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Kang X, Liu X, Chen S, Zhang W, Liu S, Ming D. Major depressive disorder recognition by quantifying EEG signal complexity using proposed APLZC and AWPLZC. J Affect Disord 2024; 356:105-114. [PMID: 38580036 DOI: 10.1016/j.jad.2024.03.169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/27/2024] [Accepted: 03/29/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND Seeking objective quantitative indicators is important for accurately recognizing major depressive disorder (MDD). Lempel-Ziv complexity (LZC), employed to characterize neurological disorders, faces limitations in tracking dynamic changes in EEG signals due to defects in the coarse-graining process, hindering its precision for MDD objective quantitative indicators. METHODS This work proposed Adaptive Permutation Lempel-Ziv Complexity (APLZC) and Adaptive Weighted Permutation Lempel-Ziv Complexity (AWPLZC) algorithms by refining the coarse-graining process and introducing weight factors to effectively improve the precision of LZC in characterizing EEGs and further distinguish MDD patients better. APLZC incorporated the ordinal pattern, while False Nearest Neighbor and Mutual Information algorithms were introduced to determine and adjust key parameters adaptively. Furthermore, we proposed AWPLZC by assigning different weights to each pattern based on APLZC. Thirty MDD patients and 30 healthy controls (HCs) were recruited and their 64-channel resting EEG signals were collected. The complexities of gamma oscillations were then separately computed using LZC, APLZC, and AWPLZC algorithms. Subsequently, a multi-channel adaptive K-nearest neighbor model was constructed for identifying MDD patients and HCs. RESULTS LZC, APLZC, and AWPLZC algorithms achieved accuracy rates of 78.29 %, 90.32 %, and 95.13 %, respectively. Sensitivities reached 67.96 %, 85.04 %, and 98.86 %, while specificities were 88.62 %, 95.35 %, and 89.92 %, respectively. Notably, AWPLZC achieved the best performance in accuracy and sensitivity, with a specificity limitation. LIMITATION The sample size is relatively small. CONCLUSION APLZC and AWPLZC algorithms, particularly AWPLZC, demonstrate superior effectiveness in differentiating MDD patients from HCs compared with LZC. These findings hold significant clinical implications for MDD diagnosis.
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Affiliation(s)
- Xianyun Kang
- Medical School, Tianjin University, Tianjin 300072, China
| | - Xiaoya Liu
- Medical School, Tianjin University, Tianjin 300072, China
| | - Sitong Chen
- Medical School, Tianjin University, Tianjin 300072, China
| | - Wenquan Zhang
- Medical School, Tianjin University, Tianjin 300072, China
| | - Shuang Liu
- Medical School, Tianjin University, Tianjin 300072, China.
| | - Dong Ming
- Medical School, Tianjin University, Tianjin 300072, China
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Zhao H, Xie J, Chen Y, Cao J, Liao WH, Cao H. Diagnosis of neurodegenerative diseases with a refined Lempel-Ziv complexity. Cogn Neurodyn 2024; 18:1153-1166. [PMID: 38826647 PMCID: PMC11143150 DOI: 10.1007/s11571-023-09973-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 04/03/2023] [Accepted: 04/19/2023] [Indexed: 06/04/2024] Open
Abstract
The investigation into the distinctive difference of gait is of significance for the clinical diagnosis of neurodegenerative diseases. However, human gait is affected by many factors like behavior, occupation and so on, and they may confuse the gait differences among Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. For the purpose of examining distinctive gait differences of neurodegenerative diseases, this study extracts various features from both vertical ground reaction force and time intervals. Moreover, refined Lempel-Ziv complexity is proposed considering the detailed distribution of signals based on the median and quartiles. Basic features (mean, coefficient of variance, and the asymmetry index), nonlinear dynamic features (Hurst exponent, correlation dimension, largest Lyapunov exponent), and refined Lempel-Ziv complexity of different neurodegenerative diseases are compared statistically by violin plot and Kruskal-Wallis test to reveal distinction and regularities. The comparative analysis results illustrate the gait differences across these neurodegenerative diseases by basic features and nonlinear dynamic features. Classification results by random forest indicate that the refined Lempel-Ziv complexity can robustly enhance the diagnosis accuracy when combined with basic features.
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Affiliation(s)
- Huan Zhao
- Key Laboratory of Education Ministry for Modern Design and Rotor Bearing System, Xi’an Jiaotong University, 28 Xianning West Road, 710049 Xi’an, China
| | - Junxiao Xie
- Key Laboratory of Education Ministry for Modern Design and Rotor Bearing System, Xi’an Jiaotong University, 28 Xianning West Road, 710049 Xi’an, China
| | - Yangquan Chen
- School of Engineering, University of California at Merced, Merced, CA 95343 USA
| | - Junyi Cao
- Key Laboratory of Education Ministry for Modern Design and Rotor Bearing System, Xi’an Jiaotong University, 28 Xianning West Road, 710049 Xi’an, China
| | - Wei-Hsin Liao
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, N.T., 999077 Hong Kong, China
| | - Hongmei Cao
- Department of Neurology, First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, 710061 Xi’an, China
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Estarellas M, Huntley J, Bor D. Neural markers of reduced arousal and consciousness in mild cognitive impairment. Int J Geriatr Psychiatry 2024; 39:e6112. [PMID: 38837281 DOI: 10.1002/gps.6112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 05/23/2024] [Indexed: 06/07/2024]
Abstract
OBJECTIVES People with Alzheimer's Disease (AD) experience changes in their level and content of consciousness, but there is little research on biomarkers of consciousness in pre-clinical AD and Mild Cognitive Impairment (MCI). This study investigated whether levels of consciousness are decreased in people with MCI. METHODS A multi-site site magnetoencephalography (MEG) dataset, BIOFIND, comprising 83 people with MCI and 83 age matched controls, was analysed. Arousal (and drowsiness) was assessed by computing the theta-alpha ratio (TAR). The Lempel-Ziv algorithm (LZ) was used to quantify the information content of brain activity, with higher LZ values indicating greater complexity and potentially a higher level of consciousness. RESULTS LZ was lower in the MCI group versus controls, indicating a reduced level of consciousness in MCI. TAR was higher in the MCI group versus controls, indicating a reduced level of arousal (i.e. increased drowsiness) in MCI. LZ was also found to be correlated with mini-mental state examination (MMSE) scores, suggesting an association between cognitive impairment and level of consciousness in people with MCI. CONCLUSIONS A decline in consciousness and arousal can be seen in MCI. As cognitive impairment worsens, measured by MMSE scores, levels of consciousness and arousal decrease. These findings highlight how monitoring consciousness using biomarkers could help understand and manage impairments found at the preclinical stages of AD. Further research is needed to explore markers of consciousness between people who progress from MCI to dementia and those who do not, and in people with moderate and severe AD, to promote person-centred care.
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Affiliation(s)
- Mar Estarellas
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
- Experimental Psychology Department, University College London, London, UK
- Department of Psychology, Cambridge University, Cambridge, UK
| | - Jonathan Huntley
- Division of Psychiatry, University College London, London, UK
- Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Daniel Bor
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
- Department of Psychology, Cambridge University, Cambridge, UK
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Huang SJY, Wang X, Halvorson BD, Bao Y, Frisbee SJ, Frisbee JC, Goldman D. Laser Doppler Fluximetry in Cutaneous Vasculature: Methods for Data Analyses. J Vasc Res 2024; 61:197-211. [PMID: 38749406 DOI: 10.1159/000538718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 03/27/2024] [Indexed: 08/09/2024] Open
Abstract
INTRODUCTION Acquisition of a deeper understanding of microvascular function across physiological and pathological conditions can be complicated by poor accessibility of the vascular networks and the necessary sophistication or intrusiveness of the equipment needed to acquire meaningful data. Laser Doppler fluximetry (LDF) provides a mechanism wherein investigators can readily acquire large amounts of data with minor inconvenience for the subject. However, beyond fairly basic analyses of erythrocyte perfusion (fluximetry) data within the cutaneous microcirculation (i.e., perfusion at rest and following imposed challenges), a deeper understanding of microvascular perfusion requires a more sophisticated approach that can be challenging for many investigators. METHODS This manuscript provides investigators with clear guidance for data acquisition from human subjects for full analysis of fluximetry data, including levels of perfusion, single- and multiscale Lempel-Ziv complexity (LZC) and sample entropy (SampEn), and wavelet-based analyses for the major physiological components of the signal. Representative data and responses are presented from a recruited cohort of healthy volunteers, and computer codes for full data analysis (MATLAB) are provided to facilitate efforts by interested investigators. CONCLUSION It is anticipated that these materials can reduce the challenge to investigators integrating these approaches into their research programs and facilitate translational research in cardiovascular science.
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Affiliation(s)
- Sophie J Y Huang
- Departments of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Xuan Wang
- Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Brayden D Halvorson
- Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Yuki Bao
- Biomedical Engineering, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Stephanie J Frisbee
- Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Jefferson C Frisbee
- Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Daniel Goldman
- Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Biomedical Engineering, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
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Grivel E, Berthelot B, Colin G, Legrand P, Ibanez V. Benefits of Zero-Phase or Linear Phase Filters to Design Multiscale Entropy: Theory and Application. ENTROPY (BASEL, SWITZERLAND) 2024; 26:332. [PMID: 38667886 PMCID: PMC11048990 DOI: 10.3390/e26040332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 03/16/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024]
Abstract
In various applications, multiscale entropy (MSE) is often used as a feature to characterize the complexity of the signals in order to classify them. It consists of estimating the sample entropies (SEs) of the signal under study and its coarse-grained (CG) versions, where the CG process amounts to (1) filtering the signal with an average filter whose order is the scale and (2) decimating the filter output by a factor equal to the scale. In this paper, we propose to derive a new variant of the MSE. Its novelty stands in the way to get the sequences at different scales by avoiding distortions during the decimation step. To this end, a linear-phase or null-phase low-pass filter whose cutoff frequency is well suited to the scale is used. Interpretations on how the MSE behaves and illustrations with a sum of sinusoids, as well as white and pink noises, are given. Then, an application to detect attentional tunneling is presented. It shows the benefit of the new approach in terms of p value when one aims at differentiating the set of MSEs obtained in the attentional tunneling state from the set of MSEs obtained in the nominal state. It should be noted that CG versions can be replaced not only for the MSE but also for other variants.
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Affiliation(s)
- Eric Grivel
- IMS Laboratory, Bordeaux INP, Bordeaux University, UMR CNRS 5218, 33400 Talence, France
| | - Bastien Berthelot
- Thales AVS France, Campus Merignac, 75-77 Av. Marcel Dassault, 33700 Mérignac, France; (B.B.); (V.I.)
| | - Gaetan Colin
- ENSEIRB-MATMECA, Bordeaux INP, 33400 Talence, France
| | - Pierrick Legrand
- IMB Laboratory, Bordeaux University, UMR CNRS 5251, ASTRAL Team, INRIA, 33400 Talence, France;
| | - Vincent Ibanez
- Thales AVS France, Campus Merignac, 75-77 Av. Marcel Dassault, 33700 Mérignac, France; (B.B.); (V.I.)
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van der Wijk G, Enkhbold Y, Cnudde K, Szostakiwskyj MW, Blier P, Knott V, Jaworska N, Protzner AB. One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response. Front Psychiatry 2024; 15:1358018. [PMID: 38628260 PMCID: PMC11018891 DOI: 10.3389/fpsyt.2024.1358018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
Introduction To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. Methods Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients. Results Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization. Discussion Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability. Clinical trial registration https://clinicaltrials.gov, identifier NCT00519428.
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Affiliation(s)
- Gwen van der Wijk
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Yaruuna Enkhbold
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Kelsey Cnudde
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | | | - Pierre Blier
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Verner Knott
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Natalia Jaworska
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Andrea B. Protzner
- Department of Psychology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Mathison Centre, University of Calgary, Calgary, AB, Canada
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Wang Q, Ren Z, Yue M, Zhao Y, Wang B, Zhao Z, Wen B, Hong Y, Chen Y, Zhao T, Wang N, Zhao P, Hong Y, Han X. A model for the diagnosis of anxiety in patients with epilepsy based on phase locking value and Lempel-Ziv complexity features of the electroencephalogram. Brain Res 2024; 1824:148662. [PMID: 37924926 DOI: 10.1016/j.brainres.2023.148662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/09/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVE Anxiety disorders (AD) are critical factors that significantly (about one-fifth) impact the quality of life (QoL) in patients with epilepsy (PWE). Objective diagnostic methods have contributed to the identification of PWE susceptible to AD. This study aimed to identify AD in PWE by constructing a diagnostic model based on the phase locking value (PLV) and Lempel-Ziv Complexity (LZC) features of the electroencephalogram (EEG). METHODS EEG data from 131 patients with epilepsy (PWE) were enrolled in this study. Patients were divided into two groups, anxiety disorder (AD, n = 61) and non-anxiety disorder (NAD, n = 70), according to the Hamilton Rating Scale for Anxiety (HAM-A). Support vector machine (SVM) and K-Nearest-Neighbor(KNN) algorithms were used to construct three models - the PLVEEG, LZCEEG, and PLVEEG + LZCEEG feature models. Finally, the area under the receiver operating characteristic curve (AUC) and statistical analyses were performed to evaluate the model performance. RESULTS The efficiency of the KNN-based PLCEEG + LZCEEG feature model was the best, and the accuracy, precision, recall, F1-score, and AUC of the model after five-fold cross-validations scores were 87.89 %, 82.27 %, 98.33 %, 88.95 %, and 0.89, respectively. When the model efficiency was optimal, 29 EEG features were suggested. Further analysis of these features indicated 22 EEG features that were significantly different between the two groups, including 50 % features of the alpha (α)-band. CONCLUSIONS The PLVEEG + LZCEEG model features can identify AD in PWE. The PLVEEG and LZCEEG characteristics of the α-band may further be explored as potential biomarkers for AD in PWE.
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Affiliation(s)
- Qi Wang
- Department of Neurology, Zhengzhou University People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Zhe Ren
- Department of Neurology, Zhengzhou University People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Mengyan Yue
- Department of Rehabilitation, The First Hospital of Shanxi Medical University, Shanxi Province, Taiyuan 030000, China
| | - Yibo Zhao
- Department of Neurology, Zhengzhou University People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Bin Wang
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453000, Henan Province, China
| | - Bin Wen
- School of Life Sciences and Technology, Xi'an Jiaotong University, Xi'an 710000, Shaanxi Province, China
| | - Yang Hong
- Department of Neurology, People's Hospital of Henan University, Zhengzhou 450003, Henan Province, China
| | - Yanan Chen
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Ting Zhao
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Na Wang
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Pan Zhao
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China
| | - Yingxing Hong
- Department of Neurology, People's Hospital of Henan University, Zhengzhou 450003, Henan Province, China
| | - Xiong Han
- Department of Neurology, Henan Provincial People's Hospital, Henan Province, Zhengzhou 450003, China.
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Wolfson SS, Kirk I, Waldie K, King C. EEG Complexity Analysis of Brain States, Tasks and ASD Risk. ADVANCES IN NEUROBIOLOGY 2024; 36:733-759. [PMID: 38468061 DOI: 10.1007/978-3-031-47606-8_37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Autism spectrum disorder is an increasingly prevalent and debilitating neurodevelopmental condition and an electroencephalogram (EEG) diagnostic challenge. Despite large amounts of electrophysiological research over many decades, an EEG biomarker for autism spectrum disorder (ASD) has not been found. We hypothesized that reductions in complex dynamical system behaviour in the human central nervous system as part of the macroscale neuronal function during cognitive processes might be detectable in whole EEG for higher-risk ASD adults. In three studies, we compared the medians of correlation dimension, largest Lyapunov exponent, Higuchi's fractal dimension, multiscale entropy, multifractal detrended fluctuation analysis and Kolmogorov complexity during resting, cognitive and social skill tasks in 20 EEG channels of 39 adults over a range of ASD risk. We found heterogeneous complexity distribution with clusters of hierarchical sequences pointing to potential cognitive processing differences, but no clear distinction based on ASD risk. We suggest that there is indication of statistically significant differences between complexity measures of brain states and tasks. Though replication of our studies is needed with a larger sample, we believe that our electrophysiological and analytic approach has potential as a biomarker for earlier ASD diagnosis.
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Affiliation(s)
- Stephen S Wolfson
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand.
| | - Ian Kirk
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
| | - Karen Waldie
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
| | - Chris King
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
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Pham MD, D’Angiulli A, Dehnavi MM, Chhabra R. From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems? Brain Sci 2023; 13:1316. [PMID: 37759917 PMCID: PMC10526461 DOI: 10.3390/brainsci13091316] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
We examine the challenging "marriage" between computational efficiency and biological plausibility-A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man's land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics.
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Affiliation(s)
- Martin Do Pham
- Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (M.D.P.); (M.M.D.)
| | - Amedeo D’Angiulli
- Department of Neuroscience, Carleton University, Ottawa, ON K1S 5B6, Canada;
| | - Maryam Mehri Dehnavi
- Department of Computer Science, University of Toronto, Toronto, ON M5S 1A1, Canada; (M.D.P.); (M.M.D.)
| | - Robin Chhabra
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
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Zilio F, Gomez-Pilar J, Chaudhary U, Fogel S, Fomina T, Synofzik M, Schöls L, Cao S, Zhang J, Huang Z, Birbaumer N, Northoff G. Altered brain dynamics index levels of arousal in complete locked-in syndrome. Commun Biol 2023; 6:757. [PMID: 37474587 PMCID: PMC10359418 DOI: 10.1038/s42003-023-05109-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 07/06/2023] [Indexed: 07/22/2023] Open
Abstract
Complete locked-in syndrome (CLIS) resulting from late-stage amyotrophic lateral sclerosis (ALS) is characterised by loss of motor function and eye movements. The absence of behavioural indicators of consciousness makes the search for neuronal correlates as possible biomarkers clinically and ethically urgent. EEG-based measures of brain dynamics such as power-law exponent (PLE) and Lempel-Ziv complexity (LZC) have been shown to have explanatory power for consciousness and may provide such neuronal indices for patients with CLIS. Here, we validated PLE and LZC (calculated in a dynamic way) as benchmarks of a wide range of arousal states across different reference states of consciousness (e.g., awake, sleep stages, ketamine, sevoflurane). We show a tendency toward high PLE and low LZC, with high intra-subject fluctuations and inter-subject variability in a cohort of CLIS patients with values graded along different arousal states as in our reference data sets. In conclusion, changes in brain dynamics indicate altered arousal in CLIS. Specifically, PLE and LZC are potentially relevant biomarkers to identify or diagnose the arousal level in CLIS and to determine the optimal time point for treatment, including communication attempts.
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Affiliation(s)
- Federico Zilio
- Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, Padua, Italy.
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - Ujwal Chaudhary
- BrainPortal Technologies GmbH, Mannheim, Germany
- ALS Voice gGmbH, Mössingen, Germany
| | - Stuart Fogel
- School of Psychology, University of Ottawa, Ottawa, Canada
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
| | - Tatiana Fomina
- Department for Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Ludger Schöls
- Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Shumei Cao
- Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jun Zhang
- Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Zirui Huang
- Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Niels Birbaumer
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
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Hernandez CI, Kargarnovin S, Hejazi S, Karwowski W. Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis. Front Comput Neurosci 2023; 17:1207067. [PMID: 37457899 PMCID: PMC10344458 DOI: 10.3389/fncom.2023.1207067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/14/2023] [Indexed: 07/18/2023] Open
Abstract
Background Considering that brain activity involves communication between millions of neurons in a complex network, nonlinear analysis is a viable tool for studying electroencephalography (EEG). The main objective of this review was to collate studies that utilized chaotic measures and nonlinear dynamical analysis in EEG of multiple sclerosis (MS) patients and to discuss the contributions of chaos theory techniques to understanding, diagnosing, and treating MS. Methods Using the preferred reporting items for systematic reviews and meta-analysis (PRISMA), the databases EbscoHost, IEEE, ProQuest, PubMed, Science Direct, Web of Science, and Google Scholar were searched for publications that applied chaos theory in EEG analysis of MS patients. Results A bibliographic analysis was performed using VOSviewer software keyword co-occurrence analysis indicated that MS was the focus of the research and that research on MS diagnosis has shifted from conventional methods, such as magnetic resonance imaging, to EEG techniques in recent years. A total of 17 studies were included in this review. Among the included articles, nine studies examined resting-state, and eight examined task-based conditions. Conclusion Although nonlinear EEG analysis of MS is a relatively novel area of research, the findings have been demonstrated to be informative and effective. The most frequently used nonlinear dynamics analyses were fractal dimension, recurrence quantification analysis, mutual information, and coherence. Each analysis selected provided a unique assessment to fulfill the objective of this review. While considering the limitations discussed, there is a promising path forward using nonlinear analyses with MS data.
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Frohlich J, Chiang JN, Mediano PAM, Nespeca M, Saravanapandian V, Toker D, Dell'Italia J, Hipp JF, Jeste SS, Chu CJ, Bird LM, Monti MM. Neural complexity is a common denominator of human consciousness across diverse regimes of cortical dynamics. Commun Biol 2022; 5:1374. [PMID: 36522453 PMCID: PMC9755290 DOI: 10.1038/s42003-022-04331-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
What is the common denominator of consciousness across divergent regimes of cortical dynamics? Does consciousness show itself in decibels or in bits? To address these questions, we introduce a testbed for evaluating electroencephalogram (EEG) biomarkers of consciousness using dissociations between neural oscillations and consciousness caused by rare genetic disorders. Children with Angelman syndrome (AS) exhibit sleep-like neural dynamics during wakefulness. Conversely, children with duplication 15q11.2-13.1 syndrome (Dup15q) exhibit wake-like neural dynamics during non-rapid eye movement (NREM) sleep. To identify highly generalizable biomarkers of consciousness, we trained regularized logistic regression classifiers on EEG data from wakefulness and NREM sleep in children with AS using both entropy measures of neural complexity and spectral (i.e., neural oscillatory) EEG features. For each set of features, we then validated these classifiers using EEG from neurotypical (NT) children and abnormal EEGs from children with Dup15q. Our results show that the classification performance of entropy-based EEG biomarkers of conscious state is not upper-bounded by that of spectral EEG features, which are outperformed by entropy features. Entropy-based biomarkers of consciousness may thus be highly adaptable and should be investigated further in situations where spectral EEG features have shown limited success, such as detecting covert consciousness or anesthesia awareness.
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Affiliation(s)
- Joel Frohlich
- Department of Psychology, University of California Los Angeles, 90095, Pritzker Hall, Los Angeles, CA, USA.
- Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tuebingen, Tuebingen, Germany.
| | - Jeffrey N Chiang
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Mark Nespeca
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
- Department of Neurology, Rady Children's Hospital San Diego, San Diego, CA, USA
| | - Vidya Saravanapandian
- Center for Autism Research and Treatment, University of California Los Angeles, Semel Institute for Neuroscience, Los Angeles, CA, USA
| | - Daniel Toker
- Department of Psychology, University of California Los Angeles, 90095, Pritzker Hall, Los Angeles, CA, USA
| | - John Dell'Italia
- Institute for Advanced Consciousness Studies, Santa Monica, CA, USA
| | - Joerg F Hipp
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland
| | - Shafali S Jeste
- Center for Autism Research and Treatment, University of California Los Angeles, Semel Institute for Neuroscience, Los Angeles, CA, USA
- Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lynne M Bird
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
- Division of Genetics/Dysmorphology, Rady Children's Hospital - San Diego, San Diego, CA, USA
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, 90095, Pritzker Hall, Los Angeles, CA, USA
- Deptment of Neurosurgery, UCLA Brain Injury Research Center, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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13
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Wang Y, Lu C, Zhang M, Wu J, Tang Z. Research on the Recognition of Various Muscle Fatigue States in Resistance Strength Training. Healthcare (Basel) 2022; 10:2292. [PMID: 36421616 PMCID: PMC9691149 DOI: 10.3390/healthcare10112292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/10/2022] [Accepted: 11/12/2022] [Indexed: 09/08/2024] Open
Abstract
Instantly and accurately identifying the state of dynamic muscle fatigue in resistance training can help fitness trainers to build a more scientific and reasonable training program. By investigating the isokinetic flexion and extension strength training of the knee joint, this paper tried to extract surface electromyogram (sEMG) features and establish recognition models to classify muscle states of the target muscles in the isokinetic strength training of the knee joint. First, an experiment was carried out to collect the sEMG signals of the target muscles. Second, two nonlinear dynamic indexes, wavelet packet entropy (WPE) and power spectrum entropy (PSE), were extracted from the obtained sEMG signals to verify the feasibility of characterizing muscle fatigue. Third, a convolutional neural network (CNN) recognition model was constructed and trained with the obtained sEMG experimental data to enable the extraction and recognition of EMG deep features. Finally, the CNN recognition model was compared with multiple support vector machines (Multi-SVM) and multiple linear discriminant analysis (Multi-LDA). The results showed that the CNN model had a better classification accuracy. The overall recognition accuracy of the CNN model applied to the test data (91.38%) was higher than that of the other two models, which verified that the CNN dynamic fatigue recognition model based on subjective and objective information feedback had better recognition performance. Furthermore, training on a larger dataset could further improve the recognition accuracy of the CNN recognition model.
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Affiliation(s)
| | - Chunfu Lu
- Industrial Design Department, Zhejiang University of Technology, Hangzhou 310023, China
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14
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Lau ZJ, Pham T, Chen SHA, Makowski D. Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur J Neurosci 2022; 56:5047-5069. [PMID: 35985344 PMCID: PMC9826422 DOI: 10.1111/ejn.15800] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 01/11/2023]
Abstract
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
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Affiliation(s)
- Zen J. Lau
- School of Social SciencesNanyang Technological UniversitySingapore
| | - Tam Pham
- School of Social SciencesNanyang Technological UniversitySingapore
| | - S. H. Annabel Chen
- School of Social SciencesNanyang Technological UniversitySingapore,Centre for Research and Development in LearningNanyang Technological UniversitySingapore,Lee Kong Chian School of MedicineNanyang Technological UniversitySingapore,National Institute of EducationNanyang Technological UniversitySingapore
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15
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Fuentes N, Garcia A, Guevara R, Orofino R, Mateos DM. Complexity of Brain Dynamics as a Correlate of Consciousness in Anaesthetized Monkeys. Neuroinformatics 2022; 20:1041-1054. [PMID: 35511398 DOI: 10.1007/s12021-022-09586-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2022] [Indexed: 12/31/2022]
Abstract
The use of anaesthesia is a fundamental tool in the investigation of consciousness. Anesthesia procedures allow to investigate different states of consciousness from sedation to deep anesthesia within controlled scenarios. In this study we use information quantifiers to measure the complexity of electrocorticogram recordings in monkeys. We apply these metrics to compare different stages of general anesthesia for evaluating consciousness in several anesthesia protocols. We find that the complexity of brain activity can be used as a correlate of consciousness. For two of the anaesthetics used, propofol and medetomidine, we find that the anaesthetised state is accompanied by a reduction in the complexity of brain activity. On the other hand we observe that use of ketamine produces an increase in complexity measurements. We relate this observation with increase activity within certain brain regions associated with the ketamine used doses. Our measurements indicate that complexity of brain activity is a good indicator for a general evaluation of different levels of consciousness awareness, both in anesthetized and non anesthetizes states.
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Affiliation(s)
- Nicolas Fuentes
- Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Alexis Garcia
- Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Ramón Guevara
- Department of Physics and Astronomy, University of Padua, Padua, Italy
| | - Roberto Orofino
- Hospital de Ninos Pedro de Elizalde, Buenos Aires, Argentina.,Hospital Español, La Plata, Argentina
| | - Diego M Mateos
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina. .,Facultad de Ciencia y Tecnología. Universidad Autónoma de Entre Ríos (UADER), Oro Verde, Entre Ríos, Argentina. .,Instituto de Matemática Aplicada del Litoral (IMAL-CONICET-UNL), CCT CONICET, Santa Fé, Argentina.
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16
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Zhao Z, Niu Y, Zhao X, Zhu Y, Shao Z, Wu X, Wang C, Gao X, Wang C, Xu Y, Zhao J, Gao Z, Ding J, Yu Y. EEG microstate in first-episode drug-naive adolescents with depression. J Neural Eng 2022; 19. [PMID: 35952647 DOI: 10.1088/1741-2552/ac88f6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/11/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND A growing number of studies have revealed significant abnormalities in EEG microstate in patients with depression, but these findings may be affected by medication. Therefore, how the EEG microstates abnormally change in patients with depression in the early stage and without the influence of medication has not been investigated so far. METHODS Resting-state EEG data and Hamilton Depression Rating Scale (HDRS) were collected from 34 first-episode drug-naïve adolescent with depression and 34 matched healthy controls. EEG microstate analysis was applied and nonlinear characteristics of EEG microstate sequences were studied by sample entropy and Lempel-Ziv complexity (LZC). The microstate temporal parameters and complexity were tried to train a SVM for classification of patients with depression. RESULTS Four typical EEG microstate topographies were obtained in both groups, but microstate C topography was significantly abnormal in depression patients. The duration of microstate B, C, D and the occurrence and coverage of microstate B significantly increased, the occurrence and coverage of microstate A, C reduced significantly in depression group. Sample entropy and LZC in the depression group were abnormally increased and were negatively correlated with HDRS. When the combination of EEG microstate temporal parameters and complexity of microstate sequence was used to classify patients with depression from healthy controls, a classification accuracy of 90.9% was obtained. CONCLUSION Abnormal EEG microstate has appeared in early depression, reflecting an underlying abnormality in configuring neural resources and transitions between distinct brain network states. EEG microstate can be used as a neurophysiological biomarker for early auxiliary diagnosis of depression.
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Affiliation(s)
- Zongya Zhao
- Xinxiang Medical University, College of Medical Engineering, Xinxiang, Henan, 453003, CHINA
| | - Yanxiang Niu
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Xiaofeng Zhao
- First Affiliated Hospital of Zhengzhou University, Department of Psychiatry, Zhengzhou, 450000, CHINA
| | - Yu Zhu
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Zhenpeng Shao
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Xingyang Wu
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Chong Wang
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Xudong Gao
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Chang Wang
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Yongtao Xu
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Junqiang Zhao
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Zhixian Gao
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Junqing Ding
- First Affiliated Hospital of Zhengzhou University Zhengzhou, Department of Neurology, Zhengzhou, 450000, CHINA
| | - Yi Yu
- Xinxiang Medical University, college of Biomedical Engineering, Xinxiang, 453003, CHINA
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17
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Golesorkhi M, Gomez-Pilar J, Çatal Y, Tumati S, Yagoub MCE, Stamatakis EA, Northoff G. From temporal to spatial topography: hierarchy of neural dynamics in higher- and lower-order networks shapes their complexity. Cereb Cortex 2022; 32:5637-5653. [PMID: 35188968 PMCID: PMC9753094 DOI: 10.1093/cercor/bhac042] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 01/25/2023] Open
Abstract
The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel-Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow-fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former; (ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks; (iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest-task change along the lines of lower- and higher-order networks; and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.
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Affiliation(s)
| | | | - Yasir Çatal
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa ON K1Z 7K4, Canada
| | - Shankar Tumati
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa ON K1Z 7K4, Canada
| | - Mustapha C E Yagoub
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa ON K1Z 7K4, Canada
| | - Emanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge CB1 0SP, United Kingdom
| | - Georg Northoff
- Corresponding author: Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health, Royal Ottawa Mental Health Centre and University of Ottawa, Ottawa, Canada.
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18
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Avots E, Jermakovs K, Bachmann M, Päeske L, Ozcinar C, Anbarjafari G. Ensemble Approach for Detection of Depression Using EEG Features. ENTROPY 2022; 24:e24020211. [PMID: 35205506 PMCID: PMC8871180 DOI: 10.3390/e24020211] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/26/2022] [Accepted: 01/27/2022] [Indexed: 11/21/2022]
Abstract
Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.
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Affiliation(s)
- Egils Avots
- iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia; (E.A.); (K.J.); (C.O.)
| | - Klāvs Jermakovs
- iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia; (E.A.); (K.J.); (C.O.)
| | - Maie Bachmann
- Biosignal Processing Laboratory, Tallinn University of Technology, 19086 Tallinn, Estonia; (M.B.); (L.P.)
| | - Laura Päeske
- Biosignal Processing Laboratory, Tallinn University of Technology, 19086 Tallinn, Estonia; (M.B.); (L.P.)
| | - Cagri Ozcinar
- iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia; (E.A.); (K.J.); (C.O.)
| | - Gholamreza Anbarjafari
- iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia; (E.A.); (K.J.); (C.O.)
- PwC Advisory, 00180 Helsinki, Finland
- Faculty of Egineering, Hasan Kalyoncu University, 27000 Gaziantep, Turkey
- Correspondence: ; Tel.: +90-372-737-4855
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19
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Regularity and randomness in ageing: Differences in resting-state EEG complexity measured by largest Lyapunov exponent. NEUROIMAGE: REPORTS 2021. [DOI: 10.1016/j.ynirp.2021.100054] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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20
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Linear and Nonlinear Quantitative EEG Analysis during Neutral Hypnosis following an Opened/Closed Eye Paradigm. Symmetry (Basel) 2021. [DOI: 10.3390/sym13081423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Hypnotic susceptibility is a major factor influencing the study of the neural correlates of hypnosis using EEG. In this context, while its effects on the response to hypnotic suggestions are undisputed, less attention has been paid to “neutral hypnosis” (i.e., the hypnotic condition in absence of suggestions). Furthermore, although an influence of opened and closed eye condition onto hypnotizability has been reported, a systematic investigation is still missing. Here, we analyzed EEG signals from 34 healthy subjects with low (LS), medium (MS), and (HS) hypnotic susceptibility using power spectral measures (i.e., TPSD, PSD) and Lempel-Ziv-Complexity (i.e., LZC, fLZC). Indeed, LZC was found to be more suitable than other complexity measures for EEG analysis, while it has been never used in the study of hypnosis. Accordingly, for each measure, we investigated within-group differences between rest and neutral hypnosis, and between opened-eye/closed-eye conditions under both rest and neutral hypnosis. Then, we evaluated between-group differences for each experimental condition. We observed that, while power estimates did not reveal notable differences between groups, LZC and fLZC were able to distinguish between HS, MS, and LS. In particular, we found a left frontal difference between HS and LS during closed-eye rest. Moreover, we observed a symmetric pattern distinguishing HS and LS during closed-eye hypnosis. Our results suggest that LZC is better capable of discriminating subjects with different hypnotic susceptibility, as compared to standard power analysis.
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Multiscale Permutation Lempel-Ziv Complexity Measure for Biomedical Signal Analysis: Interpretation and Application to Focal EEG Signals. ENTROPY 2021; 23:e23070832. [PMID: 34210034 PMCID: PMC8307896 DOI: 10.3390/e23070832] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 06/23/2021] [Accepted: 06/25/2021] [Indexed: 11/18/2022]
Abstract
This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals. Futures from an original multiscale permutation Lempel–Ziv complexity measure (MPLZC) were obtained. MPLZC measure combines a multiscale structure, ordinal analysis, and permutation Lempel–Ziv complexity for quantifying the dynamic changes of an electroencephalogram (EEG). We also show the dependency of MPLZC on several straight-forward signal processing concepts, which appear in biomedical EEG activity via a set of synthetic signals. The main material of the study consists of EEG signals, which were obtained from the Bern-Barcelona EEG database. The signals were divided into two groups: focal EEG signals (n = 100) and non-focal EEG signals (n = 100); statistical analysis was performed by means of non-parametric Mann–Whitney test. The mean value of MPLZC results in the non-focal group are significantly higher than those in the focal group for scales above 1 (p < 0.05). The result indicates that the non-focal EEG signals are more complex. MPLZC feature sets are used for the least squares support vector machine (LS-SVM) classifier to classify into the focal and non-focal EEG signals. Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%.
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22
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Yun S, Jeong B. Aberrant EEG signal variability at a specific temporal scale in major depressive disorder. Clin Neurophysiol 2021; 132:1866-1877. [PMID: 34147011 DOI: 10.1016/j.clinph.2021.05.011] [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] [Received: 09/23/2020] [Revised: 04/17/2021] [Accepted: 05/18/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Signal variability is linked to irregularities in time series caused by intrinsic nonlinearities of the neural system and can be measured on variable temporal scales over short time series. By measuring refined complex multiscale permutation entropy (RCMPE) from resting-state electroencephalography (EEG) data, we investigated the presence of a specific range of time scales characterizing major depressive disorder (MDD). METHOD We used an EEG dataset acquired from 22 MDD patients and 22 healthy controls in the eyes-closed (EC) and eyes-open (EO) states available on the PRED + CT website. Signal variability in both the EC and EO states was compared between the two groups, and their relationship to depressive symptom severity was examined. RESULTS In the EC state, the RCMPE was higher in the MDD group than in the control group on a coarse temporal scale, approximately 20-32 ms, at almost all sensors. It also showed a negative correlation with depressive symptom severity on a fine temporal scale, approximately 2-26 ms, in the frontal, right temporal, and left parietal sensor areas in MDD. The EO state revealed a group difference but no relationship with depressive symptom severity. CONCLUSION Our results suggested that the diagnosis of MDD as a trait and the severity of depressive symptoms as a state are linked to EEG signal variability on the coarse temporal scale and the fine scale in the resting state, respectively. SIGNIFICANCE Signal variability reflects different characteristics of depression depending on the temporal scale.
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Affiliation(s)
- Seokho Yun
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Bumseok Jeong
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
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23
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Mayor D, Panday D, Kandel HK, Steffert T, Banks D. CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals. ENTROPY 2021; 23:e23030321. [PMID: 33800469 PMCID: PMC7998823 DOI: 10.3390/e23030321] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. METHODS Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. RESULTS The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. CONCLUSIONS We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.
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Affiliation(s)
- David Mayor
- School of Health and Social Work, University of Hertfordshire, Hatfield AL10 9AB, UK
- Correspondence:
| | - Deepak Panday
- School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
| | - Hari Kala Kandel
- Department of Computing, Goldsmiths College, University of London, New Cross, London SE14 6NW, UK;
| | - Tony Steffert
- MindSpire, Napier House, 14-16 Mount Ephraim Rd, Tunbridge Wells TN1 1EE, UK;
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
| | - Duncan Banks
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
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24
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Zaboski BA, Stern EF, Skosnik PD, Pittenger C. Electroencephalographic Correlates and Predictors of Treatment Outcome in OCD: A Brief Narrative Review. Front Psychiatry 2021; 12:703398. [PMID: 34408681 PMCID: PMC8365146 DOI: 10.3389/fpsyt.2021.703398] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 06/21/2021] [Indexed: 12/28/2022] Open
Abstract
Electroencephalography (EEG) measures the brain's electrical activity with high temporal resolution. In comparison to neuroimaging modalities such as MRI or PET, EEG is relatively cheap, non-invasive, portable, and simple to administer, making it an attractive tool for clinical deployment. Despite this, studies utilizing EEG to investigate obsessive-compulsive disorder (OCD) are relatively sparse. This contrasts with a robust literature using other brain imaging methodologies. The present review examines studies that have used EEG to examine predictors and correlates of response in OCD and draws tentative conclusions that may guide much needed future work. Key findings include a limited literature base; few studies have attempted to predict clinical change from EEG signals, and they are confounded by the effects of both pharmacotherapy and psychotherapy. The most robust literature, consisting of several studies, has examined event-related potentials, including the P300, which several studies have reported to be abnormal at baseline in OCD and to normalize with treatment; but even here the literature is quite heterogeneous, and more work is needed. With more robust research, we suggest that the relatively low cost and convenience of EEG, especially in comparison to fMRI and PET, make it well-suited to the development of feasible personalized treatment algorithms.
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Affiliation(s)
- Brian A Zaboski
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Elisa F Stern
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Patrick D Skosnik
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Christopher Pittenger
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
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25
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He M, Zhao WB, Nguyen MN, Kiriazis H, Li YQ, Hu H, Du XJ. Association between heart rate variability indices and features of spontaneous ventricular tachyarrhythmias in mice. Clin Exp Pharmacol Physiol 2020; 47:1193-1202. [PMID: 32027390 DOI: 10.1111/1440-1681.13275] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/02/2020] [Accepted: 02/03/2020] [Indexed: 12/20/2022]
Abstract
Direct evidence is limited for the association between heart rate variability (HRV) indices and ventricular tachyarrhythmias (VTAs). While galectin-3 (Gal-3) is regarded as a causal factor for cardiac remodelling and a biomarker for arrhythmias, its regulation on VTAs and HVR is unknown. Using aged transgenic (TG) mice with cardiac overexpression of β2 -adrenoceptors and spontaneous VTAs, we studied whether changes in HRV indices correlated with the severity of VTAs, and whether Gal-3 gene knockout (KO) in TG mice might limit VTA. Body-surface ECG was recorded (10-minute period) in 9- to 10-month-old mice of non-transgenic (nTG), TG and TG × Gal-3 knockout (TG/KO). Time-domain, frequency-domain and nonlinear-domain HRV indices were calculated using the R-R intervals extracted from ECG signals and compared with frequency of VTAs. TG and TG/KO mice developed frequent VTAs and showed significant changes in certain time-domain and nonlinear-domain HRV indices relative to nTG mice. The severity of VTAs in TG and TG/KO mice in combination, estimated by VTA counts and arrhythmia score, was significantly correlated with certain time-domain and nonlinear-domain HRV indices. In conclusion, significant changes in HRV indices were evident and correlated with the severity of spontaneous VTAs in TG mice. The frequency of VTA and HRV indices were largely comparable between TG and TG/KO mice. Deletion of Gal-3 in TG mice altered certain HRV indices implying influence by neuronally localized Gal-3 on autonomic nervous activity.
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Affiliation(s)
- Mi He
- Department of Cardiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- School of Biomedical Engineering and Imaging Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - Wei-Bo Zhao
- Department of Cardiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - My-Nhan Nguyen
- Experimental Cardiology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Vic, Australia
| | - Helen Kiriazis
- Experimental Cardiology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Vic, Australia
| | - Yong-Qin Li
- School of Biomedical Engineering and Imaging Medicine, Third Military Medical University (Army Medical University), Chongqing, China
| | - Houyuan Hu
- Department of Cardiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xiao-Jun Du
- Experimental Cardiology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Vic, Australia
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi'an Jiaotong University (Health Science Center), Xi'an, China
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26
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Frohlich J, Bird LM, Dell'Italia J, Johnson MA, Hipp JF, Monti MM. High-voltage, diffuse delta rhythms coincide with wakeful consciousness and complexity in Angelman syndrome. Neurosci Conscious 2020; 2020:niaa005. [PMID: 32551137 PMCID: PMC7293820 DOI: 10.1093/nc/niaa005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 02/24/2020] [Accepted: 03/09/2020] [Indexed: 11/23/2022] Open
Abstract
Abundant evidence from slow wave sleep, anesthesia, coma, and epileptic seizures links high-voltage, slow electroencephalogram (EEG) activity to loss of consciousness. This well-established correlation is challenged by the observation that children with Angelman syndrome (AS), while fully awake and displaying volitional behavior, display a hypersynchronous delta (1–4 Hz) frequency EEG phenotype typical of unconsciousness. Because the trough of the delta oscillation is associated with down-states in which cortical neurons are silenced, the presence of volitional behavior and wakefulness in AS amidst diffuse delta rhythms presents a paradox. Moreover, high-voltage, slow EEG activity is generally assumed to lack complexity, yet many theories view functional brain complexity as necessary for consciousness. Here, we use abnormal cortical dynamics in AS to assess whether EEG complexity may scale with the relative level of consciousness despite a background of hypersynchronous delta activity. As characterized by multiscale metrics, EEGs from 35 children with AS feature significantly greater complexity during wakefulness compared with sleep, even when comparing the most pathological segments of wakeful EEG to the segments of sleep EEG least likely to contain conscious mentation and when factoring out delta power differences across states. These findings (i) warn against reverse inferring an absence of consciousness solely on the basis of high-amplitude EEG delta oscillations, (ii) corroborate rare observations of preserved consciousness under hypersynchronization in other conditions, (iii) identify biomarkers of consciousness that have been validated under conditions of abnormal cortical dynamics, and (iv) lend credence to theories linking consciousness with complexity.
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Affiliation(s)
- Joel Frohlich
- Department of Psychology, University of California Los Angeles, 3423 Franz Hall, Los Angeles, CA, USA
| | - Lynne M Bird
- Department of Pediatrics, University of California, San Diego, CA, USA.,Division of Genetics/Dysmorphology, Rady Children's Hospital San Diego, San Diego, CA, USA
| | - John Dell'Italia
- Department of Psychology, University of California Los Angeles, 3423 Franz Hall, Los Angeles, CA, USA
| | - Micah A Johnson
- Department of Psychology, University of California Los Angeles, 3423 Franz Hall, Los Angeles, CA, USA
| | - Joerg F Hipp
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, 3423 Franz Hall, Los Angeles, CA, USA.,Department of Neurosurgery, UCLA Brain Injury Research Center, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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27
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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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28
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Neural variability quenching during decision-making: Neural individuality and its prestimulus complexity. Neuroimage 2019; 192:1-14. [DOI: 10.1016/j.neuroimage.2019.02.070] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 01/31/2019] [Accepted: 02/27/2019] [Indexed: 11/20/2022] Open
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29
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Xu R, Zhang C, He F, Zhao X, Qi H, Zhou P, Zhang L, Ming D. How Physical Activities Affect Mental Fatigue Based on EEG Energy, Connectivity, and Complexity. Front Neurol 2018; 9:915. [PMID: 30429822 PMCID: PMC6220083 DOI: 10.3389/fneur.2018.00915] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Accepted: 10/09/2018] [Indexed: 11/13/2022] Open
Abstract
Many studies have verified that there is an interaction between physical activities and mental fatigue. However, few studies are focused on the effect of physical activities on mental fatigue. This study was to analyze the states of mental fatigue based on electroencephalography (EEG) and investigate how physical activities affect mental fatigue. Fourteen healthy participants participated in an experiment including a 2-back mental task (the control) and the same mental task with cycling simultaneously (physical-mental task). Each experiment consisted of three 20 min fatigue-inducing sessions repeatedly (mental fatigue for mental tasks or mental fatigue plus physical activities for physical-mental tasks). During the evaluation sessions (before and after the fatigue-inducing sessions), the states of the participants were assessed by EEG parameters. Wavelet Packet Energy (WPE), Spectral Coherence Value (SCV), and Lempel-Ziv Complexity (LZC) were used to indicate mental fatigue from the perspectives of activation, functional connectivity, and complexity of the brain. The indices are the beta band energy Eβ, the energy ratio Eα/β, inter-hemispheric SCV of beta band SCVβ and LZC. The statistical analysis shows that mental fatigue was detected by Eβ, Eα/β, SCVβ, and LZC in physical-mental task. The slopes of the linear fit on these indices verified that the mental fatigue increased more fast during physical-mental task. It is concluded form the result that physical activities can enhance the mental fatigue and speed up the fatigue process based on brain activation, functional connection, and complexity. This result differs from the traditional opinion that physical activities have no influence on mental fatigue, and finds that physical activities can increase mental fatigue. This finding helps fatigue management through exercise instruction.
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Affiliation(s)
- Rui Xu
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Chuncui Zhang
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Feng He
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xin Zhao
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Hongzhi Qi
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Peng Zhou
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lixin Zhang
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Dong Ming
- Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.,Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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30
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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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31
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Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool. ENTROPY 2017. [DOI: 10.3390/e19120673] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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32
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Measuring complexity to infer changes in the dynamics of ecological systems under stress. ECOLOGICAL COMPLEXITY 2017. [DOI: 10.1016/j.ecocom.2016.08.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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33
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Pu J, Xu H, Wang Y, Cui H, Hu Y. Combined nonlinear metrics to evaluate spontaneous EEG recordings from chronic spinal cord injury in a rat model: a pilot study. Cogn Neurodyn 2016; 10:367-73. [PMID: 27668016 PMCID: PMC5018015 DOI: 10.1007/s11571-016-9394-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 06/09/2016] [Accepted: 06/23/2016] [Indexed: 11/26/2022] Open
Abstract
Spinal cord injury (SCI) is a high-cost disability and may cause permanent loss of movement and sensation below the injury location. The chance of cure in human after SCI is extremely limited. Instead, neural regeneration could have been seen in animals after SCI, and such regeneration could be retarded by blocking neural plasticity pathways, showing the importance of neural plasticity in functional recovery. As an indicator of nonlinear dynamics in the brain, sample entropy was used here in combination with detrended fluctuation analysis (DFA) and Kolmogorov complexity to quantify functional plasticity changes in spontaneous EEG recordings of rats before and after SCI. The results showed that the sample entropy values were decreased at the first day following injury then gradually increased during recovery. DFA and Kolmogorov complexity results were in consistent with sample entropy, showing the complexity of the EEG time series was lost after injury and partially regained in 1 week. The tendency to regain complexity is in line with the observation of behavioral rehabilitation. A critical time point was found during the recovery process after SCI. Our preliminary results suggested that the combined use of these nonlinear dynamical metrics could provide a quantitative and predictive way to assess the change of neural plasticity in a spinal cord injury rat model.
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Affiliation(s)
- Jiangbo Pu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
| | - Hanhui Xu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
| | - Yazhou Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, People’s Republic of China
| | - Hongyan Cui
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
| | - Yong Hu
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, People’s Republic of China
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, People’s Republic of China
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Escudero J, Ibanez-Molina A, Iglesias-Parro S. Effect of the average delay and mean connectivity of the Kuramoto model on the complexity of the output electroencephalograms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:7873-6. [PMID: 26738117 DOI: 10.1109/embc.2015.7320217] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Cognitive functions result from the interplay of distributed brain areas operating in large-scale networks. These networks can be modelled with a number of parameters that represent their underlying dynamics. One particularly fruitful model to simulate key aspects of the large-scale brain networks is the Kuramoto model, which simulates the phase evolution of several weakly coupled oscillators that represent the mean oscillatory behavior of different cortical regions. Here, we inspected the dependency of two widespread nonlinear complexity markers, Sample Entropy (SampEn) and Lempel-Ziv Complexity (LZC), on EEG activity generated with a Kuramoto phase model where the time delay and connectivity strength among oscillators varied. We also added different levels of noise to the electroencephalogram (EEG) signals. Our results indicated that both complexity metrics reflected the changes in the delays and global synchrony levels, but we found that SampEn was slightly more sensitive to the state transition and its results were less affected by the presence of noise. These results help in the effort to understand the dynamics of EEG recordings and their relationship to large-scale networks.
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35
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Sun C, Chen R, Tian M, An X, Qi H, Xu M, Wang X, Ming D, Zhou P. Research on EEG features of attended & unattended vigilance. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:4662-4665. [PMID: 28325011 DOI: 10.1109/embc.2016.7591767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Vigilance refers to the brain alertness to objective things, including the concentration of attention and the capability to response emergencies. It is of great importance to study vigilance monitoring to avoid accidents caused by decrease of vigilance. In this study, traditional Mackworth Clock Test (MCT) was modified to induce decline of attended and unattended vigilance. We analyzed EEG features in different levels of attended vigilance and assessed unattended vigilance by amplitude of mismatch negative (MMN) which can be evoked by audio odd ball stimulations. The results show that with attended vigilance going down, power of theta and alpha bands of electroencephalogram (EEG) increases and Lempel-Ziv Complexities (LZC) show an undulated decline tendency. And with unattended vigilance going down, amplitude of MMN reduces significantly. This research has great reference value for the further vigilance study.
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36
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Artan NS. EEG analysis via multiscale Lempel-Ziv complexity for seizure detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:4535-4538. [PMID: 28269285 DOI: 10.1109/embc.2016.7591736] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Robust seizure detection and seizure prediction continues to be a challenge. Lempel-Ziv Complexity (LZC) is one of the features that has shown to be relevant in seizure detection. Recent work has shown that augmenting LZC can be beneficial to emphasize variations in amplitude or frequency when analyzing biomedical signals. In this paper, we present a first look into evaluating the feasibility of using a recently proposed feature stemmed from LZC, namely the Multiscale Lempel-Ziv Complexity (MLZC) for seizure detection. MLZC does not allow the high-frequency signal components to be overwhelmed by the low frequency signal components when calculating complexity values. We compare MLZC and LZC for identifying seizures for three cases and show MLZC can provide a clear separation between non-ictal and ictal periods for all three cases using a single threshold over 7 recordings and 7 seizures per patient, whereas LZC provided such a clear separation for only one of the patients.
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37
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The psychosis-like effects of Δ(9)-tetrahydrocannabinol are associated with increased cortical noise in healthy humans. Biol Psychiatry 2015; 78:805-13. [PMID: 25913109 PMCID: PMC4627857 DOI: 10.1016/j.biopsych.2015.03.023] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Revised: 03/06/2015] [Accepted: 03/20/2015] [Indexed: 11/20/2022]
Abstract
BACKGROUND Drugs that induce psychosis may do so by increasing the level of task-irrelevant random neural activity or neural noise. Increased levels of neural noise have been demonstrated in psychotic disorders. We tested the hypothesis that neural noise could also be involved in the psychotomimetic effects of delta-9-tetrahydrocannabinol (Δ(9)-THC), the principal active constituent of cannabis. METHODS Neural noise was indexed by measuring the level of randomness in the electroencephalogram during the prestimulus baseline period of an oddball task using Lempel-Ziv complexity, a nonlinear measure of signal randomness. The acute, dose-related effects of Δ(9)-THC on Lempel-Ziv complexity and signal power were studied in humans (n = 24) who completed 3 test days during which they received intravenous Δ(9)-THC (placebo, .015 and .03 mg/kg) in a double-blind, randomized, crossover, and counterbalanced design. RESULTS Δ(9)-THC increased neural noise in a dose-related manner. Furthermore, there was a strong positive relationship between neural noise and the psychosis-like positive and disorganization symptoms induced by Δ(9)-THC, which was independent of total signal power. Instead, there was no relationship between noise and negative-like symptoms. In addition, Δ(9)-THC reduced total signal power during both active drug conditions compared with placebo, but no relationship was detected between signal power and psychosis-like symptoms. CONCLUSIONS At doses that produced psychosis-like effects, Δ(9)-THC increased neural noise in humans in a dose-dependent manner. Furthermore, increases in neural noise were related with increases in Δ(9)-THC-induced psychosis-like symptoms but not negative-like symptoms. These findings suggest that increases in neural noise may contribute to the psychotomimetic effects of Δ(9)-THC.
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38
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Bai Y, Liang Z, Li X. A permutation Lempel-Ziv complexity measure for EEG analysis. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2015.04.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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39
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Inspecting temporal scales with non-linear signal features: A way to extract more information from brain activity? Clin Neurophysiol 2015; 126:435-6. [DOI: 10.1016/j.clinph.2014.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 07/11/2014] [Indexed: 11/21/2022]
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