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Yang Q, Liu L, Wang J, Zhang Y, Jiang N, Zhang M. Wavelet Entropy Analysis of Electroencephalogram Signals During Wake and Different Sleep Stages in Patients with Insomnia Disorder. Nat Sci Sleep 2024; 16:347-358. [PMID: 38606372 PMCID: PMC11007398 DOI: 10.2147/nss.s452017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/22/2024] [Indexed: 04/13/2024] Open
Abstract
Objective To investigate the changes in the wavelet entropy during wake and different sleep stages in patients with insomnia disorder. Methods Sixteen patients with insomnia disorder and sixteen normal controls were enrolled. They underwent scale assessment and two consecutive nights of polysomnography (PSG). Wavelet entropy analysis of electroencephalogram (EEG) signals recorded from all participants in the two groups was performed. The changes in the integral wavelet entropy (En) and individual-scale wavelet entropy (En(a)) during wake and different sleep stages in the two groups were observed, and the differences between the two groups were compared. Results The insomnia disorder group exhibited lower En during the wake stage, and higher En during the N3 stage compared with the normal control group (all P < 0.001). In terms of En(a), patients with insomnia disorder exhibited lower En(a) in the β and α frequency bands during the wake stage compared with normal controls (β band, P < 0.01; α band, P < 0.001), whereas they showed higher En(a) in the β and α frequency bands during the N3 stage than normal controls (β band, P < 0.001; α band, P < 0.001). Conclusion Wavelet entropy can reflect the changes in the complexity of EEG signals during wake and different sleep stages in patients with insomnia disorder, which provides a new method and insights about understanding of pathophysiological mechanisms of insomnia disorder. Wavelet entropy provides an objective indicator for assessing sleep quality.
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Affiliation(s)
- Qian Yang
- Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300070, People’s Republic of China
| | - Lingfeng Liu
- Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300070, People’s Republic of China
| | - Jing Wang
- Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China
| | - Ying Zhang
- Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China
| | - Nan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300354, People’s Republic of China
| | - Meiyun Zhang
- Department of Neurology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China
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Tsai WX, Tsai SJ, Lin CP, Huang NE, Yang AC. Exploring timescale-specific functional brain networks and their associations with aging and cognitive performance in a healthy cohort without dementia. Neuroimage 2024; 289:120540. [PMID: 38355076 DOI: 10.1016/j.neuroimage.2024.120540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/02/2024] [Accepted: 02/12/2024] [Indexed: 02/16/2024] Open
Abstract
INTRODUCTION Functional brain networks (FBNs) coordinate brain functions and are studied in fMRI using blood-oxygen-level-dependent (BOLD) signal correlations. Previous research links FBN changes to aging and cognitive decline, but various physiological factors influnce BOLD signals. Few studies have investigated the intrinsic components of the BOLD signal in different timescales using signal decomposition. This study aimed to explore differences between intrinsic FBNs and traditional BOLD-FBN, examining their associations with age and cognitive performance in a healthy cohort without dementia. MATERIALS AND METHODS A total of 396 healthy participants without dementia (men = 157; women = 239; age range = 20-85 years) were enrolled in this study. The BOLD signal was decomposed into several intrinsic signals with different timescales using ensemble empirical mode decomposition, and FBNs were constructed based on both the BOLD and intrinsic signals. Subsequently, network features-global efficiency and local efficiency values-were estimated to determine their relationship with age and cognitive performance. RESULTS The findings revealed that the global efficiency of traditional BOLD-FBN correlated significantly with age, with specific intrinsic FBNs contributing to these correlations. Moreover, local efficiency analysis demonstrated that intrinsic FBNs were more meaningful than traditional BOLD-FBN in identifying brain regions related to age and cognitive performance. CONCLUSIONS These results underscore the importance of exploring timescales of BOLD signals when constructing FBN and highlight the relevance of specific intrinsic FBNs to aging and cognitive performance. Consequently, this decomposition-based FBN-building approach may offer valuable insights for future fMRI studies.
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Affiliation(s)
- Wen-Xiang Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Norden E Huang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Albert C Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; Department of Medical Research, Taipei Veterans General Hospital, Taipei 11217, Taiwan; Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.
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Cataldo A, Criscuolo S, De Benedetto E, Masciullo A, Pesola M, Schiavoni R. A Novel Metric for Alzheimer's Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy. Bioengineering (Basel) 2024; 11:324. [PMID: 38671746 PMCID: PMC11048692 DOI: 10.3390/bioengineering11040324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution. In this regard, this work introduces a novel metric for AD detection by using multiscale fuzzy entropy (MFE) to assess brain complexity, offering clinicians an objective, cost-effective diagnostic tool to aid early intervention and patient care. To this purpose, brain entropy patterns in different frequency bands for 35 healthy subjects (HS) and 35 AD patients were investigated. Then, based on the resulting MFE values, a specific detection algorithm, able to assess brain complexity abnormalities that are typical of AD, was developed and further validated on 24 EEG test recordings. This MFE-based method achieved an accuracy of 83% in differentiating between HS and AD, with a diagnostic odds ratio of 25, and a Matthews correlation coefficient of 0.67, indicating its viability for AD diagnosis. Furthermore, the algorithm showed potential for identifying anomalies in brain complexity when tested on a subject with mild cognitive impairment (MCI), warranting further investigation in future research.
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Affiliation(s)
- Andrea Cataldo
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
| | - Sabatina Criscuolo
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Egidio De Benedetto
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Antonio Masciullo
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
| | - Marisa Pesola
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Raissa Schiavoni
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
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Li Z, Qu Z, Yin B, Yin L, Li X. Functional connectivity key feature analysis of cognitive impairment patients based on microstate brain network. Cereb Cortex 2024; 34:bhae043. [PMID: 38383723 DOI: 10.1093/cercor/bhae043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/21/2024] [Accepted: 01/23/2024] [Indexed: 02/23/2024] Open
Abstract
Mild cognitive impairment (MCI) is the initial phase of Alzheimer's disease (AD). The cognitive decline is linked to abnormal connectivity between different regions of the brain. Most brain network studies fail to consider the changes in brain patterns and do not reflect the dynamic pathological characteristics of patients. Therefore, this paper proposes a method for constructing brain networks based on microstate sequences. It also analyzes the microstate temporal parameters and introduces a new feature, the brain homeostasis coefficient (Bhc), to quantify the stability of patient brain connections. The results showed that microstate class B parameters were higher in the MCI than in the HC group. Additionally, the Bhc values in most channels of the MCI and AD groups were lower than those of the HC group, with the most significant differences observed in the right frontal lobe. These differences were statistically significant (P < 0.05). The findings indicate that connectivity in the right frontal lobe may be most severely disrupted in patients with cognitive impairment. Furthermore, the Montreal Cognitive Assessment score showed a strong positive correlation with Bhc. This suggests that Bhc could be a novel biomarker for evaluating cognitive function in patients with cognitive impairment.
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Affiliation(s)
- Zipeng Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P. R. China
| | - Zhongjie Qu
- School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P. R. China
| | - Bowen Yin
- Department of Neurology, The First Hospital of Qinhuangdao, Qinhuangdao, Hebei 066000, P. R. China
| | - Liyong Yin
- Department of Neurology, The First Hospital of Qinhuangdao, Qinhuangdao, Hebei 066000, P. R. China
| | - Xin Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P. R. China
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Zheng X, Wang B, Liu H, Wu W, Sun J, Fang W, Jiang R, Hu Y, Jin C, Wei X, Chen SSC. Diagnosis of Alzheimer's disease via resting-state EEG: integration of spectrum, complexity, and synchronization signal features. Front Aging Neurosci 2023; 15:1288295. [PMID: 38020761 PMCID: PMC10661409 DOI: 10.3389/fnagi.2023.1288295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Background Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence of more than 55 million people. Electroencephalogram (EEG) has become a suitable, accurate, and highly sensitive biomarker for the identification and diagnosis of AD. Methods In this study, a public database of EEG resting state-closed eye recordings containing 36 AD subjects and 29 normal subjects was used. And then, three types of signal features of resting-state EEG, i.e., spectrum, complexity, and synchronization, were performed by applying various signal processing and statistical methods, to obtain a total of 18 features for each signal epoch. Next, the supervised machine learning classification algorithms of decision trees, random forests, and support vector machine (SVM) were compared in categorizing processed EEG signal features of AD and normal cases with leave-one-person-out cross-validation. Results The results showed that compared to normal cases, the major change in EEG characteristics in AD cases was an EEG slowing, a reduced complexity, and a decrease in synchrony. The proposed methodology achieved a relatively high classification accuracy of 95.65, 95.86, and 88.54% between AD and normal cases for decision trees, random forests, and SVM, respectively, showing that the integration of spectrum, complexity, and synchronization features for EEG signals can enhance the performance of identifying AD and normal subjects. Conclusion This study recommended the integration of EEG features of spectrum, complexity, and synchronization for aiding the diagnosis of AD.
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Affiliation(s)
- Xiaowei Zheng
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
- School of Mathematics, Northwest University, Xian, China
- Medical Big Data Research Center, Northwest University, Xi'an, China
| | - Bozhi Wang
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Hao Liu
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Wencan Wu
- School of Mathematics, Northwest University, Xian, China
| | - Jiamin Sun
- School of Mathematics, Northwest University, Xian, China
| | - Wei Fang
- School of Mathematics, Northwest University, Xian, China
| | - Rundong Jiang
- School of Mathematics, Northwest University, Xian, China
| | - Yajie Hu
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Cheng Jin
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Xin Wei
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
- School of Humanities and Education, Xi'an Eurasia University, Xi'an, China
- Institute of Social Psychology, Xi'an Jiaotong University, Xi'an, China
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Scheijbeler EP, de Haan W, Stam CJ, Twisk JWR, Gouw AA. Longitudinal resting-state EEG in amyloid-positive patients along the Alzheimer's disease continuum: considerations for clinical trials. Alzheimers Res Ther 2023; 15:182. [PMID: 37858173 PMCID: PMC10585755 DOI: 10.1186/s13195-023-01327-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND To enable successful inclusion of electroencephalography (EEG) outcome measures in Alzheimer's disease (AD) clinical trials, we retrospectively mapped the progression of resting-state EEG measures over time in amyloid-positive patients with mild cognitive impairment (MCI) or dementia due to AD. METHODS Resting-state 21-channel EEG was recorded in 148 amyloid-positive AD patients (MCI, n = 88; dementia due to AD, n = 60). Two or more EEG recordings were available for all subjects. We computed whole-brain and regional relative power (i.e., theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-13 Hz), beta (13-30 Hz)), peak frequency, signal variability (i.e., theta permutation entropy), and functional connectivity values (i.e., alpha and beta corrected amplitude envelope correlation, theta phase lag index, weighted symbolic mutual information, inverted joint permutation entropy). Whole-group linear mixed effects models were used to model the development of EEG measures over time. Group-wise analysis was performed to investigate potential differences in change trajectories between the MCI and dementia subgroups. Finally, we estimated the minimum sample size required to detect different treatment effects (i.e., 50% less deterioration, stabilization, or 50% improvement) on the development of EEG measures over time, in hypothetical clinical trials of 1- or 2-year duration. RESULTS Whole-group analysis revealed significant regional and global oscillatory slowing over time (i.e., increased relative theta power, decreased beta power), with strongest effects for temporal and parieto-occipital regions. Disease severity at baseline influenced the EEG measures' rates of change, with fastest deterioration reported in MCI patients. Only AD dementia patients displayed a significant decrease of the parieto-occipital peak frequency and theta signal variability over time. We estimate that 2-year trials, focusing on amyloid-positive MCI patients, require 36 subjects per arm (2 arms, 1:1 randomization, 80% power) to detect a stabilizing treatment effect on temporal relative theta power. CONCLUSIONS Resting-state EEG measures could facilitate early detection of treatment effects on neuronal function in AD patients. Their sensitivity depends on the region-of-interest and disease severity of the study population. Conventional spectral measures, particularly recorded from temporal regions, present sensitive AD treatment monitoring markers.
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Affiliation(s)
- Elliz P Scheijbeler
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands.
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands.
| | - Willem de Haan
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Jos W R Twisk
- Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
| | - Alida A Gouw
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
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Averna A, Coelli S, Ferrara R, Cerutti S, Priori A, Bianchi AM. Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease. J Neural Eng 2023; 20:051001. [PMID: 37746822 DOI: 10.1088/1741-2552/acf8fa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 09/12/2023] [Indexed: 09/26/2023]
Abstract
Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases.
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Affiliation(s)
- Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Rosanna Ferrara
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Sergio Cerutti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Alberto Priori
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Anna Maria Bianchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Liu J, Lu H, Zhang X, Li X, Wang L, Yin S, Cui D. Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? Entropy (Basel) 2023; 25:396. [PMID: 36981285 PMCID: PMC10047945 DOI: 10.3390/e25030396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/10/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.
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Affiliation(s)
- Jing Liu
- Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Huibin Lu
- Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Xiuru Zhang
- Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Xiaoli Li
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Lei Wang
- Neurology Department, Chinese People’s Liberation Army Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Shimin Yin
- Neurology Department, Chinese People’s Liberation Army Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Dong Cui
- Hebei Key Laboratory of Information Transmission and Signal Processing, School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
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Liu J, Lu H, Guo Y, Gu G, Li X, Cui D. A new EEG determinism analysis method based on multiscale dispersion recurrence plot. Biomed Signal Process Control 2023; 80:104301. [DOI: 10.1016/j.bspc.2022.104301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Fide E, Polat H, Yener G, Özerdem MS. Effects of Pharmacological Treatments in Alzheimer's Disease: Permutation Entropy-Based EEG Complexity Study. Brain Topogr 2023; 36:106-118. [PMID: 36399219 DOI: 10.1007/s10548-022-00927-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 11/03/2022] [Indexed: 11/19/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative brain disease affecting cognitive and physical functioning. The currently available pharmacological treatments for AD mainly contain cholinesterase inhibitors (AChE-I) and N-methyl-D-aspartic acid (NMDA) receptor antagonists (i.e., memantine). Because brain signals have complex nonlinear dynamics, there has been an increase in interest in researching complexity changes in the time series of brain signals in individuals with AD. In this study, we explore the electroencephalographic (EEG) complexity for making better observation of pharmacological therapy-based treatment effects on AD patients using the permutation entropy (PE) method. We examined EEG sub-band (delta, theta, alpha, beta, and gamma) complexity in de-novo, monotherapy (AChE-I), dual therapy (AChE-I and memantine) receiving AD participants compared with healthy elderly controls. We showed that each frequency band depicts its own complexity profile, which is regionally altered between groups. These alterations were also found to be associated with global cognitive scores. Overall, our findings indicate that entropy measures could be useful to show medication effects in AD.
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Affiliation(s)
- Ezgi Fide
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Hasan Polat
- Department of Electrical and Energy, Bingöl University, Selahaddin-i Eyyübi Mah. Aydınlık Cad No: 1, 12000, Bingöl, Turkey.
| | - Görsev Yener
- Brain Dynamics Multidisciplinary Research Center, Izmir, Turkey.,Faculty of Medicine, Izmir University of Economics, Izmir, Turkey.,International Biomedicine and Genome Institute, Izmir, Turkey
| | - Mehmet Siraç Özerdem
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakır, Turkey
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A Novel In-Home Sleep Monitoring System Based on Fully Integrated Multichannel Front-End Chip and Its Multilevel Analyses. IEEE J Transl Eng Health Med 2023; 11:211-222. [PMID: 36950263 PMCID: PMC10027079 DOI: 10.1109/jtehm.2023.3248621] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/17/2023] [Accepted: 02/15/2023] [Indexed: 03/06/2023]
Abstract
OBJECTIVE A novel in-home sleep monitoring system with an 8-channel biopotential acquisition front-end chip is presented and validated via multilevel data analyses and comparision with advanced polysomnography. METHODS AND PROCEDURES The chip includes a cascaded low-noise programmable gain amplifier (PGA) and 24-bit [Formula: see text]-[Formula: see text] analog-to-digital converter (ADC). The PGA is based on three op-amp structure while the ADC adopts cascade of integrator feedforward and feedback (CIFF-B) architecture. An innovative chopper-modulated input-scaling-down technique enhances the dynamic range. The proposed system and commercial polysomnography were used for in-home sleep monitoring of 20 healthy participants. The consistency and significance of the two groups' data were analyzed. RESULTS Fabricated in 180 nm BCD technology, the input-referred noise, input impedance, common-mode rejection ratio, and dynamic range of the acquisition front-end chip were [Formula: see text]Vpp, 1.25 GN), 113.9 dB, and 119.8 dB. The kappa coefficients between the sleep stage labels of the three scorers were 0.80, 0.76, and 0.79. The consistency of the slowing index, multiscale entropy, and percentile features between the two devices reached 0.958, 0.885, and 0.834. The macro sleep architecture characteristics of the two devices were not significantly different (all p [Formula: see text] 0.05). CONCLUSION The proposed chip was applied to develop an in-home sleep monitoring system with significantly reduced size, power, and cost. Multilevel analyses demonstrated that this system collects stable and accurate in-home sleep data. CLINICAL IMPACT The proposed system can be applied for long-term in-home sleep monitoring outside of laboratory environments and sleep disorders screening that with low cost.
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Peña M, Petrillo K, Bosset M, Fain M, Chou YH, Rapcsak S, Toosizadeh N. Brain function complexity during dual-tasking is associated with cognitive impairment and age. J Neuroimaging 2022; 32:1211-1223. [PMID: 35843726 PMCID: PMC9649845 DOI: 10.1111/jon.13025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/29/2022] [Accepted: 07/03/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE Early diagnosis of cognitive impairment is important because symptoms can be delayed through therapies. Synaptic disconnections are the key characteristics of dementia, and through nonlinear complexity analysis of brain function, it is possible to identify long-range synaptic disconnections in the brain. METHODS We investigated the capability of a novel upper-extremity function (UEF) dual-task paradigm in the functional MRI (fMRI) setting, where the participant flexes and extends their arm while counting, to differentiate between cognitively normal (CN) and those with mild cognitive impairment (MCI). We used multiscale entropy (MSE) complexity analysis of the blood oxygen-level dependent time-series across neural networks and brain regions. Outside of the fMRI, we used the UEF dual-task test, while the elbow kinematics were measured using motion sensors, to record the motor function score. RESULTS Results showed 34% lower MSE values in MCI compared to CN (p<.04 for all regions and networks except cerebellum when counting down by one; effect size = 1.35±0.15) and a negative correlation between MSE values and age (average r2 of 0.30 for counting down by one and 0.36 for counting backward by three). Results also showed an improvement in the logistic regression model sensitivity by 14-24% in predicting the presence of MCI when brain function measure was added to the motor function score (kinematics data). CONCLUSIONS Current findings suggest that combining measures of neural network and motor function, in addition to neuropsychological testing, may provide an accurate tool for assessing early-stage cognitive impairment and age-related decline in cognition.
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Affiliation(s)
- Miguel Peña
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ
| | - Kelsi Petrillo
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ
| | - Mark Bosset
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ
| | - Mindy Fain
- Arizona Center on Aging, Department of Medicine, College of Medicine, University of Arizona, Tucson, AZ
- Division of Geriatrics, General Internal Medicine and Palliative Medicine, Department of Medicine, University of Arizona, Tucson, AZ
| | - Ying-hui Chou
- Department of Psychology, University of Arizona, Tucson, AZ
- Arizona Center on Aging, Department of Medicine, College of Medicine, University of Arizona, Tucson, AZ
| | - Steve Rapcsak
- Department of Neurology, University of Arizona, Tucson, AZ
- Banner Alzheimer’s Institute, Tucson, AZ
| | - Nima Toosizadeh
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ
- Arizona Center on Aging, Department of Medicine, College of Medicine, University of Arizona, Tucson, AZ
- Division of Geriatrics, General Internal Medicine and Palliative Medicine, Department of Medicine, University of Arizona, Tucson, AZ
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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: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Nagy B, Protzner AB, van der Wijk G, Wang H, Cortese F, Czigler I, Gaál ZA. The modulatory effect of adaptive task-switching training on resting-state neural network dynamics in younger and older adults. Sci Rep 2022; 12:9541. [PMID: 35680953 DOI: 10.1038/s41598-022-13708-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/26/2022] [Indexed: 11/08/2022] Open
Abstract
With increasing life expectancy and active aging, it becomes crucial to investigate methods which could compensate for generally detected cognitive aging processes. A promising candidate is adaptive cognitive training, during which task difficulty is adjusted to the participants' performance level to enhance the training and potential transfer effects. Measuring intrinsic brain activity is suitable for detecting possible distributed training-effects since resting-state dynamics are linked to the brain's functional flexibility and the effectiveness of different cognitive processes. Therefore, we investigated if adaptive task-switching training could modulate resting-state neural dynamics in younger (18-25 years) and older (60-75 years) adults (79 people altogether). We examined spectral power density on resting-state EEG data for measuring oscillatory activity, and multiscale entropy for detecting intrinsic neural complexity. Decreased coarse timescale entropy and lower frequency band power as well as increased fine timescale entropy and higher frequency band power revealed a shift from more global to local information processing with aging before training. However, cognitive training modulated these age-group differences, as coarse timescale entropy and lower frequency band power increased from pre- to post-training in the old-training group. Overall, our results suggest that cognitive training can modulate neural dynamics even when measured outside of the trained task.
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15
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Chang KH, French IT, Liang WK, Lo YS, Wang YR, Cheng ML, Huang NE, Wu HC, Lim SN, Chen CM, Juan CH. Evaluating the Different Stages of Parkinson's Disease Using Electroencephalography With Holo-Hilbert Spectral Analysis. Front Aging Neurosci 2022; 14:832637. [PMID: 35619940 PMCID: PMC9127298 DOI: 10.3389/fnagi.2022.832637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/08/2022] [Indexed: 01/04/2023] Open
Abstract
Electroencephalography (EEG) can reveal the abnormalities of dopaminergic subcortico-cortical circuits in patients with Parkinson's disease (PD). However, conventional time-frequency analysis of EEG signals cannot fully reveal the non-linear processes of neural activities and interactions. A novel Holo-Hilbert Spectral Analysis (HHSA) was applied to reveal non-linear features of resting state EEG in 99 PD patients and 59 healthy controls (HCs). PD patients demonstrated a reduction of β bands in frontal and central regions, and reduction of γ bands in central, parietal, and temporal regions. Compared with early-stage PD patients, late-stage PD patients demonstrated reduction of β bands in the posterior central region, and increased θ and δ2 bands in the left parietal region. θ and β bands in all brain regions were positively correlated with Hamilton depression rating scale scores. Machine learning algorithms using three prioritized HHSA features demonstrated "Bag" with the best accuracy of 0.90, followed by "LogitBoost" with an accuracy of 0.89. Our findings strengthen the application of HHSA to reveal high-dimensional frequency features in EEG signals of PD patients. The EEG characteristics extracted by HHSA are important markers for the identification of depression severity and diagnosis of PD.
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Affiliation(s)
- Kuo-Hsuan Chang
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Isobel Timothea French
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Central University and Academia Sinica, Taipei, Taiwan
| | - Wei-Kuang Liang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Research Center, National Central University, Taoyuan, Taiwan
| | - Yen-Shi Lo
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Yi-Ru Wang
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Mei-Ling Cheng
- Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan
- Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan
- Clinical Phenome Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Norden E. Huang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Research Center, National Central University, Taoyuan, Taiwan
- Data Analysis and Application Laboratory, The First Institute of Oceanography, Qingdao, China
| | - Hsiu-Chuan Wu
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Siew-Na Lim
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Chiung-Mei Chen
- Department of Neurology, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Chi-Hung Juan
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Research Center, National Central University, Taoyuan, Taiwan
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16
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17
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Gu H, Chou CA. Optimizing non-uniform multivariate embedding for multiscale entropy analysis of complex systems. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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18
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Walter J. Consciousness as a multidimensional phenomenon: implications for the assessment of disorders of consciousness. Neurosci Conscious 2021; 2021:niab047. [PMID: 34992792 PMCID: PMC8716840 DOI: 10.1093/nc/niab047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 10/19/2021] [Accepted: 12/10/2021] [Indexed: 01/10/2023] Open
Abstract
Disorders of consciousness (DoCs) pose a significant clinical and ethical challenge because they allow for complex forms of conscious experience in patients where intentional behaviour and communication are highly limited or non-existent. There is a pressing need for brain-based assessments that can precisely and accurately characterize the conscious state of individual DoC patients. There has been an ongoing research effort to develop neural measures of consciousness. However, these measures are challenging to validate not only due to our lack of ground truth about consciousness in many DoC patients but also because there is an open ontological question about consciousness. There is a growing, well-supported view that consciousness is a multidimensional phenomenon that cannot be fully described in terms of the theoretical construct of hierarchical, easily ordered conscious levels. The multidimensional view of consciousness challenges the utility of levels-based neural measures in the context of DoC assessment. To examine how these measures may map onto consciousness as a multidimensional phenomenon, this article will investigate a range of studies where they have been applied in states other than DoC and where more is known about conscious experience. This comparative evidence suggests that measures of conscious level are more sensitive to some dimensions of consciousness than others and cannot be assumed to provide a straightforward hierarchical characterization of conscious states. Elevated levels of brain complexity, for example, are associated with conscious states characterized by a high degree of sensory richness and minimal attentional constraints, but are suboptimal for goal-directed behaviour and external responsiveness. Overall, this comparative analysis indicates that there are currently limitations to the use of these measures as tools to evaluate consciousness as a multidimensional phenomenon and that the relationship between these neural signatures and phenomenology requires closer scrutiny.
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Affiliation(s)
- Jasmine Walter
- Cognition and Philosophy Lab, 21 Chancellor’s Walk, Monash University, Melbourne, VIC 3800, Australia
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19
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Acker L, Ha C, Zhou J, Manor B, Giattino CM, Roberts K, Berger M, Wright MC, Colon-Emeric C, Devinney M, Au S, Woldorff MG, Lipsitz LA, Whitson HE. Electroencephalogram-Based Complexity Measures as Predictors of Post-operative Neurocognitive Dysfunction. Front Syst Neurosci 2021; 15:718769. [PMID: 34858144 PMCID: PMC8631543 DOI: 10.3389/fnsys.2021.718769] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 10/12/2021] [Indexed: 11/13/2022] Open
Abstract
Physiologic signals such as the electroencephalogram (EEG) demonstrate irregular behaviors due to the interaction of multiple control processes operating over different time scales. The complexity of this behavior can be quantified using multi-scale entropy (MSE). High physiologic complexity denotes health, and a loss of complexity can predict adverse outcomes. Since postoperative delirium is particularly hard to predict, we investigated whether the complexity of preoperative and intraoperative frontal EEG signals could predict postoperative delirium and its endophenotype, inattention. To calculate MSE, the sample entropy of EEG recordings was computed at different time scales, then plotted against scale; complexity is the total area under the curve. MSE of frontal EEG recordings was computed in 50 patients ≥ age 60 before and during surgery. Average MSE was higher intra-operatively than pre-operatively (p = 0.0003). However, intraoperative EEG MSE was lower than preoperative MSE at smaller scales, but higher at larger scales (interaction p < 0.001), creating a crossover point where, by definition, preoperative, and intraoperative MSE curves met. Overall, EEG complexity was not associated with delirium or attention. In 42/50 patients with single crossover points, the scale at which the intraoperative and preoperative entropy curves crossed showed an inverse relationship with delirium-severity score change (Spearman ρ = -0.31, p = 0.054). Thus, average EEG complexity increases intra-operatively in older adults, but is scale dependent. The scale at which preoperative and intraoperative complexity is equal (i.e., the crossover point) may predict delirium. Future studies should assess whether the crossover point represents changes in neural control mechanisms that predispose patients to postoperative delirium.
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Affiliation(s)
- Leah Acker
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC, United States
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, United States
| | - Christine Ha
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, United States
| | - Junhong Zhou
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life and Harvard Medical School, Boston, MA, United States
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Brad Manor
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life and Harvard Medical School, Boston, MA, United States
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Charles M Giattino
- Center for Cognitive Neuroscience, Duke University, Durham, NC, United States
| | - Ken Roberts
- Center for Cognitive Neuroscience, Duke University, Durham, NC, United States
| | - Miles Berger
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC, United States
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, United States
- Center for Cognitive Neuroscience, Duke University, Durham, NC, United States
| | - Mary Cooter Wright
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC, United States
| | - Cathleen Colon-Emeric
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, United States
- Division of Geriatric Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
| | - Michael Devinney
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC, United States
| | - Sandra Au
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, United States
| | - Marty G Woldorff
- Center for Cognitive Neuroscience, Duke University, Durham, NC, United States
- Department of Psychiatry, Duke University, Durham, NC, United States
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States
| | - Lewis A Lipsitz
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life and Harvard Medical School, Boston, MA, United States
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Heather E Whitson
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, United States
- Division of Geriatric Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, United States
- Geriatrics Research Education and Clinical Center, Durham VA Medical Center, Durham, NC, United States
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20
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Guo Y, Chen Y, Yang Q, Hou F, Liu X, Ma Y. Multi-Scale Permutation Entropy: A Potential Measure for the Impact of Sleep Medication on Brain Dynamics of Patients with Insomnia. Entropy (Basel) 2021; 23:1101. [PMID: 34573726 PMCID: PMC8472826 DOI: 10.3390/e23091101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 08/21/2021] [Accepted: 08/22/2021] [Indexed: 11/23/2022]
Abstract
Insomnia is a common sleep disorder that is closely associated with the occurrence and deterioration of cardiovascular disease, depression and other diseases. The evaluation of pharmacological treatments for insomnia brings significant clinical implications. In this study, a total of 20 patients with mild insomnia and 75 healthy subjects as controls (HC) were included to explore alterations of electroencephalogram (EEG) complexity associated with insomnia and its pharmacological treatment by using multi-scale permutation entropy (MPE). All participants were recorded for two nights of polysomnography (PSG). The patients with mild insomnia received a placebo on the first night (Placebo) and temazepam on the second night (Temazepam), while the HCs had no sleep-related medication intake for either night. EEG recordings from each night were extracted and analyzed using MPE. The results showed that MPE decreased significantly from pre-lights-off to the period during sleep transition and then to the period after sleep onset, and also during the deepening of sleep stage in the HC group. Furthermore, results from the insomnia subjects showed that MPE values were significantly lower for the Temazepam night compared to MPE values for the Placebo night. Moreover, MPE values for the Temazepam night showed no correlation with age or gender. Our results indicated that EEG complexity, measured by MPE, may be utilized as an alternative approach to measure the impact of sleep medication on brain dynamics.
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Affiliation(s)
- Yanping Guo
- School of Electrical & Mechanical Engineering and Automation, Nanhang Jincheng College, Nanjing 211156, China;
| | - Yingying Chen
- School of Science, China Pharmaceutical University, Nanjing 210009, China; (Y.C.); (Q.Y.); (F.H.)
- School of Software, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Qianru Yang
- School of Science, China Pharmaceutical University, Nanjing 210009, China; (Y.C.); (Q.Y.); (F.H.)
| | - Fengzhen Hou
- School of Science, China Pharmaceutical University, Nanjing 210009, China; (Y.C.); (Q.Y.); (F.H.)
| | - Xinyu Liu
- School of Science, China Pharmaceutical University, Nanjing 210009, China; (Y.C.); (Q.Y.); (F.H.)
| | - Yan Ma
- Osher Center for Integrative Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02215, USA;
- Beth Israel Deaconess Medical Center, Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
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21
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Lyu J, Wei Y, Li H, Dong J, Zhang X. The effect of three-circle post standing (Zhanzhuang) qigong on the physical and psychological well-being of college students: A randomized controlled trial. Medicine (Baltimore) 2021; 100:e26368. [PMID: 34128894 PMCID: PMC8213330 DOI: 10.1097/md.0000000000026368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 06/01/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Qigong has a long-term application by integration of mind, breath and body to prevent and cure diseases. Researches show that qigong practice could adjust anxiety, the mechanism may found on brain and heart functions. Currently there are limitations on qigong's anxiety-release mechanism study between mind and body, and existing studies lack of evidence on electrophysiology research. Our objective to analyse qigong's anxiety-release effect and mechanism. METHODS A two-arm randomized clinical trial with 144 qigong naïve anxiety subjects without cerebral or cardiovascular diseases or other severe syndromes will be allocated to either a body and breath regulation group (n = 72) or a body regulation group (n = 72). Participants will conduct three-circle post standing qigong exercise 5 times per week for 8 weeks, while the three-circle post standing qigong combined with abdominal breath regulation (TCPSQ-BR) group will combined with abdominal breath regulation. The primary outcome will be the Self-Rating Anxiety Scale (SAS), and the secondary outcome will be complexity-based measures of heart rate and electroencephalogram (EEG) signals assessed at baseline and 8 weeks. Multiscale entropy analysis will be used as measure of complexity. CONCLUSION This study will be investigate the effects of qigong's anxiety-release by SAS, and will analyze the coordinates of EEG and heart rate variability (HRV) signals before and after three-circle post standing qigong (TCPSQ) practice, and to analyse their synergies by complex signal process method. ETHICS AND TRAIL REGISTRATION The protocol was approved by the institutional review boards of Beijing University of Chinese Medicine (2018BZHYLL0109). This study was registered with the "Chinese Clinical Trail Registry" in the WHO Registry Network (ChiCTR-Bon-17010840).
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Liu F, Fuh JL, Peng CK, Yang AC. Phenotyping Neuropsychiatric Symptoms Profiles of Alzheimer's Disease Using Cluster Analysis on EEG Power. Front Aging Neurosci 2021; 13:623930. [PMID: 33927606 PMCID: PMC8076521 DOI: 10.3389/fnagi.2021.623930] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 03/24/2021] [Indexed: 12/14/2022] Open
Abstract
Background: There has been an increasing interest in studying electroencephalogram (EEG) as a biomarker of Alzheimer’s disease but the association between EEG signals and patients’ neuropsychiatric symptoms remains unclear. We studied EEG signals of patients with Alzheimer’s disease to explore the associations between patients’ neuropsychiatric symptoms and clusters of patients based on their EEG powers. Methods: A total of 69 patients with mild Alzheimer’s disease (the Clinical Dementia Rating = 1) were enrolled and their EEG signals from 19 channels/electrodes were recorded in three sessions for each patient. The EEG power was calculated by Fourier transform for the four frequency bands (beta: 13–40 Hz, alpha: 8–13 Hz, theta: 4–8 Hz, and delta: <4 Hz). We performed K-means cluster analysis to classify the 69 patients into two distinct groups by the log-transformed EEG powers (4 frequency bands × 19 channels) for the three EEG sessions. In each session, both clusters were compared with each other to assess the differences in their behavioral/psychological symptoms in terms of the Neuropsychiatric Inventory (NPI) score. Results: While EEG band powers were highly consistent across all three sessions before clustering, EEG band powers were different between the two clusters in each session, especially for the delta waves. The delta band powers differed significantly between the two clusters in most channels across the three sessions. Patients’ demographics and cognitive function were not different between both clusters. However, their behavioral/psychological symptoms were different between the two clusters classified based on EEG powers. A higher NPI score was associated with the clustering of higher EEG powers. Conclusion: The present study suggests that EEG power correlates to behavioral and psychological symptoms among patients with mild Alzheimer’s disease. The clustering approach of EEG signals may provide a novel and cost-effective method to differentiate the severity of neuropsychiatric symptoms and/or predict the prognosis for Alzheimer’s patients.
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Affiliation(s)
- Friedrich Liu
- Boston University Academy, Boston, MA, United States.,Center for Dynamical Biomarkers, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
| | - Jong-Ling Fuh
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.,Department of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chung-Kang Peng
- Center for Dynamical Biomarkers, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
| | - Albert C Yang
- Institute of Brain Science/Digital Medicine Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Taoyuan Psychiatric Center, Taoyuan City, Taiwan
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23
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Yu WY, Low I, Chen C, Fuh JL, Chen LF. Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer's Disease and Mild Cognitive Impairment. Entropy (Basel) 2021; 23:427. [PMID: 33916588 PMCID: PMC8066899 DOI: 10.3390/e23040427] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 12/22/2022]
Abstract
Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer's disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a "transitional change" between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.
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Grants
- AS-BD-108-2 Academia Sinica, Taiwan
- MOST 109-2314-B-010-027, 107-2221-E-010-013, 109-2811-E-010-503, 108-2321-B-075-001, 109-2314-B-075-052-MY2 Ministry of Science and Technology, Taiwan
- VGHUST 110-G1-5-1, 110-G1-5-2, 109-V1-5-1, 109-V1-5-2 Veterans General Hospitals-University System of Taiwan Joint Research Program
- V110C-057 Taipei Veterans General Hospital
- Brain Research Center, National Yang Ming Chiao Tung University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project Taiwan Ministry of Education
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Affiliation(s)
- Wei-Yang Yu
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
| | - Intan Low
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
- Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Chien Chen
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Jong-Ling Fuh
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Li-Fen Chen
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
- Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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24
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Al-Jawahiri R, Jones M, Milne E. Spontaneous neural activity relates to psychiatric traits in 16p11.2 CNV carriers: An analysis of EEG spectral power and multiscale entropy. J Psychiatr Res 2021; 136:610-618. [PMID: 33158556 DOI: 10.1016/j.jpsychires.2020.10.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 10/22/2020] [Accepted: 10/26/2020] [Indexed: 11/28/2022]
Abstract
Copy number variations (CNV) at the 16p11.2 chromosomal region are rare high-risk CNVs associated with various clinical features and psychiatric disorders including intellectual disability, developmental delays, and autism spectrum disorder. No study to date has investigated whether spontaneous neural activity is altered for 16p11.2 CNV carriers and whether this relates to psychiatric traits. The aim of this study is to examine the impact of 16p11.2 deletions (del) and duplications (dup) on spontaneous neural activity and its relationship to psychiatric problems. EEG was previously collected as part of the Simons Searchlight initiative. Using spectral power (delta, theta, alpha, and beta frequency bands), complexity index (CI), and multiscale entropy analysis techniques, we analyzed frontal resting-state EEG data collected from 22 16p11.2 del carriers, 14 dup carriers, and 13 controls. We then examined associations between neural activity and psychiatric traits, measured with the Child Behavior Checklist. Results indicated that EEG entropy was higher for del and dup compared to controls, respectively, at all timescales. CI was also higher for del and dup compared to controls. Theta power of 16p11.2 dup carriers was higher than controls. A strong association was found between entropy at higher timescales and anxiety problems. In addition, a strong correlation was found between theta power and pervasive developmental problems. Atypical spontaneous neural activity is implicated in 16p11.2 CNVs. With higher entropy or theta power, psychiatric traits increase in severity. Our findings provide evidence of the link between genotype, neural activity, and phenotypes in 16p11.2 CNVs.
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Affiliation(s)
- Reem Al-Jawahiri
- Department of Psychology, University of Sheffield, United Kingdom.
| | - Myles Jones
- Department of Psychology, University of Sheffield, United Kingdom
| | - Elizabeth Milne
- Department of Psychology, University of Sheffield, United Kingdom
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25
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Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsalikakis D, Tsipouras MG. Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. Int J Neural Syst 2021; 31:2130002. [PMID: 33588710 DOI: 10.1142/s0129065721300023] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GR50100, Greece.,Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Vasileios Christou
- Q Base R&D, Science & Technology Park of Epirus, University of Ioannina Campus, Ioannina GR45110, Greece.,Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Pantelis Angelidis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Dimitrios Tsalikakis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Markos G Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
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26
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Tsai HJ, Yang WC, Tsai SJ, Lin CH, Yang AC. Remission of depression is associated with asymmetric hemispheric variation in EEG complexity before antidepressant treatment. J Affect Disord 2021; 281:872-879. [PMID: 33220949 DOI: 10.1016/j.jad.2020.11.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 09/14/2020] [Accepted: 11/07/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Intense increased alpha activity in the anterior brain has been recognized as one of the biomarkers of depression. This study aimed to explore whether complexity measures of electroencephalogram (EEG) signals vary across the frontal region between patients with or without remission in depression. METHODS Ninety-four patients with moderate-to-severe major depression underwent the experiment. Hilbert-Huang transform was applied to extract individual alpha EEG instantaneous amplitude. Averaged regional complexity (SampEnMean) on frontal and frontal-central areas as well as bilateral frontal areas were assessed according to multiscale entropy between the remitters (n = 26/94, 27.66%) and poor responders (n = 29/94, 30.85%). Standard deviation of regional complexity (SampEnSD) was used as a parameter to test the homogeneity of frontal alpha complexity. RESULTS No significant differences in SampEnMean for the frontal or frontal-central area were observed between the remitters and poor responders. However, the SampEnSD (i.e., regional homogeneity of frontal alpha complexity) significantly increased in the frontal-central area after treatment among the poor responders in comparison to their baseline, whereas the remitters showed an opposite trend. The remitters further showed significantly larger SampEnSD on the left frontal area at baseline, and this lateralization disappeared after antidepressant treatment except for the poor responders. LIMITATIONS This study was limited by a small sample size and without healthy controls. CONCLUSIONS Homogeneity in frontal-central alpha complexity and interhemispheric asymmetry normalization of anterior EEG complexity are associated with antidepressant efficacy, suggesting that homogeneity of dynamical brain activity is a key linked to the successful treatment of major depression.
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Affiliation(s)
- Hsin-Jung Tsai
- Department of Psychiatry, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Wei-Cheng Yang
- Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veteran General Hospital, Taipei, Taiwan; Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Ching-Hua Lin
- Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan.
| | - Albert C Yang
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan; Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan; Brain Medicine Center, Taoyuan Psychiatric Center, Taoyuan City, Taiwan.
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27
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Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease, for which aging remains the major risk factor. Aging is under a consistent pressure of increasing brain entropy (BEN) due to the progressive brain deteriorations. Noticeably, the brain constantly consumes a large amount of energy to maintain its functional integrity, likely creating or maintaining a big "reserve" to counteract the high entropy. Malfunctions of this latent reserve may indicate a critical point of disease progression. The purpose of this study was to characterize BEN in aging and AD and to test an inverse-U-shape BEN model: BEN increases with age and AD pathology in normal aging but decreases in the AD continuum. BEN was measured with resting state fMRI and compared across aging and the AD continuum. Associations of BEN with age, education, clinical symptoms, and pathology were examined by multiple regression. The analysis results highlighted resting BEN in the default mode network, medial temporal lobe, and prefrontal cortex and showed that: (1) BEN increased with age and pathological deposition in normal aging but decreased with age and pathological deposition in the AD continuum; (2) AD showed catastrophic BEN reduction, which was related to more severe cognitive impairment and daily function disability; and (3) BEN decreased with education years in normal aging, but not in the AD continuum. BEN evolution follows an inverse-U trajectory when AD progresses from normal aging to AD dementia. Education is beneficial for suppressing the entropy increase potency in normal aging.
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Affiliation(s)
- Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, United States
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28
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Abstract
Visual short-term memory (VSTM) is an important cognitive function that acts as a temporary storage for visual information. Previous studies have shown that VSTM capacity can be modulated by the location of one’s hands, where hand proximity enhances neural processing and memory of nearby visual stimuli. The present study used traditional event-related potentials (ERP) along with multiscale entropy (MSE) analysis to shed light on the neural mechanism(s) behind such near-hand effect. Participants’ electroencephalogram (EEG) data were recorded as they performed a VSTM task with their hands either proximal or distal to the display. ERP analysis showed altered memory processing in the 400–700 ms time window during memory retrieval period. Importantly, MSE analysis also showed significant EEG difference between hand proximal and distal conditions between scales 10 to 20, and such difference is clustered around the right parietal cortex – a region that is involved in VSTM processing and bimodal hand-eye integration. The implications of higher MSE time scale in the parietal cortex are discussed in the context of signal complexity and its possible relation to cognitive processing. To our knowledge, this study provides the first investigation using MSE to characterize the temporal characteristics and signal complexity behind the effect of hand proximity.
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Affiliation(s)
- Philip Tseng
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, TMU-Shuang Ho Hospital, New Taipei City, Taiwan.,Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yu-Hui Lo
- Brain and Consciousness Research Center, TMU-Shuang Ho Hospital, New Taipei City, Taiwan
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29
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Keshmiri S. Comparative Analysis of the Permutation and Multiscale Entropies for Quantification of the Brain Signal Variability in Naturalistic Scenarios. Brain Sci 2020; 10:E527. [PMID: 32781789 PMCID: PMC7463830 DOI: 10.3390/brainsci10080527] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 11/16/2022] Open
Abstract
As alternative entropy estimators, multiscale entropy (MSE) and permutation entropy (PE) are utilized for quantification of the brain function and its signal variability. In this context, their applications are primarily focused on two specific domains: (1) the effect of brain pathology on its function (2) the study of altered states of consciousness. As a result, there is a paucity of research on applicability of these measures in more naturalistic scenarios. In addition, the utility of these measures for quantification of the brain function and with respect to its signal entropy is not well studied. These shortcomings limit the interpretability of the measures when used for quantification of the brain signal entropy. The present study addresses these limitations by comparing MSE and PE with entropy of human subjects' EEG recordings, who watched short movie clips with negative, neutral, and positive content. The contribution of the present study is threefold. First, it identifies a significant anti-correlation between MSE and entropy. In this regard, it also verifies that such an anti-correlation is stronger in the case of negative rather than positive or neutral affects. Second, it finds that MSE significantly differentiates between these three affective states. Third, it observes that the use of PE does not warrant such significant differences. These results highlight the level of association between brain's entropy in response to affective stimuli on the one hand and its quantification in terms of MSE and PE on the other hand. This, in turn, allows for more informed conclusions on the utility of MSE and PE for the study and analysis of the brain signal variability in naturalistic scenarios.
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Affiliation(s)
- Soheil Keshmiri
- The Thomas N. Sato BioMEC-X Laboratories, Advanced Telecommunications Research Institute International (ATR), 2-2 Hikaridai Seika-cho, Kyoto 619-02, Japan
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30
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Shim YS, Shin HE. Analysis of Neuropsychiatric Symptoms in Patients with Alzheimer's Disease Using Quantitative EEG and sLORETA. NEURODEGENER DIS 2020; 20:12-19. [PMID: 32610338 DOI: 10.1159/000508130] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 04/23/2020] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE The electrocortical activities associated with the neuropsychiatric symptoms (NPSs) of Alzheimer's disease (AD) were investigated using frequency-domain electroencephalography (EEG) spectral source analysis, and the potential electrocortical indices identified. MATERIALS AND METHODS Scalp EEG data were obtained from 51 patients with AD to investigate the presence of four NPS subdomains, hyperactivity, psychosis, affective symptoms, and apathy. EEG power spectra and the standardized low-resolution brain electromagnetic tomography (sLORETA)-localized EEG cortical sources were compared between the groups with and without the four NPS subdomains in eight frequency bands: 1-4, 4-8, 8-10, 10-12, 12-18, 18-20, 20-30, and 30-45 Hz. RESULTS The power spectral analysis of EEG data showed that AD patients with psychosis had lower values at the α2-band in most areas. In patients with apathy, the θ-to-β power ratio showed a greater activity over the frontal and central regions. The cortical source analysis using sLORETA revealed that patients with psychosis showed decreased values in the α2-band and patients with apathy showed higher δ-values, especially in the right frontal and temporal regions. CONCLUSION The results of the present study showed that both classical EEG spectral and EEG source analysis could differentiate patients with and without NPSs, especially psychosis and apathy subdomains. Spectral and sLORETA analyses provided information helpful for a better characterization in patients with NPSs.
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Affiliation(s)
- Yong S Shim
- Department of Neurology, The Catholic University of Korea Eunpyeong St. Mary's Hospital, Seoul, Republic of Korea,
| | - Hae-Eun Shin
- Department of Neurology, The Catholic University of Korea Bucheon St. Mary's Hospital, Bucheon, Republic of Korea
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31
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Rossini P, Di Iorio R, Vecchio F, Anfossi M, Babiloni C, Bozzali M, Bruni A, Cappa S, Escudero J, Fraga F, Giannakopoulos P, Guntekin B, Logroscino G, Marra C, Miraglia F, Panza F, Tecchio F, Pascual-leone A, Dubois B. Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clin Neurophysiol 2020; 131:1287-310. [DOI: 10.1016/j.clinph.2020.03.003] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 02/06/2023]
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32
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Liu M, Liu X, Hildebrandt A, Zhou C. Individual Cortical Entropy Profile: Test-Retest Reliability, Predictive Power for Cognitive Ability, and Neuroanatomical Foundation. Cereb Cortex Commun 2020; 1:tgaa015. [PMID: 34296093 PMCID: PMC8153045 DOI: 10.1093/texcom/tgaa015] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 04/24/2020] [Accepted: 05/01/2020] [Indexed: 12/19/2022] Open
Abstract
The entropy profiles of cortical activity have become novel perspectives to investigate individual differences in behavior. However, previous studies have neglected foundational aspects of individual entropy profiles, that is, the test-retest reliability, the predictive power for cognitive ability in out-of-sample data, and the underlying neuroanatomical basis. We explored these issues in a large young healthy adult dataset (Human Connectome Project, N = 998). We showed the whole cortical entropy profile from resting-state functional magnetic resonance imaging is a robust personalized measure, while subsystem profiles exhibited heterogeneous reliabilities. The limbic network exhibited lowest reliability. We tested the out-of-sample predictive power for general and specific cognitive abilities based on reliable cortical entropy profiles. The default mode and visual networks are most crucial when predicting general cognitive ability. We investigated the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. Cortical thickness and structural connectivity explained spatial variations in the group-averaged entropy profile. Cortical folding and myelination in the attention and frontoparietal networks determined predominantly individual cortical entropy profile. This study lays foundations for brain-entropy-based studies on individual differences to understand cognitive ability and related pathologies. These findings broaden our understanding of the associations between neural structures, functional dynamics, and cognitive ability.
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Affiliation(s)
- Mianxin Liu
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Xinyang Liu
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Department of Physics, Zhejiang University, 310000 Hangzhou, China
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33
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Kosciessa JQ, Kloosterman NA, Garrett DD. Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it? PLoS Comput Biol 2020; 16:e1007885. [PMID: 32392250 PMCID: PMC7241858 DOI: 10.1371/journal.pcbi.1007885] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 05/21/2020] [Accepted: 04/18/2020] [Indexed: 01/10/2023] Open
Abstract
Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via "similarity bounds" biases coarse MSE scales-that are thought to reflect slow dynamics-by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales-presumed to indicate fast dynamics-is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.
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Affiliation(s)
- Julian Q. Kosciessa
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Niels A. Kloosterman
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Douglas D. Garrett
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
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34
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Hsu CF, Chao H, Yang AC, Yeh C, Hsu L, Chi S. Discrimination of Severity of Alzheimer’s Disease with Multiscale Entropy Analysis of EEG Dynamics. Applied Sciences 2020; 10:1244. [DOI: 10.3390/app10041244] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Multiscale entropy (MSE) was used to analyze electroencephalography (EEG) signals to differentiate patients with Alzheimer’s disease (AD) from healthy subjects. It was found that the MSE values of the EEG signals from the healthy subjects are higher than those of the AD ones at small time scale factors in the MSE algorithm, while lower than those of the AD patients at large time scale factors. Based on the finding, we applied the linear discriminant analysis (LDA) to optimize the differentiating performance by comparing the resulting weighted sum of the MSE values under some specific time scales of each subject. The EEG data from 15 healthy subjects, 69 patients with mild AD, and 15 patients with moderate to severe AD were recorded. As a result, the weighted sum values are significantly higher for the healthy than the patients with moderate to severe AD groups. The optimal testing accuracy under five specific scales is 100% based on the EEG signals acquired from the T4 electrode. The resulting weighted sum value for the mild AD group is in the middle of those for the healthy and the moderate to severe AD groups. Therefore, the MSE-based weighted sum value can potentially be an index of severity of Alzheimer’s disease.
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35
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Chang YW, Tsai SJ, Wu YF, Yang AC. Development of an Al-Based Web Diagnostic System for Phenotyping Psychiatric Disorders. Front Psychiatry 2020; 11:542394. [PMID: 33250789 PMCID: PMC7674487 DOI: 10.3389/fpsyt.2020.542394] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 09/14/2020] [Indexed: 12/14/2022] Open
Abstract
Background: Artificial intelligence (AI)-based medical diagnostic applications are on the rise. Our recent study has suggested an explainable deep neural network (EDNN) framework for identifying key structural deficits related to the pathology of schizophrenia. Here, we presented an AI-based web diagnostic system for schizophrenia under the EDNN framework with three-dimensional (3D) visualization of subjects' neuroimaging dataset. Methods: This AI-based web diagnostic system consisted of a web server and a neuroimaging diagnostic database. The web server deployed the EDNN algorithm under the Node.js environment. Feature selection and network model building were performed on the dataset obtained from two hundred schizophrenic patients and healthy controls in the Taiwan Aging and Mental Illness (TAMI) cohort. We included an independent cohort with 88 schizophrenic patients and 44 healthy controls recruited at Tri-Service General Hospital Beitou Branch for validation purposes. Results: Our AI-based web diagnostic system achieved 84.00% accuracy (89.47% sensitivity, 80.62% specificity) for gray matter (GM) and 90.22% accuracy (89.21% sensitivity, 91.23% specificity) for white matter (WM) on the TAMI cohort. For the Beitou cohort as an unseen test set, the model achieved 77.27 and 70.45% accuracy for GM and WM. Furthermore, it achieved 85.50 and 88.20% accuracy after model retraining to mitigate the effects of drift on the predictive capability. Moreover, our system visualized the identified voxels in brain atrophy in a 3D manner with patients' structural image, optimizing the evaluation process of the diagnostic results. Discussion: Together, our approach under the EDNN framework demonstrated the potential future direction of making a schizophrenia diagnosis based on structural brain imaging data. Our deep learning model is explainable, arguing for the accuracy of the key information related to the pathology of schizophrenia when using the AI-based web assessment platform. The rationale of this approach is in accordance with the Research Domain Criteria suggested by the National Institute of Mental Health.
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Affiliation(s)
- Yu-Wei Chang
- Institute of Brain Science and Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science and Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan.,Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yung-Fu Wu
- Department of Psychiatry, Beitou Branch, Tri-service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Albert C Yang
- Institute of Brain Science and Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan.,Brain Medicine Center, Tao-Yuan Psychiatric Center, Tao-Yuan, Taiwan
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36
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Furutani N, Nariya Y, Takahashi T, Ito H, Yoshimura Y, Hiraishi H, Hasegawa C, Ikeda T, Kikuchi M. Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity. Front Psychiatry 2020; 11:746. [PMID: 32848924 PMCID: PMC7406828 DOI: 10.3389/fpsyt.2020.00746] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 07/16/2020] [Indexed: 12/14/2022] Open
Abstract
Mental imagery behaviors of various modalities include visual, auditory, and motor behaviors. Their alterations are pathologically involved in various psychiatric disorders. Results of earlier studies suggest that imagery behaviors are correlated with the modulated activities of the respective modality-specific regions and the additional activities of supramodal imagery-related regions. Additionally, despite the availability of complexity analysis in the neuroimaging field, it has not been used for neural decoding approaches. Therefore, we sought to characterize neural oscillation related to multimodal imagery through complexity-based neural decoding. For this study, we modified existing complexity measures to characterize the time evolution of temporal complexity. We took magnetoencephalography (MEG) data of eight healthy subjects as they performed multimodal imagery and non-imagery tasks. The MEG data were decomposed into amplitude and phase of sub-band frequencies by Hilbert-Huang transform. Subsequently, we calculated the complexity values of each reconstructed time series, along with raw data and band power for comparison, and applied these results as inputs to decode visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI) functions. Consequently, intra-subject decoding with the complexity yielded a characteristic sensitivity map for each task with high decoding accuracy. The map is inverted in the occipital regions between VP and VI and in the central regions between ME and MI. Additionally, replacement of the labels into two classes as imagery and non-imagery also yielded better classification performance and characteristic sensitivity with the complexity. It is particularly interesting that some subjects showed characteristic sensitivities not only in modality-specific regions, but also in supramodal regions. These analyses indicate that two-class and four-class classifications each provided better performance when using complexity than when using raw data or band power as input. When inter-subject decoding was used with the same model, characteristic sensitivity maps were also obtained, although their decoding performance was lower. Results of this study underscore the availability of complexity measures in neural decoding approaches and suggest the possibility of a modality-independent imagery-related mechanism. The use of time evolution of temporal complexity in neural decoding might extend our knowledge of the neural bases of hierarchical functions in the human brain.
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Affiliation(s)
- Naoki Furutani
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yuta Nariya
- Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Haruka Ito
- General course, Sundai-Kofu High School, Kofu, Japan
| | - Yuko Yoshimura
- Institute of Human and Social Sciences, Kanazawa University, Kanazawa, Japan
| | - Hirotoshi Hiraishi
- Department of Biofunctional Imaging, Preeminent Medical Photonics Education & Research Center, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Chiaki Hasegawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
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Furutani N, Nariya Y, Takahashi T, Noto S, Yang AC, Hirosawa T, Kameya M, Minabe Y, Kikuchi M. Decomposed Temporal Complexity Analysis of Neural Oscillations and Machine Learning Applied to Alzheimer's Disease Diagnosis. Front Psychiatry 2020; 11:531801. [PMID: 33101073 PMCID: PMC7495507 DOI: 10.3389/fpsyt.2020.531801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 08/17/2020] [Indexed: 12/25/2022] Open
Abstract
Despite growing evidence of aberrant neuronal complexity in Alzheimer's disease (AD), it remains unclear how this variation arises. Neural oscillations reportedly comprise different functions depending on their own properties. Therefore, in this study, we investigated details of the complexity of neural oscillations by decomposing the oscillations into frequency, amplitude, and phase for AD patients. We applied resting-state magnetoencephalography (MEG) to 17 AD patients and 21 healthy control subjects. We first decomposed the source time series of the MEG signal into five intrinsic mode functions using ensemble empirical mode decomposition. We then analyzed the temporal complexities of these time series using multiscale entropy. Results demonstrated that AD patients had lower complexity on short time scales and higher complexity on long time scales in the alpha band in temporal regions of the brain. We evaluated the alpha band complexity further by decomposing it into amplitude and phase using Hilbert spectral analysis. Consequently, we found lower amplitude complexity and higher phase complexity in AD patients. Correlation analyses between spectral complexity and decomposed complexities revealed scale-dependency. Specifically, amplitude complexity was positively correlated with spectral complexity on short time scales, whereas phase complexity was positively correlated with spectral complexity on long time scales. Regarding the relevance of cognitive function to the complexity measures, the phase complexity on the long time scale was found to be correlated significantly with the Mini-Mental State Examination score. Additionally, we examined the diagnostic utility of the complexity characteristics using machine learning (ML) methods. We prepared a feature pool using multiple sparse autoencoders (SAEs), chose some discriminating features, and applied them to a support vector machine (SVM). Compared to the simple SVM and the SVM after feature selection (FS + SVM), the SVM with multiple SAEs (SAE + FS + SVM) had improved diagnostic accuracy. Through this study, we 1) advanced the understanding of neuronal complexity in AD patients using decomposed temporal complexity analysis and 2) demonstrated the effectiveness of combining ML methods with information about signal complexity for the diagnosis of AD.
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Affiliation(s)
- Naoki Furutani
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yuta Nariya
- Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Sarah Noto
- Faculty of Nursing, National College of Nursing, Tokyo, Japan
| | - Albert C Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Tetsu Hirosawa
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Masafumi Kameya
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yoshio Minabe
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
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Kaur Y, Ouyang G, Junge M, Sommer W, Liu M, Zhou C, Hildebrandt A. The reliability and psychometric structure of Multi-Scale Entropy measured from EEG signals at rest and during face and object recognition tasks. J Neurosci Methods 2019; 326:108343. [PMID: 31276692 DOI: 10.1016/j.jneumeth.2019.108343] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 05/29/2019] [Accepted: 07/01/2019] [Indexed: 12/23/2022]
Abstract
BACKGROUND Multi-Scale Entropy (MSE) is a widely used marker of Brain Signal Complexity (BSC) at multiple temporal scales. METHODOLOGICAL IMPROVEMENT There is no systematic research addressing the psychometric quality and reliability of MSE. It is unknown how recording conditions of EEG signals affect individual differences in MSE. These gaps can be addressed by means of Structural Equation Modeling (SEM). RESULTS Based on a large sample of 210 young adults, we estimated measurement models for MSE derived from multiple epochs of EEG signal measured during resting state conditions with closed and open eyes, and during a visual task with multiple experimental manipulations. Factor reliability estimates, quantified by the McDonald's ω coefficient, are high at lower and acceptable at higher time scales. Above individual differences in signal entropy observed across all recording conditions, persons specifically differ with respect to their BSC in open eyes resting state condition as compared with closed eyes state, and in task processing state MSE as compared with resting state. COMPARISON WITH EXISTING METHODS By means of SEM, we decomposed individual differences in BSC into different factors depending on the recording condition of EEG signals. This goes beyond existing methods that aim at estimating average MSE differences across recording conditions, but do not address whether individual differences are additionally affected by the type of EEG recording condition. CONCLUSION Eyes closed and open and task conditions strongly influence individual differences in MSE. We provide recommendations for future studies aiming to address BSC using MSE as a neural marker of cognitive abilities.
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Affiliation(s)
- Yadwinder Kaur
- Department of Psychology, University of Greifswald, Germany; Department of Psychology, Carl von Ossietzky Universität Oldenburg, Germany.
| | - Guang Ouyang
- Department of Psychology, University of Greifswald, Germany; The Laboratory of Neuroscience for Education, The University of Hong Kong, Hong Kong
| | - Martin Junge
- Department of Psychology, University of Greifswald, Germany
| | - Werner Sommer
- Department of Psychology, Humboldt-Universität zu Berlin, Germany
| | - Mianxin Liu
- Department of Physics and Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Changsong Zhou
- Department of Physics and Centre for Nonlinear Studies, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Germany.
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Abstract
Due to the non-linearity of numerous physiological recordings, non-linear analysis of multi-channel signals has been extensively used in biomedical engineering and neuroscience. Multivariate multiscale sample entropy (MSE–mvMSE) is a popular non-linear metric to quantify the irregularity of multi-channel time series. However, mvMSE has two main drawbacks: (1) the entropy values obtained by the original algorithm of mvMSE are either undefined or unreliable for short signals (300 sample points); and (2) the computation of mvMSE for signals with a large number of channels requires the storage of a huge number of elements. To deal with these problems and improve the stability of mvMSE, we introduce multivariate multiscale dispersion entropy (MDE–mvMDE), as an extension of our recently developed MDE, to quantify the complexity of multivariate time series. We assess mvMDE, in comparison with the state-of-the-art and most widespread multivariate approaches, namely, mvMSE and multivariate multiscale fuzzy entropy (mvMFE), on multi-channel noise signals, bivariate autoregressive processes, and three biomedical datasets. The results show that mvMDE takes into account dependencies in patterns across both the time and spatial domains. The mvMDE, mvMSE, and mvMFE methods are consistent in that they lead to similar conclusions about the underlying physiological conditions. However, the proposed mvMDE discriminates various physiological states of the biomedical recordings better than mvMSE and mvMFE. In addition, for both the short and long time series, the mvMDE-based results are noticeably more stable than the mvMSE- and mvMFE-based ones. For short multivariate time series, mvMDE, unlike mvMSE, does not result in undefined values. Furthermore, mvMDE is faster than mvMFE and mvMSE and also needs to store a considerably smaller number of elements. Due to its ability to detect different kinds of dynamics of multivariate signals, mvMDE has great potential to analyse various signals.
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Affiliation(s)
- Hamed Azami
- School of Engineering, Institute for Digital Communications, University of Edinburgh, King’s Buildings, Edinburgh EH9 3FB, UK;
- Department of Neurology and Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
- Correspondence:
| | - Alberto Fernández
- Departamento de Psiquiatría y Psicología Médica, Universidad Complutense de Madrid, 28040 Madrid, Spain;
- Laboratorio de Neurociencia Cognitiva y Computacional, Centro de Tecnología Biomédica, Universidad Politecnica de Madrid and Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Javier Escudero
- School of Engineering, Institute for Digital Communications, University of Edinburgh, King’s Buildings, Edinburgh EH9 3FB, UK;
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Shafer RL, Solomon EM, Newell KM, Lewis MH, Bodfish JW. Visual feedback during motor performance is associated with increased complexity and adaptability of motor and neural output. Behav Brain Res 2019; 376:112214. [PMID: 31494179 DOI: 10.1016/j.bbr.2019.112214] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 08/30/2019] [Accepted: 09/04/2019] [Indexed: 11/19/2022]
Abstract
Complex motor behavior is believed to be dependent on sensorimotor integration - the neural process of using sensory input to plan, guide, and correct movements. Previous studies have shown that the complexity of motor output is low when sensory feedback is withheld during precision motor tasks. However, much of this research has focused on motor behavior rather than neural processing, and therefore, has not specifically assessed the role of sensorimotor neural functioning in the execution of complex motor behavior. The present study uses a stimulus-tracking task with simultaneous electroencephalography (EEG) recording to assess the effect of visual feedback on motor performance, motor complexity, and sensorimotor neural processing in healthy adults. The complexity of the EEG signal was analyzed to capture the information content in frequency bands (alpha and beta) and scalp regions (central, parietal, and occipital) that are associated with sensorimotor processing. Consistent with previous literature, motor performance and its complexity were higher when visual feedback was provided relative to when it was withheld. The complexity of the neural signal was also higher when visual feedback was provided. This was most robust at frequency bands (alpha and beta) and scalp regions (parietal and occipital) associated with sensorimotor processing. The findings show that visual feedback increases the information available to the brain when generating complex, adaptive motor output.
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Affiliation(s)
- Robin L Shafer
- Vanderbilt Brain Institute, Vanderbilt University, 6133 Medical Research Building III, 465 21st Avenue South, Nashville, TN, 37232, USA.
| | - Eli M Solomon
- Neuroscience and Behavior Program, Wesleyan University Rm 257 Hall-Atwater, Wesleyan University, Middletown, CT, 06459, USA.
| | - Karl M Newell
- Department of Kinesiology, University of Georgia, G3 Aderhold Hall, 110 Carlton Street, Athens, GA, 30602, USA.
| | - Mark H Lewis
- Department of Psychiatry, University of Florida College of Medicine, PO Box 100256, L4-100 McKnight Brain Institute, 1149 Newell Drive, Gainesville, FL, 32611, USA.
| | - James W Bodfish
- Vanderbilt Brain Institute, Vanderbilt University, 6133 Medical Research Building III, 465 21st Avenue South, Nashville, TN, 37232, USA; Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, 8310 Medical Center East, 1215 21st Avenue South, Nashville, TN, 37232, USA.
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41
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Liu M, Song C, Liang Y, Knöpfel T, Zhou C. Assessing spatiotemporal variability of brain spontaneous activity by multiscale entropy and functional connectivity. Neuroimage 2019; 198:198-220. [PMID: 31091474 DOI: 10.1016/j.neuroimage.2019.05.022] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/17/2019] [Accepted: 05/09/2019] [Indexed: 01/24/2023] Open
Abstract
Brain signaling occurs across a wide range of spatial and temporal scales, and analysis of brain signal variability and synchrony has attracted recent attention as markers of intelligence, cognitive states, and brain disorders. However, current technologies to measure brain signals in humans have limited resolutions either in space or in time and cannot fully capture spatiotemporal variability, leaving it untested whether temporal variability and spatiotemporal synchrony are valid and reliable proxy of spatiotemporal variability in vivo. Here we used optical voltage imaging in mice under anesthesia and wakefulness to monitor cortical voltage activity at both high spatial and temporal resolutions to investigate functional connectivity (FC, a measure of spatiotemporal synchronization), Multi-Scale Entropy (MSE, a measure of temporal variability), and their relationships to Regional Entropy (RE, a measure of spatiotemporal variability). We observed that across cortical space, MSE pattern can largely explain RE pattern at small and large temporal scales with high positive and negative correlation respectively, while FC pattern strongly negatively associated with RE pattern. The time course of FC and small scale MSE tightly followed that of RE, while large scale MSE was more loosely coupled to RE. fMRI and EEG data simulated by reducing spatiotemporal resolution of the voltage imaging data or considering hemodynamics yielded MSE and FC measures that still contained information about RE based on the high resolution voltage imaging data. This suggested that MSE and FC could still be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. Our results support the notion that FC and MSE are effective biomarkers for brain states, and provide a promising viewpoint to unify these two principal domains in human brain data analysis.
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42
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Mulligan BP, Smart CM, Segalowitz SJ. Neuropsychological and resting-state electroencephalographic markers of older adult neurocognitive adaptability. Clin Neuropsychol 2019; 33:390-418. [PMID: 30648474 DOI: 10.1080/13854046.2018.1543453] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE This study was undertaken to explore multimethod neurocognitive screening tools to aid in detection of older adults who may be at heightened risk of pathological cognitive decline (preclinical dementia). In so doing, this study advances the theoretical conceptualization of neurocognitive adaptability in the context of aging and dementia. METHOD This article reports original data from the baseline measurement occasion of a longitudinal study of healthy, community-dwelling older adults from the Victoria, British Columbia region. Participants were diagnosed as normal, subtle decline, or mild cognitive impairment according to actuarial neuropsychological criteria (adjusted for age only or adjusted for age and premorbid IQ). Diagnostic classification was employed to illustrate group differences in a novel metric of multi-timescale neural adaptability derived from 4-min of resting-state electroencephalographic data collected from each participant (immediately following their neuropsychological evaluation). RESULTS Prior findings were replicated; adjusting raw neuropsychological test scores for individual differences in estimated premorbid IQ appeared to increase the sensitivity of standardized clinical tasks to subtle cognitive impairment. Moreover, and consistent with prior neuroscientific research, timescale-specific (i.e. at ∼12-20 ms timescales) differences in resting-state neural adaptability appeared to characterize groups who differed in terms of neuropsycholgoical diagnostic classification. CONCLUSIONS Recently proposed actuarial neuropsychological criteria for subtle cognitive decline identify older adults who show timescale-specific changes in resting brain function that may signal the onset of preclinical dementia. The subtle decline stage may represent a critical inflection point-partial loss of neurocognitive adaptability-on a pathological aging trajectory. These findings illustrate areas of potential future development in neurocognitive health care.
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Affiliation(s)
- Bryce P Mulligan
- a Department of Psychology , The Ottawa Hospital , Ottawa , Canada.,b Department of Psychology , University of Victoria , Victoria , Canada.,c Institute on Aging & Lifelong Health , University of Victoria , Victoria , Canada
| | - Colette M Smart
- b Department of Psychology , University of Victoria , Victoria , Canada.,c Institute on Aging & Lifelong Health , University of Victoria , Victoria , Canada
| | - Sidney J Segalowitz
- d Psychology Department , Brock University , St. Catharines , Canada.,e The Jack and Nora Walker Centre for Lifespan Development Research , Brock University , St. Catharines , Canada
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43
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Smith RX, Jann K, Dapretto M, Wang DJJ. Imbalance of Functional Connectivity and Temporal Entropy in Resting-State Networks in Autism Spectrum Disorder: A Machine Learning Approach. Front Neurosci 2018; 12:869. [PMID: 30542259 PMCID: PMC6277800 DOI: 10.3389/fnins.2018.00869] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 11/07/2018] [Indexed: 12/24/2022] Open
Abstract
Background: Two approaches to understanding the etiology of neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) involve network level functional connectivity (FC) and the dynamics of neuronal signaling. The former approach has revealed both increased and decreased FC in individuals with ASD. The latter approach has found high frequency EEG oscillations and higher levels of epilepsy in children with ASD. Together, these findings have led to the hypothesis that atypical excitatory-inhibitory neural signaling may lead to imbalanced association pathways. However, simultaneously reconciling local temporal dynamics with network scale spatial connectivity remains a difficult task and thus empirical support for this hypothesis is lacking. Methods: We seek to fill this gap by combining two powerful resting-state functional MRI (rs-fMRI) methods-functional connectivity (FC) and wavelet-based regularity analysis. Wavelet-based regularity analysis is an entropy measure of the local rs-fMRI time series signal. We examined the relationship between the RSN entropy and integrity in individuals with ASD and controls from the Autism Brain Imaging Data Exchange (ABIDE) cohort using a putative set of 264 functional brain regions-of-interest (ROI). Results: We observed that an imbalance in intra- and inter-network FC across 11 RSNs in ASD individuals (p = 0.002) corresponds to a weakened relationship with RSN temporal entropy (p = 0.02). Further, we observed that an estimated RSN entropy model significantly distinguished ASD from controls (p = 0.01) and was associated with level of ASD symptom severity (p = 0.003). Conclusions: Imbalanced brain connectivity and dynamics at the network level coincides with their decoupling in ASD. The association with ASD symptom severity presents entropy as a potential biomarker.
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Affiliation(s)
- Robert X. Smith
- NeuroImaging Laboratories (NIL) at Washington University School of Medicine, Washington University in Saint Louis, Saint Louis, MO, United States
| | - Kay Jann
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Danny J. J. Wang
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
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Low I, Kuo PC, Tsai CL, Liu YH, Lin MW, Chao HT, Chen YS, Hsieh JC, Chen LF. Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity. Front Neurosci 2018; 12:826. [PMID: 30524221 PMCID: PMC6256283 DOI: 10.3389/fnins.2018.00826] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2018] [Accepted: 10/23/2018] [Indexed: 12/28/2022] Open
Abstract
The irregularity and uncertainty of neurophysiologic signals across different time scales can be regarded as neural complexity, which is related to the adaptability of the nervous system and the information processing between neurons. We recently reported general loss of brain complexity, as measured by multiscale sample entropy (MSE), at pain-related regions in females with primary dysmenorrhea (PDM). However, it is unclear whether this loss of brain complexity is associated with inter-subject genetic variations. Brain-derived neurotrophic factor (BDNF) is a widely expressed neurotrophin in the brain and is crucial to neural plasticity. The BDNF Val66Met single-nucleotide polymorphism (SNP) is associated with mood, stress, and pain conditions. Therefore, we aimed to examine the interactions of BDNF Val66Met polymorphism and long-term menstrual pain experience on brain complexity. We genotyped BDNF Val66Met SNP in 80 PDM females (20 Val/Val, 31 Val/Met, 29 Met/Met) and 76 healthy female controls (25 Val/Val, 36 Val/Met, 15 Met/Met). MSE analysis was applied to neural source activity estimated from resting-state magnetoencephalography (MEG) signals during pain-free state. We found that brain complexity alterations were associated with the interactions of BDNF Val66Met polymorphism and menstrual pain experience. In healthy female controls, Met carriers (Val/Met and Met/Met) demonstrated lower brain complexity than Val/Val homozygotes in extensive brain regions, suggesting a possible protective role of Val/Val homozygosity in brain complexity. However, after experiencing long-term menstrual pain, the complexity differences between different genotypes in healthy controls were greatly diminished in PDM females, especially in the limbic system, including the hippocampus and amygdala. Our results suggest that pain experience preponderantly affects the effect of BDNF Val66Met polymorphism on brain complexity. The results of the present study also highlight the potential utilization of resting-state brain complexity for the development of new therapeutic strategies in patients with chronic pain.
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Affiliation(s)
- Intan Low
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.,Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Po-Chih Kuo
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Cheng-Lin Tsai
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
| | - Yu-Hsiang Liu
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Ming-Wei Lin
- Institute of Public Health, National Yang-Ming University, Taipei, Taiwan
| | - Hsiang-Tai Chao
- Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yong-Sheng Chen
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.,Center for Emergent Functional Matter Science, National Chiao Tung University, Hsinchu, Taiwan
| | - Jen-Chuen Hsieh
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.,Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Li-Fen Chen
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.,Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.,Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
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Hou F, Yu Z, Peng CK, Yang A, Wu C, Ma Y. Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset. Front Neurosci 2018; 12:809. [PMID: 30483046 PMCID: PMC6243118 DOI: 10.3389/fnins.2018.00809] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 10/17/2018] [Indexed: 11/24/2022] Open
Abstract
Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI1−30. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions.
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Affiliation(s)
- Fengzhen Hou
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Zhinan Yu
- Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing, China
| | - Chung-Kang Peng
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
| | - Albert Yang
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
| | - Chunyong Wu
- Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, China Pharmaceutical University, Nanjing, China.,Department of Pharmaceutical Analysis, China Pharmaceutical University, Nanjing, China
| | - Yan Ma
- Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States
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Cassani R, Estarellas M, San-Martin R, Fraga FJ, Falk TH. Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment. Dis Markers 2018; 2018:5174815. [PMID: 30405860 PMCID: PMC6200063 DOI: 10.1155/2018/5174815] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/12/2018] [Accepted: 07/29/2018] [Indexed: 12/17/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.
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Affiliation(s)
- Raymundo Cassani
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
| | - Mar Estarellas
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
- Department of Bioengineering, Imperial College London, London, UK
| | - Rodrigo San-Martin
- Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Francisco J. Fraga
- Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Tiago H. Falk
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
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Fan M, Yang AC, Fuh JL, Chou CA. Topological Pattern Recognition of Severe Alzheimer's Disease via Regularized Supervised Learning of EEG Complexity. Front Neurosci 2018; 12:685. [PMID: 30337850 PMCID: PMC6180281 DOI: 10.3389/fnins.2018.00685] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Accepted: 09/12/2018] [Indexed: 12/05/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales. Two regularized logistic regression methods were applied to extracted MSE features to capture the topographic pattern of MSEs of AD cohorts compared to healthy baseline. Furthermore, canonical correlation analysis was performed to evaluate the multivariate correlation between EEG complexity and cognitive dysfunction measured by the Neuropsychiatric Inventory scores. 123 participants were recruited and each participant was examined in three sessions (length = 10 seconds) to collect resting-state EEG signals. MSE features were extracted across 20 time scale factors with pre-determined parameters (m = 2, r = 0.15). The results showed that comparing to logistic regression model, the regularized learning methods performed better for discriminating severe AD cohort from normal control, very mild and mild cohorts (test accuracy ~ 80%), as well as for selecting significant biomarkers arcoss the brain regions. It was found that temporal and occipitoparietal brain regions were more discriminative in regard to classifying severe AD cohort vs. normal controls, but more diverse and distributed patterns of EEG complexity in the brain were exhibited across individuals in early stages of AD.
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Affiliation(s)
- Miaolin Fan
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, United States
| | - Albert C Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.,Institute of Brain Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Jong-Ling Fuh
- Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Chun-An Chou
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, United States
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Abstract
There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. However, some traditional complexity measure algorithms only quantify the complexities of signals, but cannot discriminate different signals very well. To analyze the complexity of epileptic EEG signals better, a new multiscale permutation Rényi entropy (MPEr) algorithm is proposed. In this algorithm, the coarse-grained procedure is introduced by using weighting-averaging method, and the weighted factors are determined by analyzing nonlinear signals. We apply the new algorithm to analyze epileptic EEG signals. The experimental results show that MPEr algorithm has good performance for discriminating different EEG signals. Compared with permutation Rényi entropy (PEr) and multiscale permutation entropy (MPE), MPEr distinguishes different EEG signals successfully. The proposed MPEr algorithm is effective and has good applications prospects in EEG signals analysis.
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Affiliation(s)
- Yinghuang Yin
- School of Physics and Electronics, Central South University, Changsha, P.R.China
| | - Kehui Sun
- School of Physics and Electronics, Central South University, Changsha, P.R.China
- * E-mail:
| | - Shaobo He
- School of Computer Science and Technology, Hunan University of Arts and Science, Changde, P.R.China
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Azami H, Kinney-Lang E, Ebied A, Fernandez A, Escudero J. Multiscale dispersion entropy for the regional analysis of resting-state magnetoencephalogram complexity in Alzheimer's disease. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2017:3182-3185. [PMID: 29060574 DOI: 10.1109/embc.2017.8037533] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Alzheimer's disease (AD) is a progressive and irreversible brain disorder of the nervous system affecting memory, thinking, and emotion. It is the most important cause of dementia and an influential social problem in all the world. The complexity of brain recordings has been successfully used to help to characterize AD. We have recently introduced multiscale dispersion entropy (MDE) as a very fast and powerful tool to quantify the complexity of signals. The aim of this study is to assess the ability of MDE, in comparison with multiscale permutation entropy (MPE) and multiscale entropy (MSE), to discriminate 36 AD patients from 26 elderly age-matched control subjects using resting-state magnetoencephalogram (MEG) recordings. The results showed that MDE, unlike MSE, does not lead to undefined values. Moreover, the differences between the MDE values for AD palatines versus controls were more significant than their corresponding MSE- and MPE-based values. In addition, the computation time for our recently developed MDE was considerably less than that for MSE and even MPE.
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