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Khatun S, Morshed BI, Bidelman GM. Monitoring Disease Severity of Mild Cognitive Impairment from Single-Channel EEG Data Using Regression Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:1054. [PMID: 38400211 PMCID: PMC10893543 DOI: 10.3390/s24041054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/28/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024]
Abstract
A deviation in the soundness of cognitive health is known as mild cognitive impairment (MCI), and it is important to monitor it early to prevent complicated diseases such as dementia, Alzheimer's disease (AD), and Parkinson's disease (PD). Traditionally, MCI severity is monitored with manual scoring using the Montreal Cognitive Assessment (MoCA). In this study, we propose a new MCI severity monitoring algorithm with regression analysis of extracted features of single-channel electro-encephalography (EEG) data by automatically generating severity scores equivalent to MoCA scores. We evaluated both multi-trial and single-trail analysis for the algorithm development. For multi-trial analysis, 590 features were extracted from the prominent event-related potential (ERP) points and corresponding time domain characteristics, and we utilized the lasso regression technique to select the best feature set. The 13 best features were used in the classical regression techniques: multivariate regression (MR), ensemble regression (ER), support vector regression (SVR), and ridge regression (RR). The best results were observed for ER with an RMSE of 1.6 and residual analysis. In single-trial analysis, we extracted a time-frequency plot image from each trial and fed it as an input to the constructed convolutional deep neural network (CNN). This deep CNN model resulted an RMSE of 2.76. To our knowledge, this is the first attempt to generate automated scores for MCI severity equivalent to MoCA from single-channel EEG data with multi-trial and single data.
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Affiliation(s)
- Saleha Khatun
- Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN 38152, USA
| | - Bashir I. Morshed
- Compute Science Department, Texas Tech University, Lubbock, TX 79409, USA
| | - Gavin M. Bidelman
- School of Communication Sciences and Disorders, University of Memphis, Memphis, TN 38152, USA
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2
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Arjmandi-Rad S, Vestergaard Nieland JD, Goozee KG, Vaseghi S. The effects of different acetylcholinesterase inhibitors on EEG patterns in patients with Alzheimer's disease: A systematic review. Neurol Sci 2024; 45:417-430. [PMID: 37843690 DOI: 10.1007/s10072-023-07114-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/01/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVE Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia. The early diagnosis of AD is an important factor for the control of AD progression. Electroencephalography (EEG) can be used for early diagnosis of AD. Acetylcholinesterase inhibitors (AChEIs) are also used for the amelioration of AD symptoms. In this systematic review, we reviewed the effect of different AChEIs including donepezil, rivastigmine, tacrine, physostigmine, and galantamine on EEG patterns in patients with AD. METHODS PubMed electronic database was searched and 122 articles were found. After removal of unrelated articles, 24 articles were selected for the present study. RESULTS AChEIs can decrease beta, theta, and delta frequency bands in patients with AD. However, conflicting results were found for alpha band. Some studies have shown increased alpha frequency, while others have shown decreased alpha frequency following treatment with AChEIs. The only difference was the type of drug. CONCLUSIONS We found that studies reporting the decreased alpha frequency used donepezil and galantamine, while studies reporting the increased alpha frequency used rivastigmine and tacrine. It was suggested that future studies should focus on the effect of different AChEIs on EEG bands, especially alpha frequency in patients with AD, to compare their effects and find the reason for their different influence on EEG patterns. Also, differences between the effects of AChEIs on oligodendrocyte differentiation and myelination may be another important factor. This is the first article investigating the effect of different AChEIs on EEG patterns in patients with AD.
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Affiliation(s)
- Shirin Arjmandi-Rad
- Institute for Cognitive & Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | | | - Kathryn G Goozee
- KaRa Institute of Neurological Diseases Pty Ltd, Macquarie, NSW, Australia
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, Australia
| | - Salar Vaseghi
- Cognitive Neuroscience Lab, Medicinal Plants Research Center, Institute of Medicinal Plants, ACECR, Karaj, Iran.
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Kanthi A, Singh D, Manjunath NK, Nagarathna R. Changes in Electrical Activities of the Brain Associated with Cognitive Functions in Type 2 Diabetes Mellitus: A Systematic Review. Clin EEG Neurosci 2024; 55:130-142. [PMID: 35343277 DOI: 10.1177/15500594221089106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Introduction: Electroencephalogram (EEG) has the potentials to decipher the neural underpinnings of cognitive processes in clinical and healthy populations. Objective: The current systematic review is intended to examine the functional brain changes underlying cognitive dysfunctions in T2DM patients. Methods: The review was conducted on studies published in the PubMed, WebofScience, Cochrane, PsycInfo database till June 2021. The keywords used were electroencephalogram, T2DM, cognitive impairment/dysfunction. We considered studies using resting-state EEG and ERP. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines were followed to compile the studies. Results: The search yielded a total of 2384 studies. Finally, 16 independent studies were included. There was a pattern of a shift in EEG power observed from higher to lower frequencies in T2DM patients, though to a lesser degree than Alzheimer's disease patients. P300 latency was increased in T2DM patients mainly over frontal, parietal, and posterior regions. P300 and N100 amplitudes were decreased in T2DM patients than in healthy controls. Conclusion: The results indicate that T2DM has consequences for cognitive functions, and it finds a place in the continuum of healthy cognition to dementia.
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Affiliation(s)
- Amit Kanthi
- Department of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bangalore, India
| | - Deepeshwar Singh
- Department of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bangalore, India
| | - N K Manjunath
- Department of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bangalore, India
| | - Raghuram Nagarathna
- Arogyadhama, Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bangalore, India
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Rosanne O, Alves de Oliveira A, Falk TH. EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs. SENSORS (BASEL, SWITZERLAND) 2023; 23:9352. [PMID: 38067725 PMCID: PMC10708818 DOI: 10.3390/s23239352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023]
Abstract
Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.
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Affiliation(s)
- Olivier Rosanne
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada;
| | - Alcyr Alves de Oliveira
- Graduate Program in Psychology and Health, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil;
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada;
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Chu KT, Lei WC, Wu MH, Fuh JL, Wang SJ, French IT, Chang WS, Chang CF, Huang NE, Liang WK, Juan CH. A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer's disease. Front Aging Neurosci 2023; 15:1195424. [PMID: 37674782 PMCID: PMC10477374 DOI: 10.3389/fnagi.2023.1195424] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/25/2023] [Indexed: 09/08/2023] Open
Abstract
Aims Our aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms. Methods A total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms. Results (a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier. Conclusion Integrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage.
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Affiliation(s)
- Kwo-Ta Chu
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Yang-Ming Hospital, Taoyuan, Taiwan
| | - Weng-Chi Lei
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan, Taiwan
| | - Ming-Hsiu Wu
- Division of Neurology, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
- Department of Long-Term Care and Health Promotion, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan
| | - Jong-Ling Fuh
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shuu-Jiun Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Isobel T. 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
| | - Wen-Sheng Chang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
| | - Chi-Fu Chang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
| | - Norden E. Huang
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan, Taiwan
- Key Laboratory of Data Analysis and Applications, First Institute of Oceanography, SOA, Qingdao, China
| | - Wei-Kuang Liang
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan, Taiwan
| | - Chi-Hung Juan
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan, Taiwan
- Department of Psychology, Kaohsiung Medical University, Kaohsiung, Taiwan
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Klepl D, He F, Wu M, Blackburn DJ, Sarrigiannis PG. Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer's Disease. Neuroscience 2023; 521:77-88. [PMID: 37121381 DOI: 10.1016/j.neuroscience.2023.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/08/2023] [Accepted: 04/04/2023] [Indexed: 05/02/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals, such as electroencephalography (EEG) recordings, into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the reconstruction of a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. Cross-bispectrum detects cross-frequency differences, mainly increased FC in AD cases in δ-θ coupling. Overall, increased strength of low-frequency coupling and decreased level of high-frequency coupling is observed in AD cases in comparison to healthy controls (HC). We demonstrate that a graph-theoretic analysis of cross-frequency brain networks is crucial to obtain a more detailed insight into their structure and function. Vulnerability analysis reveals that the integration and segregation properties of networks are enabled by different frequency couplings in AD networks compared to HCs. Finally, we use the reconstructed networks for classification. The extra cross-frequency coupling information can improve the classification performance significantly, suggesting an important role of cross-frequency FC. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD.
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Affiliation(s)
- Dominik Klepl
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2JH, UK; Infocomm Research, A*STAR, Singapore
| | - Fei He
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2JH, UK.
| | - Min Wu
- Infocomm Research, A*STAR, Singapore
| | - Daniel J Blackburn
- Department of Neuroscience, University of Sheffield, SheffieldS10 2HQ, UK
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Gunawardena R, Sarrigiannis PG, Blackburn DJ, He F. Kernel-based Nonlinear Manifold Learning for EEG-based Functional Connectivity Analysis and Channel Selection with Application to Alzheimer's Disease. Neuroscience 2023:S0306-4522(23)00253-1. [PMID: 37301505 DOI: 10.1016/j.neuroscience.2023.05.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 05/15/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
Dynamical, causal, and cross-frequency coupling analysis using the electroencephalogram (EEG) has gained significant attention for diagnosing and characterizing neurological disorders. Selecting important EEG channels is crucial for reducing computational complexity in implementing these methods and improving classification accuracy. In neuroscience, measures of (dis)similarity between EEG channels are often used as functional connectivity (FC) features, and important channels are selected via feature selection. Developing a generic measure of (dis)similarity is important for FC analysis and channel selection. In this study, learning of (dis)similarity information within the EEG is achieved using kernel-based nonlinear manifold learning. The focus is on FC changes and, thereby, EEG channel selection. Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM) are employed for this purpose. The resulting kernel (dis)similarity matrix is used as a novel measure of linear and nonlinear FC between EEG channels. The analysis of EEG from healthy controls (HC) and patients with mild to moderate Alzheimer's disease (AD) are presented as a case study. Classification results are compared with other commonly used FC measures. Our analysis shows significant differences in FC between bipolar channels of the occipital region and other regions (i.e. parietal, centro-parietal, and fronto-central) between AD and HC groups. Furthermore, our results indicate that FC changes between channels along the fronto-parietal region and the rest of the EEG are important in diagnosing AD. Our results and its relation to functional networks are consistent with those obtained from previous studies using fMRI, resting-state fMRI and EEG.
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Affiliation(s)
- Rajintha Gunawardena
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, CV1 5FB, UK
| | | | - Daniel J Blackburn
- Department of Neuroscience, The University of Sheffield, Sheffield, S10 2HQ, UK
| | - Fei He
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, CV1 5FB, UK.
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Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer's Disease Diagnosis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:3198066. [PMID: 36818579 PMCID: PMC9931465 DOI: 10.1155/2023/3198066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/15/2022] [Accepted: 01/11/2023] [Indexed: 02/11/2023]
Abstract
Biomarkers based on resting-state electroencephalography (EEG) signals have emerged as a promising tool in the study of Alzheimer's disease (AD). Recently, a state-of-the-art biomarker was found based on visual inspection of power modulation spectrograms where three "patches" or regions from the modulation spectrogram were proposed and used for AD diagnostics. Here, we propose the use of deep neural networks, in particular convolutional neural networks (CNNs) combined with saliency maps, trained on power modulation spectrogram inputs to find optimal patches in a data-driven manner. Experiments are conducted on EEG data collected from fifty-four participants, including 20 healthy controls, 19 patients with mild AD, and 15 moderate-to-severe AD patients. Five classification tasks are explored, including the three-class problem, early-stage detection (control vs. mild-AD), and severity level detection (mild vs. moderate-to-severe). Experimental results show the proposed biomarkers outperform the state-of-the-art benchmark across all five tasks, as well as finding complementary modulation spectrogram regions not previously seen via visual inspection. Lastly, experiments are conducted on the proposed biomarkers to test their sensitivity to age, as this is a known confound in AD characterization. Across all five tasks, none of the proposed biomarkers showed a significant relationship with age, thus further highlighting their usefulness for automated AD diagnostics.
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Tiwari A, Cassani R, Kshirsagar S, Tobon DP, Zhu Y, Falk TH. Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note. SENSORS 2022; 22:s22124579. [PMID: 35746361 PMCID: PMC9229858 DOI: 10.3390/s22124579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/14/2022] [Accepted: 06/15/2022] [Indexed: 02/04/2023]
Abstract
Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms.
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Affiliation(s)
- Abhishek Tiwari
- Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada; (A.T.); (S.K.); (Y.Z.)
- Myant Inc., Toronto, ON M9W 1B6, Canada
| | - Raymundo Cassani
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada;
| | - Shruti Kshirsagar
- Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada; (A.T.); (S.K.); (Y.Z.)
| | - Diana P. Tobon
- Faculty of Engineering, Universidad de Medellín, Medellín 050026, Colombia;
| | - Yi Zhu
- Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada; (A.T.); (S.K.); (Y.Z.)
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada; (A.T.); (S.K.); (Y.Z.)
- Correspondence:
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qEEG Analysis in the Diagnosis of Alzheimer’s Disease: A Comparison of Functional Connectivity and Spectral Analysis. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Alzheimer’s disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain and decline of cognitive abilities. This study compared an FFT-based spectral analysis against a functional connectivity analysis for the diagnosis of AD. Both quantitative methods were applied on an EEG dataset including 20 diagnosed AD patients and 20 age-matched healthy controls (HC). The obtained results showed an advantage of the functional connectivity analysis when compared to the spectral analysis; while the latter could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting a ‘phase-locked’ state in AD-affected brains. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized to the centro-parietal and centro-temporal areas in the theta frequency band (4–8 Hz). This study applies a neural metric for Alzheimer’s detection from a data science perspective rather than from a neuroscience one and shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.
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Alvi AM, Siuly S, Wang H. Neurological abnormality detection from electroencephalography data: a review. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10062-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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12
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Şeker M, Özbek Y, Yener G, Özerdem MS. Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 206:106116. [PMID: 33957376 DOI: 10.1016/j.cmpb.2021.106116] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 04/11/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Electroencephalogram (EEG) is one of the most demanded screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain. Identification of AD in early stage gives rise to efficient treatment in dementia. Mild Cognitive Impairment (MCI) is considered as a conversion stage. Reducing EEG complexity can be used as a marker to detect AD. The aim of this study is to develop a 3-way diagnostic classification using EEG complexity in the detection of MCI/AD in clinical practice. This study also investigates the effects of different eyes states, i.e. eyes-open, eyes-closed on classification performance. METHODS EEG recordings from 85 AD, 85 MCI subjects, and 85 Healthy Controls with eyes-open and eyes- closed are analyzed. Permutation Entropy (PE) values are computed from frontal, central, parietal, temporal, and occipital regions for each EEG epoch. Distribution of PE values are visualized to observe discrimination of MCI/AD with HC. Visual investigations are combined with statistical analysis using ANOVA to determine whether groups are significant or not. Multinomial Logistic Regression model is applied to feature sets in order to classify participants individually. RESULTS Distribution of measured PE shows that EEG complexity is lower in AD and higher in HC group. MCI group is observed as an intermediate form due to heterogeneous values. Results from 3-way classification indicate that F1-scores and rates of sensitivity and specificity achieve the highest overall discrimination rates reaching up to 100% for at TP8 for eyes-closed condition; and C3, C4, T8, O2 electrodes for eyes-open condition. Classification of HC from both patient groups is achieved best. Eyes-open state increases discrimination of MCI and AD. CONCLUSIONS This nonlinear EEG methodology study contributes to literature with high discrimination rates for identification of AD. PE is recommended as a practical diagnostic neuro-marker for AD studies. Resting state EEG at eyes-open condition can be more advantageous over eyes-closed EEG recordings for diagnosis of AD.
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Affiliation(s)
- Mesut Şeker
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
| | - Yağmur Özbek
- Department of Neurosciences, Health Science Institute, Dokuz Eylül University, Izmir
| | - Görsev Yener
- Department of Neurosciences, Health Science Institute, Dokuz Eylül University, Izmir; Izmir Biomedicine and Genome Center, Izmir, Turkey; Department of Neurology, Faculty of Medicine, Izmir Ekonomi University, Izmir, Turkey; Department of Neurology, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey
| | - Mehmet Siraç Özerdem
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey
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Rosanne O, Albuquerque I, Cassani R, Gagnon JF, Tremblay S, Falk TH. Adaptive Filtering for Improved EEG-Based Mental Workload Assessment of Ambulant Users. Front Neurosci 2021; 15:611962. [PMID: 33897342 PMCID: PMC8058356 DOI: 10.3389/fnins.2021.611962] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
Recently, due to the emergence of mobile electroencephalography (EEG) devices, assessment of mental workload in highly ecological settings has gained popularity. In such settings, however, motion and other common artifacts have been shown to severely hamper signal quality and to degrade mental workload assessment performance. Here, we show that classical EEG enhancement algorithms, conventionally developed to remove ocular and muscle artifacts, are not optimal in settings where participant movement (e.g., walking or running) is expected. As such, an adaptive filter is proposed that relies on an accelerometer-based referential signal. We show that when combined with classical algorithms, accurate mental workload assessment is achieved. To test the proposed algorithm, data from 48 participants was collected as they performed the Revised Multi-Attribute Task Battery-II (MATB-II) under a low and a high workload setting, either while walking/jogging on a treadmill, or using a stationary exercise bicycle. Accuracy as high as 95% could be achieved with a random forest based mental workload classifier with ambulant users. Moreover, an increase in gamma activity was found in the parietal cortex, suggesting a connection between sensorimotor integration, attention, and workload in ambulant users.
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Affiliation(s)
- Olivier Rosanne
- Institut National de la Recherche Scientifique - Centre Énergie, Matériaux et Télécomunication, Université du Québec, Montréal, QC, Canada
| | - Isabela Albuquerque
- Institut National de la Recherche Scientifique - Centre Énergie, Matériaux et Télécomunication, Université du Québec, Montréal, QC, Canada
| | - Raymundo Cassani
- Institut National de la Recherche Scientifique - Centre Énergie, Matériaux et Télécomunication, Université du Québec, Montréal, QC, Canada
| | | | | | - Tiago H Falk
- Institut National de la Recherche Scientifique - Centre Énergie, Matériaux et Télécomunication, Université du Québec, Montréal, QC, Canada
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14
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Perez-Valero E, Lopez-Gordo MA, Morillas C, Pelayo F, Vaquero-Blasco MA. A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG. J Alzheimers Dis 2021; 80:1363-1376. [PMID: 33682717 DOI: 10.3233/jad-201455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the economic and social implications of the disease, traditional diagnosis techniques, and the fundaments of automated AD detection. Then, we present electroencephalography (EEG) as an appropriate alternative for the early detection of AD, owing to its reduced cost, portability, and non-invasiveness. We also describe the main time and frequency domain EEG features that are employed in AD detection. Subsequently, we examine some of the main studies of the last decade that aim to provide an automatic detection of AD and its previous stages by means of SP and ML. In these studies, brain data was acquired using multiple medical techniques such as magnetic resonance imaging, positron emission tomography, and EEG. The main aspects of each approach, namely feature extraction, classification model, validation approach, and performance metrics, are compiled and discussed. Lastly, a set of conclusions and recommendations for future research on AD automatic detection are drawn in the final section of the paper.
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Affiliation(s)
- Eduardo Perez-Valero
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Miguel A Lopez-Gordo
- Department of Signal Theory, Telematics and Communications, University of Granada, Granada,Spain.,Nicolo Association, Churriana de la Vega, Spain
| | - Christian Morillas
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Francisco Pelayo
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Computer Architecture and Technology, University of Granada, Granada, Spain
| | - Miguel A Vaquero-Blasco
- Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada, Spain.,Department of Signal Theory, Telematics and Communications, University of Granada, Granada,Spain
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15
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Mansourian M, Khademi S, Marateb HR. A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining. Diagnostics (Basel) 2021; 11:393. [PMID: 33669114 PMCID: PMC7996506 DOI: 10.3390/diagnostics11030393] [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: 01/03/2021] [Revised: 02/13/2021] [Accepted: 02/17/2021] [Indexed: 02/07/2023] Open
Abstract
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.
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Affiliation(s)
- Mahsa Mansourian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran;
| | - Sadaf Khademi
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
| | - Hamid Reza Marateb
- Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan 8174-67344, Iran;
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16
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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] [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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17
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Macedo A, Gómez C, Rebelo MÂ, Poza J, Gomes I, Martins S, Maturana-Candelas A, Pablo VGD, Durães L, Sousa P, Figueruelo M, Rodríguez M, Pita C, Arenas M, Álvarez L, Hornero R, Lopes AM, Pinto N. Risk Variants in Three Alzheimer's Disease Genes Show Association with EEG Endophenotypes. J Alzheimers Dis 2021; 80:209-223. [PMID: 33522999 PMCID: PMC8075394 DOI: 10.3233/jad-200963] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background: Dementia due to Alzheimer’s disease (AD) is a complex neurodegenerative disorder, which much of heritability remains unexplained. At the clinical level, one of the most common physiological alterations is the slowing of oscillatory brain activity, measurable by electroencephalography (EEG). Relative power (RP) at the conventional frequency bands (i.e., delta, theta, alpha, beta-1, and beta-2) can be considered as AD endophenotypes. Objective: The aim of this work is to analyze the association between sixteen genes previously related with AD: APOE, PICALM, CLU, BCHE, CETP, CR1, SLC6A3, GRIN2
β, SORL1, TOMM40, GSK3
β, UNC5C, OPRD1, NAV2, HOMER2, and IL1RAP, and the slowing of the brain activity, assessed by means of RP at the aforementioned frequency bands. Methods: An Iberian cohort of 45 elderly controls, 45 individuals with mild cognitive impairment, and 109 AD patients in the three stages of the disease was considered. Genomic information and brain activity of each subject were analyzed. Results: The slowing of brain activity was observed in carriers of risk alleles in IL1RAP (rs10212109, rs9823517, rs4687150), UNC5C (rs17024131), and NAV2 (rs1425227, rs862785) genes, regardless of the disease status and situation towards the strongest risk factors: age, sex, and APOE ɛ4 presence. Conclusion: Endophenotypes reduce the complexity of the general phenotype and genetic variants with a major effect on those specific traits may be then identified. The found associations in this work are novel and may contribute to the comprehension of AD pathogenesis, each with a different biological role, and influencing multiple factors involved in brain physiology.
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Affiliation(s)
- Ana Macedo
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,JTA: The Data Scientists, Porto, Portugal
| | - Carlos Gómez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
| | - Miguel Ângelo Rebelo
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Jesús Poza
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
| | - Iva Gomes
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Sandra Martins
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | | | | | - Luis Durães
- Associação Portuguesa de Familiares e Amigos de Doentes de Alzheimer, Lavra, Portugal
| | - Patrícia Sousa
- Associação Portuguesa de Familiares e Amigos de Doentes de Alzheimer, Lavra, Portugal
| | - Manuel Figueruelo
- Asociación de Familiares y Amigos de Enfermos de Alzheimer y otras demencias de Zamora, Zamora, Spain
| | - María Rodríguez
- Asociación de Familiares y Amigos de Enfermos de Alzheimer y otras demencias de Zamora, Zamora, Spain
| | - Carmen Pita
- Asociación de Familiares y Amigos de Enfermos de Alzheimer y otras demencias de Zamora, Zamora, Spain
| | - Miguel Arenas
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,CINBIO (Biomedical Research Center), University of Vigo, Vigo, Spain.,Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain
| | - Luis Álvarez
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,Adeneas, Valencia, Spain
| | - Roberto Hornero
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
| | - Alexandra M Lopes
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | - Nádia Pinto
- IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Porto, Portugal.,i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,Centro de Matemática da Universidade do Porto, Porto, Portugal
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18
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Maturana-Candelas A, Gómez C, Poza J, Ruiz-Gómez SJ, Hornero R. Inter-band Bispectral Analysis of EEG Background Activity to Characterize Alzheimer's Disease Continuum. Front Comput Neurosci 2020; 14:70. [PMID: 33100999 PMCID: PMC7554631 DOI: 10.3389/fncom.2020.00070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 06/25/2020] [Indexed: 11/13/2022] Open
Abstract
The aim of this study was to characterize the EEG alterations in inter-band interactions along the Alzheimer's disease (AD) continuum. For this purpose, EEG background activity from 51 healthy control subjects, 51 mild cognitive impairment patients, 50 mild AD patients, 50 moderate AD patients, and 50 severe AD patients was analyzed by means of bispectrum. Three inter-band features were extracted from bispectrum matrices: bispectral relative power (BispRP), cubic bispectral entropy (BispEn), and bispectral median frequency (BispMF). BispRP results showed an increase of delta and theta interactions with other frequency bands and the opposite behavior for alpha, beta-1, and beta-2. Delta and theta interactions, along with the rest of the spectrum, also experimented a decrease of BispEn with disease progression, suggesting these bands interact with a reduced variety of components in advanced stages of dementia. Finally, BispMF showed a consistent reduction along the AD continuum in all bands, which is reflective of an interaction of the global spectrum with lower frequency bands as the disease develops. Our results indicate a progressive decrease in inter-band interactions with the severity of the disease, especially those involving high frequency components. Since inter-band coupling oscillations are related to complex and multi-scaled brain processes, these alterations likely reflect the neurodegeneration associated with the AD continuum.
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Affiliation(s)
- Aarón Maturana-Candelas
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
| | - Saúl J Ruiz-Gómez
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
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19
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Farina FR, Emek-Savaş DD, Rueda-Delgado L, Boyle R, Kiiski H, Yener G, Whelan R. A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment. Neuroimage 2020; 215:116795. [PMID: 32278090 DOI: 10.1016/j.neuroimage.2020.116795] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 03/27/2020] [Accepted: 03/28/2020] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs = 1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs = 0.67-0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI.
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Affiliation(s)
- F R Farina
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
| | - D D Emek-Savaş
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, Izmir, 35160, Turkey; Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland
| | - L Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - R Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - H Kiiski
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Department of Neurology, Dokuz Eylul University Medical School, Izmir, 35340, Turkey
| | - R Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland.
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20
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Cai C, Huang C, Yang C, Zhang X, Peng Y, Zhao W, Hong X, Ren F, Hong D, Xiao Y, Yan J. Altered Patterns of Phase Position Connectivity in Default Mode Subnetwork of Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment. Front Neurosci 2020; 14:185. [PMID: 32265623 PMCID: PMC7099636 DOI: 10.3389/fnins.2020.00185] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 02/19/2020] [Indexed: 01/19/2023] Open
Abstract
Alzheimer’s disease (AD), which most commonly occurs in the elder, is a chronic neurodegenerative disease with no agreed drugs or treatment protocols at present. Amnestic mild cognitive impairment (aMCI), earlier than AD onset and later than subjective cognitive decline (SCD) onset, has a serious probability of converting into AD. The SCD, which can last for decades, subjectively complains of decline impairment in memory. Distinct altered patterns of default mode network (DMN) subnetworks connected to the whole brain are perceived as prominent hallmarks of the early stages of AD. Nevertheless, the aberrant phase position connectivity (PPC) connected to the whole brain in DMN subnetworks remains unknown. Here, we hypothesized that there exist distinct variations of PPC in DMN subnetworks connected to the whole brain for patients with SCD and aMCI, which might be acted as discriminatory neuroimaging biomarkers. We recruited 27 healthy controls (HC), 20 SCD and 28 aMCI subjects, respectively, to explore aberrant patterns of PPC in DMN subnetworks connected to the whole brain. In anterior DMN (aDMN), SCD group exhibited aberrant PPC in the regions of right superior cerebellum lobule (SCL), right superior frontal gyrus of medial part (SFGMP), and left fusiform gyrus (FG) in comparison of HC group, by contrast, no prominent difference was found in aMCI group. It is important to note that aMCI group showed increased PPC in the right SFGMP in comparison with SCD group. For posterior DMN (pDMN), SCD group showed decreased PPC in the left superior parietal lobule (SPL) and right superior frontal gyrus (SFG) compared to HC group. It is noteworthy that aMCI group showed decreased PPC in the left middle frontal gyrus of orbital part (MFGOP) and right SFG compared to HC group, yet increased PPC was found in the left superior temporal gyrus of temporal pole (STGTP). Additionally, aMCI group exhibited decreased PPC in the left MFGOP compared to SCD group. Collectively, our results have shown that the aberrant regions of PPC observed in DMN are related to cognitive function, and it might also be served as impressible neuroimaging biomarkers for timely intervention before AD occurs.
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Affiliation(s)
- Chunting Cai
- School of Informatics, Xiamen University, Xiamen, China
| | - Chenxi Huang
- School of Informatics, Xiamen University, Xiamen, China
| | - Chenhui Yang
- School of Informatics, Xiamen University, Xiamen, China
| | - Xiaodong Zhang
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Yonghong Peng
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
| | - Wenbing Zhao
- Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH, United States
| | - Xin Hong
- School of Informatics, Xiamen University, Xiamen, China
| | - Fujia Ren
- School of Informatics, Xiamen University, Xiamen, China
| | - Dan Hong
- School of Informatics, Xiamen University, Xiamen, China
| | - Yutian Xiao
- School of Informatics, Xiamen University, Xiamen, China
| | - Jiqiang Yan
- School of Informatics, Xiamen University, Xiamen, China
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21
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Namazi H, Aghasian E, Ala TS. Complexity-based classification of EEG signal in normal subjects and patients with epilepsy. Technol Health Care 2020; 28:57-66. [DOI: 10.3233/thc-181579] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | - Erfan Aghasian
- School of Engineering and ICT, University of Tasmania, Hobart, TAS, Australia
| | - Tirdad Seifi Ala
- Hearing Sciences (Scottish Section), Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Glasgow, UK
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22
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Benwell CSY, Davila-Pérez P, Fried PJ, Jones RN, Travison TG, Santarnecchi E, Pascual-Leone A, Shafi MM. EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer's disease and type 2 diabetes. Neurobiol Aging 2020; 85:83-95. [PMID: 31727363 PMCID: PMC6942171 DOI: 10.1016/j.neurobiolaging.2019.10.004] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 09/30/2019] [Accepted: 10/07/2019] [Indexed: 12/13/2022]
Abstract
Rhythmic neural activity has been proposed to play a fundamental role in cognition. Both healthy and pathological aging are characterized by frequency-specific changes in oscillatory activity. However, the cognitive relevance of these changes across the spectrum from normal to pathological aging remains unknown. We examined electroencephalography (EEG) correlates of cognitive function in healthy aging and 2 of the most prominent and debilitating age-related disorders: type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD). Relative to healthy controls (HC), patients with AD were impaired on nearly every cognitive measure, whereas patients with T2DM performed worse mainly on learning and memory tests. A continuum of alterations in resting-state EEG was associated with pathological aging, generally characterized by reduced alpha (α) and beta (β) power (AD < T2DM < HC) and increased delta (δ) and theta (θ) power (AD > T2DM > HC), with some variations across different brain regions. There were also reductions in the frequency and power density of the posterior dominant rhythm in AD. The ratio of (α + β)/(δ + θ) was specifically associated with cognitive function in a domain- and diagnosis-specific manner. The results thus captured both similarities and differences in the pathophysiology of cerebral oscillations in T2DM and AD. Overall, pathological brain aging is marked by a shift in oscillatory power from higher to lower frequencies, which can be captured by a single cognitively relevant measure of the ratio of (α + β) over (δ + θ) power.
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Affiliation(s)
- Christopher S Y Benwell
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Psychology, School of Social Sciences, University of Dundee, Dundee, UK.
| | - Paula Davila-Pérez
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Neuroscience and Motor Control Group (NEUROcom), Institute for Biomedical Research (INIBIC), Universidade da Coruña, A Coruña, Spain
| | - Peter J Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Richard N Jones
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Butler Hospital, Providence, RI, USA
| | - Thomas G Travison
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA; Institut Guttman, Universitat Autonoma de Barcelona, Badalona, Barcelona, Spain; Center for Memory Health, Hebrew Senior Life, Boston, MA, USA
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Comprehensive Epilepsy Center, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
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Menardi A, Pascual-Leone A, Fried PJ, Santarnecchi E. The Role of Cognitive Reserve in Alzheimer's Disease and Aging: A Multi-Modal Imaging Review. J Alzheimers Dis 2019; 66:1341-1362. [PMID: 30507572 DOI: 10.3233/jad-180549] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Comforts in modern society have generally been associated with longer survival rates, enabling individuals to reach advanced age as never before in history. With the increase in longevity, however, the incidence of neurodegenerative diseases, especially Alzheimer's disease, has also doubled. Nevertheless, most of the observed variance, in terms of time of clinical diagnosis and progression, often remains striking. Only recently, differences in the social, educational and occupational background of the individual, as proxies of cognitive reserve (CR), have been hypothesized to play a role in accounting for such discrepancies. CR is a well-established concept in literature; lots of studies have been conducted in trying to better understand its underlying neural substrates and associated biomarkers, resulting in an incredible amount of data being produced. Here, we aimed to summarize recent relevant published work addressing the issue, gathering evidence for the existence of a common path across research efforts that might ease future investigations by providing a general perspective on the actual state of the arts. An innovative model is hereby proposed, addressing the role of CR across structural and functional evidences, as well as the potential implementation of non-invasive brain stimulation techniques in the causal validation of such theoretical frame.
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Affiliation(s)
- Arianna Menardi
- Brain Investigation and Neuromodulation Lab, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Italy.,Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Peter J Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Emiliano Santarnecchi
- Brain Investigation and Neuromodulation Lab, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Italy.,Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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Musaeus CS, Engedal K, Høgh P, Jelic V, Mørup M, Naik M, Oeksengaard AR, Snaedal J, Wahlund LO, Waldemar G, Andersen BB. Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease. Clin Neurophysiol 2019; 130:1889-1899. [DOI: 10.1016/j.clinph.2019.07.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/26/2019] [Accepted: 07/03/2019] [Indexed: 12/27/2022]
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25
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Yu H, Zhu L, Cai L, Wang J, Liu C, Shi N, Liu J. Variation of functional brain connectivity in epileptic seizures: an EEG analysis with cross-frequency phase synchronization. Cogn Neurodyn 2019; 14:35-49. [PMID: 32015766 DOI: 10.1007/s11571-019-09551-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 07/22/2019] [Accepted: 08/02/2019] [Indexed: 11/26/2022] Open
Abstract
Frequency coupling in nervous system is believed to be associated with normal and impaired brain functions. However, most of the existing experiments have been concentrated on the coupling strength within frequency bands, while the coupling strength between different bands is ignored. In this work, we apply phase synchronization index (PSI) to investigate the cross-frequency coupling (CFC) of Electroencephalogram (EEG) signals. The PSI matrixes for the multi-channel EEG signals are calculated from interictal to ictal period in each sliding time window. The results show that CFC changes obviously once seizure occurs between the different bands, and such alteration is earlier than the appearance of clinical symptoms in seizure. Considering the similar role of the within-frequency coupling (WFC), we further reconstruct multi-layered brain networks, including CFC networks and WFC networks. The graph metrics are applied to investigate the variation of network structure of the epileptic brain. Significant decreases/increases of the local/global efficiency are found in δ-β, δ-α, θ-α and δ-θ bands from the CFC network, while WFC network shows a significant decline in the local efficiency in θ and α bands. These findings suggest that CFC may provide a new perspective to observe the alteration of brain structure when seizure occurs, and the investigation of functional connectivity across the full frequency spectrum can give us a deeper understanding of epileptic brains.
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Affiliation(s)
- Haitao Yu
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lin Zhu
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lihui Cai
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Chen Liu
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Nan Shi
- 2Department of Neurology, Tangshan Gongren Hospital, Tangshan, 063000 Hebei China
| | - Jing Liu
- 2Department of Neurology, Tangshan Gongren Hospital, Tangshan, 063000 Hebei China
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26
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Lo Giudice P, Mammone N, Morabito FC, Pizzimenti RG, Ursino D, Virgili L. Leveraging network analysis to support experts in their analyses of subjects with MCI and AD. Med Biol Eng Comput 2019; 57:1961-1983. [PMID: 31301007 DOI: 10.1007/s11517-019-02004-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 06/09/2019] [Indexed: 11/25/2022]
Abstract
In this paper, we propose a network analysis-based approach to help experts in their analyses of subjects with mild cognitive impairment (hereafter, MCI) and Alzheimer's disease (hereafter, AD) and to investigate the evolution of these subjects over time. The inputs of our approach are the electroencephalograms (hereafter, EEGs) of the patients to analyze, performed at a certain time and, again, 3 months later. Given an EEG of a subject, our approach constructs a network with nodes that represent the electrodes and edges that denote connections between electrodes. Then, it applies several network-based techniques allowing the investigation of subjects with MCI and AD and the analysis of their evolution over time. (i) A connection coefficient, supporting experts to distinguish patients with MCI from patients with AD; (ii) A conversion coefficient, supporting experts to verify if a subject with MCI is converting to AD; (iii) Some network motifs, i.e., network patterns very frequent in one kind of patient and absent, or very rare, in the other. Patients with AD, just by the very nature of their condition, cannot be forced to stay motionless while undergoing examinations for a long time. EEG is a non-invasive examination that can be easily done on them. Since AD and MCI, if prodromal to AD, are associated with a loss of cortical connections, the adoption of network analysis appears suitable to investigate the effects of the progression of the disease on EEG. This paper confirms the suitability of this idea Graphical Abstract Ability of our proposed model to distinguish a control subject from a patient with MCI and a patient with AD. Blue edges represent strong connections among the corresponding brain areas; red edges denote middle connections, whereas green edges indicate weak connections. In the control subject (at the top), most connections are blue. In the patient with MCI (at the middle), most connections are red and green. In the patient with AD (at the bottom), most connections are either absent or green. .
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Affiliation(s)
- Paolo Lo Giudice
- DIIES, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
| | - Nadia Mammone
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
| | | | | | | | - Luca Virgili
- DII, Polytechnic University of Marche, Ancona, Italy
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27
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Early Electrophysiological Disintegration of Hippocampal Neural Networks in a Novel Locus Coeruleus Tau-Seeding Mouse Model of Alzheimer's Disease. Neural Plast 2019; 2019:6981268. [PMID: 31285742 PMCID: PMC6594257 DOI: 10.1155/2019/6981268] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/19/2019] [Accepted: 04/30/2019] [Indexed: 01/31/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive, neurodegenerative disease characterized by loss of synapses and disrupted functional connectivity (FC) across different brain regions. Early in AD progression, tau pathology is found in the locus coeruleus (LC) prior to amyloid-induced exacerbation of clinical symptoms. Here, a tau-seeding model in which preformed synthetic tau fibrils (K18) were unilaterally injected into the LC of P301L mice, equipped with multichannel electrodes for recording EEG in frontal cortical and CA1-CA3 hippocampal areas, was used to longitudinally quantify over 20 weeks of functional network dynamics in (1) power spectra; (2) FC using intra- and intersite phase-amplitude theta-gamma coupling (PAC); (3) coherence, partial coherence, and global coherent network efficiency (Eglob) estimates; and (4) the directionality of functional connectivity using extended partial direct coherence (PDC). A sustained leftward shift in the theta peak frequency was found early in the power spectra of hippocampal CA1 networks ipsilateral to the injection site. Strikingly, hippocampal CA1 coherence and Eglob measures were impaired in K18-treated animals. Estimation of instantaneous EEG amplitudes revealed deficiency in the propagation directionality of gamma oscillations in the CA1 circuit. Impaired PAC strength evidenced by decreased modulation of the theta frequency phase on gamma frequency amplitude further confirms impairments of the neural CA1 network. The present results demonstrate early dysfunctional hippocampal networks, despite no spreading tau pathology to the hippocampus and frontal cortex. The ability of the K18 seed in the brainstem LC to elicit such robust functional alterations in distant hippocampal structures in the absence of pathology challenges the classic view that tau pathology spread to an area is necessary to elicit functional impairments in that area.
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28
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Tzimourta KD, Giannakeas N, Tzallas AT, Astrakas LG, Afrantou T, Ioannidis P, Grigoriadis N, Angelidis P, Tsalikakis DG, Tsipouras MG. EEG Window Length Evaluation for the Detection of Alzheimer's Disease over Different Brain Regions. Brain Sci 2019; 9:E81. [PMID: 31013964 PMCID: PMC6523667 DOI: 10.3390/brainsci9040081] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/10/2019] [Accepted: 04/10/2019] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Medical Physics, Medical School, University of Ioannina, GR45110 Ioannina, Greece.
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece.
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, GR47100 Arta, Greece.
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, GR45110 Ioannina, Greece.
| | - Theodora Afrantou
- 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece.
| | - Panagiotis Ioannidis
- 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece.
| | - Nikolaos Grigoriadis
- 2nd Department of Neurology, AHEPA University Hospital, Aristotle University of Thessaloniki, GR54636 Thessaloniki, Greece.
| | - Pantelis Angelidis
- Department of Informatics and Telecommunications Engineering, University of Western Macedonia, GR50100 Kozani, Greece.
| | - Dimitrios G Tsalikakis
- Department of Informatics and Telecommunications Engineering, University of Western Macedonia, GR50100 Kozani, Greece.
| | - Markos G Tsipouras
- Department of Informatics and Telecommunications Engineering, University of Western Macedonia, GR50100 Kozani, Greece.
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29
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Cai L, Wei X, Wang J, Yu H, Deng B, Wang R. Reconstruction of functional brain network in Alzheimer's disease via cross-frequency phase synchronization. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.07.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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30
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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. DISEASE MARKERS 2018; 2018:5174815. [PMID: 30405860 PMCID: PMC6200063 DOI: 10.1155/2018/5174815] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [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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31
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Fraga FJ, Mamani GQ, Johns E, Tavares G, Falk TH, Phillips NA. Early diagnosis of mild cognitive impairment and Alzheimer's with event-related potentials and event-related desynchronization in N-back working memory tasks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 164:1-13. [PMID: 30195417 DOI: 10.1016/j.cmpb.2018.06.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 05/24/2018] [Accepted: 06/14/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE In this study we investigate whether or not event-related potentials (ERP) and/or event-related (de)synchronization (ERD/ERS) can be used to differentiate between 27 healthy elderly (HE), 21 subjects diagnosed with mild cognitive impairment (MCI) and 15 mild Alzheimer's disease (AD) patients. METHODS Using 32-channel EEG recordings, we measured ERP responses to a three-level (N-back, N = 0,1,2) visual working memory task. We also performed ERD analysis over the same EEG data, dividing the full-band signal into the well-known delta, theta, alpha, beta and gamma bands. Both ERP and ERD analyses were followed by cluster analysis with correction for multicomparisons whenever significant differences were found between groups. RESULTS Regarding ERP (full-band analysis), our findings have shown both patient groups (MCI and AD) with reduced P450 amplitude (compared to HE controls) in the execution of the non-match 1-back task at many scalp electrodes, chiefly at parietal and centro-parietal areas. However, no significant differences were found between MCI and AD in ERP analysis whatever was the task. As for sub-band analyses, ERD/ERS measures revealed that HE subjects elicited consistently greater alpha ERD responses than MCI and AD patients during the 1-back task in the match condition, with all differences located at frontal, central and occipital regions. Moreover, in the non-match condition, it was possible to distinguish between MCI and AD patients when they were performing the 0-back task, with MCI presenting more desynchronization than AD on the theta band at temporal and fronto-temporal areas. In summary, ERD analyses have revealed themselves more valuable than ERP, since they showed significant differences in all three group comparisons: HE vs. MCI, HE vs. AD, and MCI vs. AD. CONCLUSIONS Based on these findings, we conclude that ERD responses to working memory (N-back) tasks could be useful not only for early MCI diagnosis or for improved AD diagnosis, but probably also for assessing the likelihood of MCI progression to AD, after further validated by a longitudinal study.
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Affiliation(s)
- Francisco J Fraga
- Engineering, Modelling and Applied Social Sciences Center, Universidade Federal do ABC, Santo André, São Paulo, Brazil.
| | - Godofredo Quispe Mamani
- Engineering, Modelling and Applied Social Sciences Center, Universidade Federal do ABC, Santo André, São Paulo, Brazil; Departamento de Estadística, Universidad Nacional del Altiplano, Puno, Peru
| | - Erin Johns
- Department of Psychology, Concordia University, Montreal, Quebec, Canada
| | - Guilherme Tavares
- Engineering, Modelling and Applied Social Sciences Center, Universidade Federal do ABC, Santo André, São Paulo, Brazil
| | - Tiago H Falk
- Institut National de la Recherche Scientifique (INRS-EMT), University of Quebec, Montreal, Quebec, Canada
| | - Natalie A Phillips
- Department of Psychology, Concordia University, Montreal, Quebec, Canada
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Poza J, Bachiller A, Gomez C, Garcia M, Nunez P, Gomez-Pilar J, Tola-Arribas MA, Cano M, Hornero R. Phase-amplitude coupling analysis of spontaneous EEG activity in Alzheimer's disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:2259-2262. [PMID: 29060347 DOI: 10.1109/embc.2017.8037305] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This study was aimed at exploring phase-amplitude coupling (PAC) patterns of neural activity in dementia due to Alzheimer's disease (AD). For this task, five minutes of spontaneous electroencephalographic (EEG) activity from 22 patients with mild AD and 16 cognitively healthy controls were studied. To assess PAC patterns, phase-locking value was computed between the phase of low frequencies and the power of high frequencies within each sensor. Our results showed that high-frequency gamma power is phase-locked to the alpha peak in EEG signals. Furthermore, statistically significant differences (p<;0.05, permutation test) between patients with mild AD and elderly controls were observed at the lower left temporo-parietal area, suggesting that early stages of AD elicit a region-specific decrease of PAC in the neural activity.
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Goodman MS, Kumar S, Zomorrodi R, Ghazala Z, Cheam ASM, Barr MS, Daskalakis ZJ, Blumberger DM, Fischer C, Flint A, Mah L, Herrmann N, Bowie CR, Mulsant BH, Rajji TK. Theta-Gamma Coupling and Working Memory in Alzheimer's Dementia and Mild Cognitive Impairment. Front Aging Neurosci 2018; 10:101. [PMID: 29713274 PMCID: PMC5911490 DOI: 10.3389/fnagi.2018.00101] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 03/26/2018] [Indexed: 01/09/2023] Open
Abstract
Working memory deficits are common among individuals with Alzheimer's dementia (AD) or mild cognitive impairment (MCI). Yet, little is known about the mechanisms underlying these deficits. Theta-gamma coupling-the modulation of high-frequency gamma oscillations by low-frequency theta oscillations-is a neurophysiologic process underlying working memory. We assessed the relationship between theta-gamma coupling and working memory deficits in AD and MCI. We hypothesized that: (1) individuals with AD would display the most significant working memory impairments followed by MCI and finally healthy control (HC) participants; and (2) there would be a significant association between working memory performance and theta-gamma coupling across all participants. Ninety-eight participants completed the N-back working memory task during an electroencephalography (EEG) recording: 33 with AD (mean ± SD age: 76.5 ± 6.2), 34 with MCI (mean ± SD age: 74.8 ± 5.9) and 31 HCs (mean ± SD age: 73.5 ± 5.2). AD participants performed significantly worse than control and MCI participants on the 1- and 2-back conditions. Regarding theta-gamma coupling, AD participants demonstrated the lowest level of coupling followed by the MCI and finally control participants on the 2-back condition. Finally, a linear regression analysis demonstrated that theta-gamma coupling (β = 0.69, p < 0.001) was the most significant predictor of 2-back performance. Our results provide evidence for a relationship between altered theta-gamma coupling and working memory deficits in individuals with AD and MCI. They also provide insight into a potential mechanism underlying working memory impairments in these individuals.
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Affiliation(s)
- Michelle S Goodman
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Sanjeev Kumar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Reza Zomorrodi
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Zaid Ghazala
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Amay S M Cheam
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mera S Barr
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Daniel M Blumberger
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Corinne Fischer
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Alastair Flint
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Linda Mah
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, ON, Canada
| | - Nathan Herrmann
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Christopher R Bowie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Tarek K Rajji
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Bazzigaluppi P, Beckett TL, Koletar MM, Lai AY, Joo IL, Brown ME, Carlen PL, McLaurin J, Stefanovic B. Early-stage attenuation of phase-amplitude coupling in the hippocampus and medial prefrontal cortex in a transgenic rat model of Alzheimer's disease. J Neurochem 2017; 144:669-679. [PMID: 28777881 DOI: 10.1111/jnc.14136] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 06/27/2017] [Accepted: 07/26/2017] [Indexed: 01/21/2023]
Abstract
Alzheimer's disease (AD) is pathologically characterized by amyloid-β peptide (Aβ) accumulation, neurofibrillary tangle formation, and neurodegeneration. Preclinical studies on neuronal impairments associated with progressive amyloidosis have demonstrated some Aβ-dependent neuronal dysfunction including modulation of gamma-aminobutyric acid-ergic signaling. The present work focuses on the early stage of disease progression and uses TgF344-AD rats that recapitulate a broad repertoire of AD-like pathologies to investigate the neuronal network functioning using simultaneous intracranial recordings from the hippocampus (HPC) and the medial prefrontal cortex (mPFC), followed by pathological analyses of gamma-aminobutyric acid (GABAA ) receptor subunits α1, α5, and δ, and glutamic acid decarboxylases (GAD65 and GAD67). Concomitant to amyloid deposition and tau hyperphosphorylation, low-gamma band power was strongly attenuated in the HPC and mPFC of TgF344-AD rats in comparison to those in non-transgenic littermates. In addition, the phase-amplitude coupling of the neuronal networks in both areas was impaired, evidenced by decreased modulation of theta band phase on gamma band amplitude in TgF344-AD animals. Finally, the gamma coherence between HPC and mPFC was attenuated as well. These results demonstrate significant neuronal network dysfunction at an early stage of AD-like pathology. This network dysfunction precedes the onset of cognitive deficits and is likely driven by Aβ and tau pathologies. This article is part of the Special Issue "Vascular Dementia".
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Affiliation(s)
- Paolo Bazzigaluppi
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Fundamental Neurobiology, Krembil Research Institute, Toronto, Ontario, Canada
| | - Tina L Beckett
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Margaret M Koletar
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Aaron Y Lai
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Illsung L Joo
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Mary E Brown
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Peter L Carlen
- Fundamental Neurobiology, Krembil Research Institute, Toronto, Ontario, Canada
| | - JoAnne McLaurin
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Biological Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Bojana Stefanovic
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Ontario, Canada
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Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F. Loss of brain inter-frequency hubs in Alzheimer's disease. Sci Rep 2017; 7:10879. [PMID: 28883408 PMCID: PMC5589939 DOI: 10.1038/s41598-017-07846-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 06/29/2017] [Indexed: 01/05/2023] Open
Abstract
Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-β deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.
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Affiliation(s)
- J Guillon
- Inria Paris, Aramis project-team, 75013, Paris, France
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France
| | - Y Attal
- MyBrain Technologies, Paris, France
| | - O Colliot
- Inria Paris, Aramis project-team, 75013, Paris, France
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France
| | - V La Corte
- Institute of Psychology, University Paris Descartes, Sorbonne Paris Cite, France
- INSERM UMR 894, Center of Psychiatry and Neurosciences, Memory and Cognition Laboratory, Paris, France
| | - B Dubois
- Department of Neurology, Institut de la Memoire et de la Maladie dAlzheimer - IM2A, Paris, France
| | - D Schwartz
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France
| | - M Chavez
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France
| | - F De Vico Fallani
- Inria Paris, Aramis project-team, 75013, Paris, France.
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France.
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D'Onofrio G, Sancarlo D, Ricciardi F, Panza F, Seripa D, Cavallo F, Giuliani F, Greco A. Information and Communication Technologies for the Activities of Daily Living in Older Patients with Dementia: A Systematic Review. J Alzheimers Dis 2017; 57:927-935. [PMID: 28304297 DOI: 10.3233/jad-161145] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Significant innovations have been introduced in recent years in the application of information and communication technologies (ICTs) to support healthcare for patients with dementia. OBJECTIVE In the present systematic review, our goal is to keep track of ICT concepts and approaches to support the range of activities of daily living for people with dementia and to provide a snapshot of the effect that technology is having on patients' self-reliance. METHODS We reviewed the literature and identified systematic reviews of cohort studies and other authoritative reports. Our selection criteria included: (1) activities of daily living, (2) ICT, and (3) dementia. RESULTS We identified 56 studies published between 2000 and 2015, of which 26 met inclusion criteria. The present systematic review revealed many ICT systems that could purportedly support the range of activities of daily living for patients with dementia. The results showed five research bodies: 1) technologies used by patients with dementia, 2) technologies used by caregivers, 3) monitoring systems, 4) ambient assistive living with ICTs, and 5) tracking and wayfinding. CONCLUSIONS There is a potential for ICTs to support dementia care at home and to improve quality of life for caregivers, reducing healthcare costs and premature institutional care for these patients.
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Affiliation(s)
- Grazia D'Onofrio
- Department of Medical Sciences, Geriatric Unit and Laboratory of Gerontology and Geriatrics, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy.,BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Daniele Sancarlo
- Department of Medical Sciences, Geriatric Unit and Laboratory of Gerontology and Geriatrics, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy
| | - Francesco Ricciardi
- ICT, Innovation & Research Unit, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy
| | - Francesco Panza
- Department of Medical Sciences, Geriatric Unit and Laboratory of Gerontology and Geriatrics, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy.,Department of Basic Medicine, Neurodegenerative Disease Unit, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy.,Department of Clinical Research in Neurology, University of Bari Aldo Moro, "Pia Fondazione Cardinale G. Panico", Tricase, Lecce, Italy
| | - Davide Seripa
- Department of Medical Sciences, Geriatric Unit and Laboratory of Gerontology and Geriatrics, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy
| | - Filippo Cavallo
- BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Francesco Giuliani
- ICT, Innovation & Research Unit, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy
| | - Antonio Greco
- Department of Medical Sciences, Geriatric Unit and Laboratory of Gerontology and Geriatrics, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy
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Ianof JN, Fraga FJ, Ferreira LA, Ramos RT, Demario JLC, Baratho R, Basile LFH, Nitrini R, Anghinah R. Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis. Dement Neuropsychol 2017; 11:176-185. [PMID: 29213509 PMCID: PMC5710686 DOI: 10.1590/1980-57642016dn11-020010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 05/24/2017] [Indexed: 12/23/2022] Open
Abstract
Alzheimer's disease (AD) is a dementia that affects a large contingent of the elderly population characterized by the presence of neurofibrillary tangles and senile plaques. Traumatic brain injury (TBI) is a non-degenerative injury caused by an external mechanical force. One of the main causes of TBI is diffuse axonal injury (DAI), promoted by acceleration-deceleration mechanisms. OBJECTIVE To understand the electroencephalographic differences in functional mechanisms between AD and DAI groups. METHODS The study included 20 subjects with AD, 19 with DAI and 17 healthy adults submitted to high resolution EEG with 128 channels. Cortical sources of EEG rhythms were estimated by exact low-resolution electromagnetic tomography (eLORETA) analysis. RESULTS The eLORETA analysis showed that, in comparison to the control (CTL) group, the AD group had increased theta activity in the parietal and frontal lobes and decreased alpha 2 activity in the parietal, frontal, limbic and occipital lobes. In comparison to the CTL group, the DAI group had increased theta activity in the limbic, occipital sublobar and temporal areas. CONCLUSION The results suggest that individuals with AD and DAI have impairment of electrical activity in areas important for memory and learning.
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Affiliation(s)
- Jéssica Natuline Ianof
- Neurology Department, University of São Paulo Medical School
Hospital (FMUSP-HC), São Paulo, SP, Brazil
| | - Francisco José Fraga
- Engineering, Modeling and Applied Social Sciences Center (CECS) -
Federal University of ABC (UFABC), São Paulo, SP, Brazil
| | - Leonardo Alves Ferreira
- Engineering, Modeling and Applied Social Sciences Center (CECS) -
Federal University of ABC (UFABC), São Paulo, SP, Brazil
| | | | - José Luiz Carlos Demario
- Department of Actuarial and Quantitative Methods - Pontifical
Catholic of São Paulo, São Paulo, SP, Brazil
| | - Regina Baratho
- Department of Actuarial and Quantitative Methods - Pontifical
Catholic of São Paulo, São Paulo, SP, Brazil
| | - Luís Fernando Hindi Basile
- Neurology Department, University of São Paulo Medical School
Hospital (FMUSP-HC), São Paulo, SP, Brazil
- Laboratory of Psychophysiology - Methodist University of São
Paulo, São Paulo, SP, Brazil
| | - Ricardo Nitrini
- Neurology Department, University of São Paulo Medical School
Hospital (FMUSP-HC), São Paulo, SP, Brazil
| | - Renato Anghinah
- Neurology Department, University of São Paulo Medical School
Hospital (FMUSP-HC), São Paulo, SP, Brazil
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38
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Cassani R, Falk TH, Fraga FJ, Cecchi M, Moore DK, Anghinah R. Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.12.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Kanda PAM, Oliveira EF, Fraga FJ. EEG epochs with less alpha rhythm improve discrimination of mild Alzheimer's. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 138:13-22. [PMID: 27886711 DOI: 10.1016/j.cmpb.2016.09.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Revised: 09/01/2016] [Accepted: 09/23/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Eyes-closed-awake electroencephalogram (EEG) is a useful tool in the diagnosis of Alzheimer's. However, there is eyes-closed-awake EEG with dominant or rare alpha rhythm. In this paper, we show that random selection of EEG epochs disregarding the alpha rhythm will lead to bias concerning EEG-based Alzheimer's Disease diagnosis. METHODS We compared EEG epochs with more than 30% and with less than 30% alpha rhythm of mild Alzheimer's Disease patients and healthy elderly. We classified epochs as dominant alpha scenario and rare alpha scenario according to alpha rhythm (8-13 Hz) percentage in O1, O2 and Oz channels. Accordingly, we divided the probands into four groups: 17 dominant alpha scenario controls, 15 mild Alzheimer's patients with dominant alpha scenario epochs, 12 rare alpha scenario healthy elderly and 15 mild Alzheimer's Disease patients with rare alpha scenario epochs. We looked for group differences using one-way ANOVA tests followed by post-hoc multiple comparisons (p < 0.05) over normalized energy values (%) on the other four well-known frequency bands (delta, theta, beta and gamma) using two different electrode configurations (parieto-occipital and central). RESULTS After carrying out post-hoc multiple comparisons, for both electrode configurations we found significant differences between mild Alzheimer's patients and healthy elderly on beta- and theta-energy (%) only for the rare alpha scenario. No differences were found for the dominant alpha scenario in any of the five frequency bands. CONCLUSIONS This is the first study of Alzheimer's awake-EEG reporting the influence of alpha rhythm on epoch selection, where our results revealed that, contrarily to what was most likely expected, less synchronized EEG epochs (rare alpha scenario) better discriminated mild Alzheimer's than those presenting abundant alpha (dominant alpha scenario). In addition, we find out that epoch selection is a very sensitive issue in qEEG research. Consequently, for Alzheimer's studies dealing with resting state EEG, we propose that epoch selection strategies should always be cautiously designed and thoroughly explained.
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Affiliation(s)
| | - Eliezyer F Oliveira
- CECS - Engineering, Modelling and Applied Social Sciences Center, UFABC - Universidade Federal do ABC, Santo André, SP, Brazil
| | - Francisco J Fraga
- CECS - Engineering, Modelling and Applied Social Sciences Center, UFABC - Universidade Federal do ABC, Santo André, SP, Brazil.
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40
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Neto E, Biessmann F, Aurlien H, Nordby H, Eichele T. Regularized Linear Discriminant Analysis of EEG Features in Dementia Patients. Front Aging Neurosci 2016; 8:273. [PMID: 27965568 PMCID: PMC5127828 DOI: 10.3389/fnagi.2016.00273] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Accepted: 10/31/2016] [Indexed: 10/24/2022] Open
Abstract
The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer's disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10-fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD, and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features, we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC = 0.84). We also obtained AUC = 0.74 for discrimination of AD from HC, AUC = 0.77 for discrimination of VaD from HC, and finally AUC = 0.61 for discrimination of AD from VaD. Our models were able to separate HC from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia.
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Affiliation(s)
- Emanuel Neto
- Section for Clinical Neurophysiology, Haukeland University HospitalBergen, Norway; Institute of Biological and Medical Psychology, University of BergenBergen, Norway
| | | | - Harald Aurlien
- Section for Clinical Neurophysiology, Haukeland University Hospital Bergen, Norway
| | - Helge Nordby
- Institute of Biological and Medical Psychology, University of Bergen Bergen, Norway
| | - Tom Eichele
- Section for Clinical Neurophysiology, Haukeland University HospitalBergen, Norway; Institute of Biological and Medical Psychology, University of BergenBergen, Norway; K.G. Jebsen Center for Neuropsychiatric DisordersBergen, Norway
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41
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Chen C, Zhou C, Cavanaugh JM, Kallakuri S, Desai A, Zhang L, King AI. Quantitative electroencephalography in a swine model of blast-induced brain injury. Brain Inj 2016; 31:120-126. [PMID: 27830938 DOI: 10.1080/02699052.2016.1216603] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
OBJECTIVE Electroencephalography (EEG) was used to examine brain activity abnormalities earlier after blast exposure using a swine model to develop a qEEG data analysis protocol. METHODS Anaesthetized swine were exposed to 420-450 Kpa blast overpressure and survived for 3 days after blast. EEG recordings were performed at 15 minutes before the blast and 15 minutes, 30 minutes, 2 hours and 1, 2 and 3 days post-blast using surface recording electrodes and a Biopac 4-channel data acquisition system. Off-line quantitative EEG (qEEG) data analysis was performed to determine qEEG changes. RESULTS Blast induced qEEG changes earlier after blast exposure, including a decrease of mean amplitude (MAMP), an increase of delta band power, a decrease of alpha band root mean square (RMS) and a decrease of 90% spectral edge frequency (SEF90). CONCLUSIONS This study demonstrated that qEEG is sensitive for cerebral injury. The changes of qEEG earlier after the blast indicate the potential of utilization of multiple parameters of qEEG for diagnosis of blast-induced brain injury. Early detection of blast induced brain injury will allow early screening and assessment of brain abnormalities in soldiers to enable timely therapeutic intervention.
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Affiliation(s)
- Chaoyang Chen
- a Department of Biomedical Engineering , Wayne State University , Detroit , MI , USA
| | - Chengpeng Zhou
- a Department of Biomedical Engineering , Wayne State University , Detroit , MI , USA
| | - John M Cavanaugh
- a Department of Biomedical Engineering , Wayne State University , Detroit , MI , USA
| | - Srinivasu Kallakuri
- a Department of Biomedical Engineering , Wayne State University , Detroit , MI , USA
| | - Alok Desai
- a Department of Biomedical Engineering , Wayne State University , Detroit , MI , USA
| | - Liying Zhang
- a Department of Biomedical Engineering , Wayne State University , Detroit , MI , USA
| | - Albert I King
- a Department of Biomedical Engineering , Wayne State University , Detroit , MI , USA
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Hazra A, Corbett BF, You JC, Aschmies S, Zhao L, Li K, Lepore AC, Marsh ED, Chin J. Corticothalamic network dysfunction and behavioral deficits in a mouse model of Alzheimer's disease. Neurobiol Aging 2016; 44:96-107. [PMID: 27318137 DOI: 10.1016/j.neurobiolaging.2016.04.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 04/21/2016] [Accepted: 04/22/2016] [Indexed: 10/21/2022]
Abstract
Alzheimer's disease is associated with cognitive decline and seizures. Growing evidence indicates that seizures contribute to cognitive deficits early in disease, but how they develop and impact cognition are unclear. To investigate potential mechanisms, we studied a mouse model that overexpresses mutant human amyloid precursor protein with high levels of amyloid beta (Aβ). These mice develop generalized epileptiform activity, including nonconvulsive seizures, consistent with alterations in corticothalamic network activity. Amyloid precursor protein mice exhibited reduced activity marker expression in the reticular thalamic nucleus, a key inhibitory regulatory nucleus, and increased activity marker expression in downstream thalamic relay targets that project to cortex and limbic structures. Slice recordings revealed impaired cortical inputs to the reticular thalamic nucleus that may contribute to corticothalamic dysfunction. These results are consistent with our findings of impaired sleep maintenance in amyloid precursor protein mice. Finally, the severity of sleep impairments predicted the severity of deficits in Morris water maze, suggesting corticothalamic dysfunction may relate to hippocampal dysfunction, and may be a pathophysiological mechanism underlying multiple behavioral and cognitive alterations in Alzheimer's disease.
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Affiliation(s)
- Anupam Hazra
- Department of Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107.,Farber Institute for Neurosciences, Thomas Jefferson University, Philadelphia, PA 19107
| | - Brian F Corbett
- Department of Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107.,Farber Institute for Neurosciences, Thomas Jefferson University, Philadelphia, PA 19107
| | - Jason C You
- Department of Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107.,Farber Institute for Neurosciences, Thomas Jefferson University, Philadelphia, PA 19107
| | - Suzan Aschmies
- Department of Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107.,Farber Institute for Neurosciences, Thomas Jefferson University, Philadelphia, PA 19107
| | - Lijuan Zhao
- Department of Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107.,Farber Institute for Neurosciences, Thomas Jefferson University, Philadelphia, PA 19107
| | - Ke Li
- Department of Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107.,Farber Institute for Neurosciences, Thomas Jefferson University, Philadelphia, PA 19107
| | - Angelo C Lepore
- Department of Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107.,Farber Institute for Neurosciences, Thomas Jefferson University, Philadelphia, PA 19107
| | - Eric D Marsh
- Departments of Pediatrics and Neurology, Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104
| | - Jeannie Chin
- Department of Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107.,Farber Institute for Neurosciences, Thomas Jefferson University, Philadelphia, PA 19107.,Memory & Brain Research Center, Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
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43
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Dimitriadis SI, Laskaris NA, Bitzidou MP, Tarnanas I, Tsolaki MN. A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Front Neurosci 2015; 9:350. [PMID: 26539070 PMCID: PMC4611062 DOI: 10.3389/fnins.2015.00350] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Accepted: 09/14/2015] [Indexed: 11/13/2022] Open
Abstract
The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis.
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Affiliation(s)
- Stavros I Dimitriadis
- Artificial Intelligence Information Analysis Lab, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece ; Neuroinformatics Group, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Nikolaos A Laskaris
- Artificial Intelligence Information Analysis Lab, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece ; Neuroinformatics Group, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Malamati P Bitzidou
- Artificial Intelligence Information Analysis Lab, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Ioannis Tarnanas
- Health-IS Lab, Chair of Information Management, ETH Zurich Zurich, Switzerland ; 3rd Department of Neurology, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Magda N Tsolaki
- 3rd Department of Neurology, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
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44
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Neto E, Allen EA, Aurlien H, Nordby H, Eichele T. EEG Spectral Features Discriminate between Alzheimer's and Vascular Dementia. Front Neurol 2015; 6:25. [PMID: 25762978 PMCID: PMC4327579 DOI: 10.3389/fneur.2015.00025] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2014] [Accepted: 01/29/2015] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.
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Affiliation(s)
- Emanuel Neto
- Institute of Biological and Medical Psychology, University of Bergen , Bergen , Norway ; Section for Clinical Neurophysiology, Haukeland University Hospital , Bergen , Norway
| | - Elena A Allen
- Institute of Biological and Medical Psychology, University of Bergen , Bergen , Norway ; K. G. Jebsen Center for Research on Neuropsychiatric Disorders , Bergen , Norway ; The Mind Research Network , Albuquerque, NM , USA
| | - Harald Aurlien
- Section for Clinical Neurophysiology, Haukeland University Hospital , Bergen , Norway
| | - Helge Nordby
- Institute of Biological and Medical Psychology, University of Bergen , Bergen , Norway
| | - Tom Eichele
- Institute of Biological and Medical Psychology, University of Bergen , Bergen , Norway ; Section for Clinical Neurophysiology, Haukeland University Hospital , Bergen , Norway ; K. G. Jebsen Center for Research on Neuropsychiatric Disorders , Bergen , Norway
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45
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Power spectral density and coherence analysis of Alzheimer's EEG. Cogn Neurodyn 2014; 9:291-304. [PMID: 25972978 DOI: 10.1007/s11571-014-9325-x] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Revised: 12/03/2014] [Accepted: 12/10/2014] [Indexed: 10/24/2022] Open
Abstract
In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.
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46
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Wang R, Wang J, Yu H, Wei X, Yang C, Deng B. Decreased coherence and functional connectivity of electroencephalograph in Alzheimer's disease. CHAOS (WOODBURY, N.Y.) 2014; 24:033136. [PMID: 25273216 DOI: 10.1063/1.4896095] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. Coherence is introduced to measure the pair-wise normalized linear synchrony and functional correlations between two EEG signals in different frequency domains, and graph analysis is further used to investigate the influence of AD on the functional connectivity of human brain. Data analysis results show that, compared with the control group, the pair-wise coherence of AD group is significantly decreased, especially for the theta and alpha frequency bands in the frontal and parieto-occipital regions. Furthermore, functional connectivity among different brain regions is reconstructed based on EEG, which exhibit obvious small-world properties. Graph analysis demonstrates that the local functional connections between regions for AD decrease. In addition, it is found that small-world properties of AD networks are largely weakened, by calculating its average path lengths, clustering coefficients, global efficiency, local efficiency, and small-worldness. The obtained results show that both pair-wise coherence and functional network can be taken as effective measures to distinguish AD patients from the normal, which may benefit our understanding of the disease.
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Affiliation(s)
- Ruofan Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Haitao Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Xile Wei
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Chen Yang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
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47
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Cassani R, Falk TH, Fraga FJ, Kanda PAM, Anghinah R. The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis. Front Aging Neurosci 2014; 6:55. [PMID: 24723886 PMCID: PMC3971195 DOI: 10.3389/fnagi.2014.00055] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Accepted: 03/06/2014] [Indexed: 11/13/2022] Open
Abstract
Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.
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Affiliation(s)
- Raymundo Cassani
- Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, University of Quebec Montreal, QC, Canada
| | - Tiago H Falk
- Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, University of Quebec Montreal, QC, Canada
| | - Francisco J Fraga
- Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, University of Quebec Montreal, QC, Canada ; Engineering, Modelling and Applied Social Sciences Center, Universidade Federal do ABC São Paulo, Brazil
| | - Paulo A M Kanda
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, Universidade de São Paulo São Paulo, Brazil
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, Universidade de São Paulo São Paulo, Brazil
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