2006] [Indexed: 10/25/2022]
Abstract
The system complexity, as calculated from correlation dimension, embedded in each layer and its modulation by specific inputs and general excitatory state are not yet known. The aims of present study were to estimate the system complexity across the cortical layers by analyzing intracortical EEG signals using a nonlinear analytical method, and to identify how layer-related complexity varies with the alteration of thalamic input and brain state. Male Sprague-Dawley rats were anesthetized under l% halothane. Sixteen channels of evoked or spontaneous EEG signals were recorded simultaneously across the six cortical layers in the somatosensory cortex with a single Michigan probe. The system complexity was assessed by computing correlation dimension, D(2), based on the Nonlinear Time Series Analysis data analysis program. Cortical layer IV exhibited a D(2) value, 3.24, that was significantly higher than that of the other cortical layers. The D(2) values in layers IV and II/III were significantly reduced after reversible deactivation of the ventral posterior lateral thalamic nucleus. D(2) decreased with increases in administered halothane concentration from 0.75% to 2.0%, particularly in layer IV. The present findings suggest that cortical layer IV maintains a higher complexity than the other layers and that the complexity of the mid-cortical layers is subject to regulation from specific thalamic inputs and more sensitive to changes in the general state of brain excitation.
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Affiliation(s)
- Zi-Hao Wang
- Computing Centre, Academia Sinica, Taipei 11529, Taiwan, ROC
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Hornero R, Abásolo D, Jimeno N, Sánchez CI, Poza J, Aboy M. Variability, Regularity, and Complexity of Time Series Generated by Schizophrenic Patients and Control Subjects. IEEE Trans Biomed Eng 2006; 53:210-8. [PMID: 16485749 DOI: 10.1109/tbme.2005.862547] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We analyzed time series generated by 20 schizophrenic patients and 20 sex- and age-matched control subjects using three nonlinear methods of time series analysis as test statistics: central tendency measure (CTM) from the scatter plots of first differences of data, approximate entropy (ApEn), and Lempel-Ziv (LZ) complexity. We divided our data into a training set (10 patients and 10 control subjects) and a test set (10 patients and 10 control subjects). The training set was used for algorithm development and optimum threshold selection. Each method was assessed prospectively using the test dataset. We obtained 80% sensitivity and 90% specificity with LZ complexity, 90% sensitivity, and 60% specificity with ApEn, and 70% sensitivity and 70% specificity with CTM. Our results indicate that there exist differences in the ability to generate random time series between schizophrenic subjects and controls, as estimated by the CTM, ApEn, and LZ. This finding agrees with most previous results showing that schizophrenic patients are characterized by less complex neurobehavioral and neuropsychologic measurements.
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Affiliation(s)
- Roberto Hornero
- ETS Ingenieros de Telecomunicación, University of Valladolid, Spain.
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Abstract
OBJECTIVE Nonlinear properties exist within the brain across a hierarchy of scales and within a variety of critical neural processes. Only a few studies of brain activity in schizophrenia, however, have used nonlinear methods. This review paper evaluates the contribution of the nonlinear sciences towards understanding schizophrenia. METHOD Applications of nonlinear methods to the study of schizophrenia symptoms and to healthy and schizophrenia functional neuroscience data are reviewed. The main flaws of nonlinear algorithms and recent methods to correct these are also appraised. RESULTS Initial research methods utilized in the study of nonlinearity in schizophrenia have fundamental methodological limitations. In the last decade, many of these problems have been addressed, facilitating future progress. Research incorporating these improvements has been applied to normal electroencephalogram (EEG) data and to the symptoms of schizophrenia, but not systematically to brain imaging data collected from patients with schizophrenia. CONCLUSION There is strong statistical evidence for weak nonlinearity in normal EEG and in the fluctuations of the symptoms of schizophrenia. However, the contribution of nonlinear processes to brain dysfunction in schizophrenia is yet to be properly established or accurately quantified. Despite this, recent methodological advances suggest that a 'nonlinear theory' of schizophrenia may be helpful in understanding this disorder.
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Affiliation(s)
- Michael Breakspear
- The School of Psychiatry, University of New South Wales and the Black Dog Institute, Australia.
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Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 2005; 116:2266-301. [PMID: 16115797 DOI: 10.1016/j.clinph.2005.06.011] [Citation(s) in RCA: 708] [Impact Index Per Article: 37.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2005] [Revised: 06/03/2005] [Accepted: 06/11/2005] [Indexed: 02/07/2023]
Abstract
Many complex and interesting phenomena in nature are due to nonlinear phenomena. The theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a stage, where it becomes possible to study self-organization and pattern formation in the complex neuronal networks of the brain. One approach to nonlinear time series analysis consists of reconstructing, from time series of EEG or MEG, an attractor of the underlying dynamical system, and characterizing it in terms of its dimension (an estimate of the degrees of freedom of the system), or its Lyapunov exponents and entropy (reflecting unpredictability of the dynamics due to the sensitive dependence on initial conditions). More recently developed nonlinear measures characterize other features of local brain dynamics (forecasting, time asymmetry, determinism) or the nonlinear synchronization between recordings from different brain regions. Nonlinear time series has been applied to EEG and MEG of healthy subjects during no-task resting states, perceptual processing, performance of cognitive tasks and different sleep stages. Many pathologic states have been examined as well, ranging from toxic states, seizures, and psychiatric disorders to Alzheimer's, Parkinson's and Cre1utzfeldt-Jakob's disease. Interpretation of these results in terms of 'functional sources' and 'functional networks' allows the identification of three basic patterns of brain dynamics: (i) normal, ongoing dynamics during a no-task, resting state in healthy subjects; this state is characterized by a high dimensional complexity and a relatively low and fluctuating level of synchronization of the neuronal networks; (ii) hypersynchronous, highly nonlinear dynamics of epileptic seizures; (iii) dynamics of degenerative encephalopathies with an abnormally low level of between area synchronization. Only intermediate levels of rapidly fluctuating synchronization, possibly due to critical dynamics near a phase transition, are associated with normal information processing, whereas both hyper-as well as hyposynchronous states result in impaired information processing and disturbed consciousness.
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Affiliation(s)
- C J Stam
- Department of Clinical Neurophysiology, VU University Medical Centre, P.O. Box 7057, 1007 MB Amsterdam, The Netherlands.
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Bartolomei F, Trébuchon A, Gavaret M, Régis J, Wendling F, Chauvel P. Acute alteration of emotional behaviour in epileptic seizures is related to transient desynchrony in emotion-regulation networks. Clin Neurophysiol 2005; 116:2473-9. [PMID: 16125458 DOI: 10.1016/j.clinph.2005.05.013] [Citation(s) in RCA: 55] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2004] [Revised: 04/26/2005] [Accepted: 05/14/2005] [Indexed: 11/22/2022]
Abstract
OBJECTIVE During focal epileptic seizures, patients may express intense agitation, screaming and facial expressions of rage, fear or anger. The precise anatomical origin of such intense ictal emotional behaviour is not fully understood and the mechanisms by which the epileptic discharges provoke these phenomena are unknown. In the present study, we analysed the neurophysiological mechanisms underlying ictal emotional behaviour in 3 patients with frontal lobe epilepsies undergoing intracerebral recordings for presurgical evaluation. METHODS We analyzed the interactions between regions forming 'emotional networks', before and during behavioural alterations. Intracerebral recordings (SEEG method) of seizures from 3 patients presenting with frontal lobe seizures were analyzed. A nonlinear measure of SEEG signal interdependencies was used to evaluate the functional couplings occurring between brain structures. RESULTS We found that these intense emotional alterations were associated with a decrease of synchrony between signals recorded from the neural networks known to be involved in emotional processing, and in particular a loss of synchrony between the orbito-frontal cortex and the amygdala. This disruption of functional connections could then result in the disruption of emotional regulation leading to the release of altered behaviour, as observed in epileptic patients during seizures. CONCLUSIONS We propose that the occurrence of intense ictal emotional behaviour disturbance in frontal lobe seizures is related to a disruption of the normal mechanisms of emotional regulation SIGNIFICANCE These results provide some insight into our understanding of the pathophysiological processes involved in human partial epilepsies as well as in the interpretation of clinical semiology.
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Affiliation(s)
- Fabrice Bartolomei
- Service de Neurophysiologie Clinique, INSERM EMI 9926, CHU TIMONE et Université de la Méditerranée, 264 Rue Saint-Pierre, 13385 Marseille Cedex 5, France.
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Burioka N, Cornélissen G, Maegaki Y, Halberg F, Kaplan DT, Miyata M, Fukuoka Y, Endo M, Suyama H, Tomita Y, Shimizu E. Approximate entropy of the electroencephalogram in healthy awake subjects and absence epilepsy patients. Clin EEG Neurosci 2005; 36:188-93. [PMID: 16128154 DOI: 10.1177/155005940503600309] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The approximate entropy (ApEn) of signals in the electroencephalogram (EEG) was evaluated in 8 healthy volunteers and in 10 patients with absence epilepsy, both during seizure-free and seizure intervals. We estimated the nonlinearity of each 3-sec EEG segment using surrogate data methods. The mean (+/- SD) ApEn in EEG was 0.83 +/- 0.22 in healthy subjects awake with eyes closed. It was significantly lower during epileptic seizures (0.48 +/- 0.05) than during seizure-free intervals (0.80 +/- 0.13) (P < 0.001). Nonlinearity was clearly detected in EEG signals from epileptic patients during seizures but not during seizure-free intervals or in EEG signals from healthy subjects. The ApEn of EEG signals estimated over consecutive intervals could serve to determine pathological brain activity such as that occurring during absence epilepsy.
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Affiliation(s)
- Naoto Burioka
- Division of Medical Oncology and Molecular Respirology, Faculty of Medicine, Tottori University, 36-1 Nishimachi, Yonago 683-8504, Japan.
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Abstract
Quantitative electroencephalogram (qEEG) plays a significant role in EEG-based clinical diagnosis and studies of brain function. In past decades, various qEEG methods have been extensively studied. This article provides a detailed review of the advances in this field. qEEG methods are generally classified into linear and nonlinear approaches. The traditional qEEG approach is based on spectrum analysis, which hypothesizes that the EEG is a stationary process. EEG signals are nonstationary and nonlinear, especially in some pathological conditions. Various time-frequency representations and time-dependent measures have been proposed to address those transient and irregular events in EEG. With regard to the nonlinearity of EEG, higher order statistics and chaotic measures have been put forward. In characterizing the interactions across the cerebral cortex, an information theory-based measure such as mutual information is applied. To improve the spatial resolution, qEEG analysis has also been combined with medical imaging technology (e.g., CT, MR, and PET). With these advances, qEEG plays a very important role in basic research and clinical studies of brain injury, neurological disorders, epilepsy, sleep studies and consciousness, and brain function.
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Affiliation(s)
- Nitish V Thakor
- Biomedical Engineering Department, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
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Watanabe TAA, Cellucci CJ, Kohegyi E, Bashore TR, Josiassen RC, Greenbaun NN, Rapp PE. The algorithmic complexity of multichannel EEGs is sensitive to changes in behavior. Psychophysiology 2003; 40:77-97. [PMID: 12751806 DOI: 10.1111/1469-8986.00009] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Symbolic measures of complexity provide a quantitative characterization of the sequential structure of symbol sequences. Promising results from the application of these methods to the analysis of electroencephalographic (EEG) and event-related brain potential (ERP) activity have been reported. Symbolic measures used thus far have two limitations, however. First, because the value of complexity increases with the length of the message, it is difficult to compare signals of different epoch lengths. Second, these symbolic measures do not generalize easily to the multichannel case. We address these issues in studies in which both single and multichannel EEGs were analyzed using measures of signal complexity and algorithmic redundancy, the latter being defined as a sequence-sensitive generalization of Shannon's redundancy. Using a binary partition of EEG activity about the median, redundancy was shown to be insensitive to the size of the data set while being sensitive to changes in the subject's behavioral state (eyes open vs. eyes closed). The covariance complexity, calculated from the singular value spectrum of a multichannel signal, was also found to be sensitive to changes in behavioral state. Statistical separations between the eyes open and eyes closed conditions were found to decrease following removal of the 8- to 12-Hz content in the EEG, but still remained statistically significant. Use of symbolic measures in multivariate signal classification is described.
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Affiliation(s)
- T A A Watanabe
- Department of Pharmacology and Physiology, Drexel University, College of Medicine, Philadelphia, Pennsylvania, USA
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Abstract
OBJECTIVE The aim of the present study is to assess information transmission between different cortical areas in schizophrenics by estimating the average cross mutual information (A-CMI) and to characterize the dynamical property of the cortical areas of schizophrenic patients from multi-channel EEG by establishing the auto mutual information (AMI). METHODS We recorded the EEG from 16 electrodes in 10 schizophrenic patients and 10 age-matched normal controls. We estimated the slope of the AMI to evaluate the complexity of the EEG signal from one electrode and the A-CMI values of all 16x16 pairs of electrodes were calculated to investigate the information transmission of different cortical areas in schizophrenic patients. RESULTS In T5 and C3 electrodes, the schizophrenic patients had lower complexity than normal controls. The schizophrenic patients had significantly higher interhemispheric and intrahemispheric A-CMI values than the normal controls. CONCLUSIONS These results are consistent with previous findings that suggest left hemispheric hypotemporality and inter- and/or intra-hemispheric overconnectivity in schizophrenics. Our results of the left hemispheric hypotemporality and the increased interhemispheric information transmission in temporal lobe may support the hypothesis that the abnormal laterlization in temporal lobe are due to left temporal lobe deficit in schizophrenic patients.
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Affiliation(s)
- Sun Hee Na
- Department of Physics, Korea Advanced Institute of Science and Technology, 305-701 Taejon, South Korea
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Laskaris NA, Ioannides AA. Semantic geodesic maps: a unifying geometrical approach for studying the structure and dynamics of single trial evoked responses. Clin Neurophysiol 2002; 113:1209-26. [PMID: 12139999 DOI: 10.1016/s1388-2457(02)00124-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
OBJECTIVES A general framework for identifying and describing structure in a given sample of evoked response single-trial signals (STs) is introduced. The approach is based on conceptually simple geometrical ideas and enables the convergence of pattern analysis and non-linear time series analysis. METHODS Classical steps for analyzing the STs by waveform are first employed and the ST-analysis is transferred to a multidimensional space, the feature space, the geometry of which is systematically studied via multidimensional scaling (MDS) techniques giving rise to semantic maps. The structure in the feature space characterizes the trial-to-trial variability and this is utilized to probe functional connectivity between two brain areas. The underlying dynamic process responsible for the emerged structure can be described by a multidimensional trajectory in the feature space. This in turn enables the detection of dynamical interareal coupling as similarity between the corresponding trajectories. RESULTS AND CONCLUSIONS The utility of semantic maps was demonstrated using magnetoencephalographic data from a simple auditory paradigm. The coupling of ongoing activity and evoked response is vividly demonstrated and contrasted with the apparent deflection from zero baseline that survives averaging. Prototypes are easily identified as the end points of distinct paths in the semantic map representation, and their neighborhood is populated by STs with distinct properties not only in the latencies where the evoked response is expected to be strong, but also and very significantly in the prestimulus period. Finally our results provide evidence for interhemispheric binding in the (4-8 Hz) range and dynamical coupling at faster time scales.
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Affiliation(s)
- N A Laskaris
- Laboratory for Human Brain Dynamics, Brain Science Institute, Riken, Wako-shi 351-0198, Japan
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