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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] [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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2
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Al-Nuaimi AH, Blūma M, Al-Juboori SS, Eke CS, Jammeh E, Sun L, Ifeachor E. Robust EEG Based Biomarkers to Detect Alzheimer's Disease. Brain Sci 2021; 11:1026. [PMID: 34439645 PMCID: PMC8394244 DOI: 10.3390/brainsci11081026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 11/16/2022] Open
Abstract
Biomarkers to detect Alzheimer's disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).
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Affiliation(s)
- Ali H. Al-Nuaimi
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
- College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Al Adhamiya, Baghdad 10053, Iraq
| | - Marina Blūma
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy;
| | - Shaymaa S. Al-Juboori
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
- College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Al Adhamiya, Baghdad 10053, Iraq
| | - Chima S. Eke
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Emmanuel Jammeh
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Lingfen Sun
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Emmanuel Ifeachor
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
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3
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Shigihara Y, Hoshi H, Shinada K, Okada T, Kamada H. Non-pharmacological treatment changes brain activity in patients with dementia. Sci Rep 2020; 10:6744. [PMID: 32317774 PMCID: PMC7174400 DOI: 10.1038/s41598-020-63881-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 04/07/2020] [Indexed: 12/05/2022] Open
Abstract
Non-pharmacological treatment (NPT) improves cognitive functions and behavioural disturbances in patients with dementia, but the underlying neural mechanisms are unclear. In this observational study, 21 patients with dementia received NPTs for several months. Patients were scanned using magnetoencephalography twice during the NPT period to evaluate NPT effects on resting-state brain activity. Additionally, cognitive functions and behavioural disturbances were measured using the Mini-Mental State Examination (MMSE-J) and a short version of the Dementia Behaviour Disturbance Scale (DBD-13) at the beginning and the end of the NPT period. In contrast to the average DBD-13 score, the average MMSE-J score improved after the NPT period. Magnetoencephalography data revealed a reduced alpha activity in the right temporal lobe and fusiform gyrus, as well as an increased low-gamma activity in the right angular gyrus. DBD-13 score changes were correlated with beta activity in the sensorimotor area. These findings corroborate previous studies confirming NPT effects on brain activity in healthy participants and people at risk of dementia. Our results provide additional evidence that brains of patients with dementia have the capacity for plasticity, which may be responsible for the observed NPT effects. In dementia, NPT might lead to improvements in the quality of life.
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Affiliation(s)
- Yoshihito Shigihara
- Precision Medicine Centre, Hokuto Hospital, Obihiro City, Japan.
- Department of Neurosurgery, Hokuto Hospital, Obihiro City, Japan.
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro City, Japan
| | - Keita Shinada
- Geriatric Health Services Facility Kakehashi, Hokuto Hospital Group, Obihiro City, Japan
| | - Toyoji Okada
- Department of Clinical Laboratory, Hokuto Hospital, Obihiro City, Japan
| | - Hajime Kamada
- Department of Neurosurgery, Hokuto Hospital, Obihiro City, Japan
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Echegoyen I, López-Sanz D, Martínez JH, Maestú F, Buldú JM. Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer's Disease: An Analysis Based on Frequency Bands. ENTROPY 2020; 22:e22010116. [PMID: 33285891 PMCID: PMC7516422 DOI: 10.3390/e22010116] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/15/2020] [Accepted: 01/16/2020] [Indexed: 12/14/2022]
Abstract
We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer's Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.
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Affiliation(s)
- Ignacio Echegoyen
- Laboratory of Biological Networks, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain;
- Complex Systems Group, Rey Juan Carlos University, 28933 Madrid, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain;
- Correspondence:
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain; (D.L.-S.); (F.M.)
- Department of Experimental Psychology, Complutense University of Madrid, 28223 Madrid, Spain
| | - Johann H. Martínez
- Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain;
- Biomedical Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain; (D.L.-S.); (F.M.)
- Department of Experimental Psychology, Complutense University of Madrid, 28223 Madrid, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, 28029 Zaragoza, Spain
| | - Javier M. Buldú
- Laboratory of Biological Networks, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain;
- Complex Systems Group, Rey Juan Carlos University, 28933 Madrid, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain;
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Wang B, Niu Y, Miao L, Cao R, Yan P, Guo H, Li D, Guo Y, Yan T, Wu J, Xiang J, Zhang H. Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping. Front Aging Neurosci 2017; 9:378. [PMID: 29209199 PMCID: PMC5701971 DOI: 10.3389/fnagi.2017.00378] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 11/03/2017] [Indexed: 01/05/2023] Open
Abstract
Alzheimer's disease (AD) is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI) has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI) and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE) to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients. The rs-fMRI signals of 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. After preprocessing, whole-brain entropy maps of the four groups were extracted and subjected to Gaussian smoothing. We performed a one-way analysis of variance (ANOVA) on the brain entropy maps of the four groups. The results after adjusting for age and sex differences together revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls. We found five clusters that exhibited significant differences and were distributed primarily in the occipital, frontal, and temporal lobes. The average PE of the five clusters exhibited a decreasing trend from MCI to AD. The AD group exhibited the least complexity. Additionally, the average PE of the five clusters was significantly positively correlated with the Mini-Mental State Examination (MMSE) scores and significantly negatively correlated with Functional Assessment Questionnaire (FAQ) scores and global Clinical Dementia Rating (CDR) scores in the patient groups. Significant correlations were also found between the PE and regional homogeneity (ReHo) in the patient groups. These results indicated that declines in PE might be related to changes in regional functional homogeneity in AD. These findings suggested that complexity analyses using PE in rs-fMRI signals can provide important information about the fMRI characteristics of cognitive impairments in MCI and AD.
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Affiliation(s)
- Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.,Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yan Niu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Liwen Miao
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Rui Cao
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Pengfei Yan
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yuxiang Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China.,Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing Institute of Technology, Beijing, China.,Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
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