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Simmatis LER, Russo EE, Altug Y, Murugathas V, Janevski J, Oh D, Chiu Q, Harmsen IE, Samuel N. Towards discovery and implementation of neurophysiologic biomarkers of Alzheimer's disease using entropy methods. Neuroscience 2024; 558:105-113. [PMID: 39163898 DOI: 10.1016/j.neuroscience.2024.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 07/23/2024] [Accepted: 08/11/2024] [Indexed: 08/22/2024]
Abstract
Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disease that leads to substantial loss of quality of life. Therapies currently available for AD do not modify the disease course and have limited efficacy in symptom control. As such, novel and precise therapies tailored to individual patients' neurophysiologic profiles are needed. Functional neuroimaging tools have demonstrated substantial potential to provide quantifiable insight into brain function in various neurologic disorders, particularly AD. Entropy, a novel analysis for better understanding the nonlinear nature of neurophysiological data, has demonstrated consistent accuracy in disease detection. This literature review characterizes the use of entropy-based analyses from functional neuroimaging tools, including electroencephalography (EEG) and magnetoencephalography (MEG), in patients with AD for disease detection, therapeutic response measurement, and providing clinical insights.
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Affiliation(s)
- Leif E R Simmatis
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Cove Neurosciences Inc., Toronto, Ontario, Canada
| | - Emma E Russo
- Cove Neurosciences Inc., Toronto, Ontario, Canada
| | | | - Vijairam Murugathas
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Cove Neurosciences Inc., Toronto, Ontario, Canada
| | - Josh Janevski
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Cove Neurosciences Inc., Toronto, Ontario, Canada
| | - Donghun Oh
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Cove Neurosciences Inc., Toronto, Ontario, Canada
| | - Queenny Chiu
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Cove Neurosciences Inc., Toronto, Ontario, Canada
| | - Irene E Harmsen
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Cove Neurosciences Inc., Toronto, Ontario, Canada
| | - Nardin Samuel
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Cove Neurosciences Inc., Toronto, Ontario, Canada.
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Kachare P, Puri D, Sangle SB, Al-Shourbaji I, Jabbari A, Kirner R, Alameen A, Migdady H, Abualigah L. LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection. Phys Eng Sci Med 2024; 47:1037-1050. [PMID: 38862778 DOI: 10.1007/s13246-024-01425-w] [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: 11/26/2023] [Accepted: 04/10/2024] [Indexed: 06/13/2024]
Abstract
Alzheimer's disease (AD) is a progressive and incurable neurologi-cal disorder with a rising mortality rate, worsened by error-prone, time-intensive, and expensive clinical diagnosis methods. Automatic AD detection methods using hand-crafted Electroencephalogram (EEG) signal features lack accuracy and reliability. A lightweight convolution neural network for AD detection (LCADNet) is investigated to extract disease-specific features while reducing the detection time. The LCADNet uses two convolutional layers for extracting complex EEG features, two fully connected layers for selecting disease-specific features, and a softmax layer for predicting AD detection probability. A max-pooling layer interlaced between convolutional layers decreases the time-domain redundancy in the EEG signal. The efficiency of the LCADNet and four pre-trained models using transfer learning is compared using a publicly available AD detection dataset. The LCADNet shows the lowest computation complexity in terms of both the number of floating point operations and inference time and the highest classification performance across six measures. The generalization of the LCADNet is assessed by cross-testing it with two other publicly available AD detection datasets. It outperforms existing EEG-based AD detection methods with an accuracy of 98.50%. The LCADNet may be a valuable aid for neurologists and its Python implemen- tation can be found at github.com/SandeepSangle12/LCADNet.git.
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Affiliation(s)
- Pramod Kachare
- Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India
| | - Digambar Puri
- Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India
| | - Sandeep B Sangle
- Department of Electronics and Telecommunication, Ramrao Adik Institute of Technology, D. Y. Patil Campus, Navi-Mumbai, Maharashtra, 400706, India
| | - Ibrahim Al-Shourbaji
- Department of Electrical and Electronics Engineering, Jazan University, Jazan, 45142, Saudi Arabia
- Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Abdoh Jabbari
- Department of Electrical and Electronics Engineering, Jazan University, Jazan, 45142, Saudi Arabia
| | - Raimund Kirner
- Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Abdalla Alameen
- Department of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir, 11991, Saudi Arabia
| | - Hazem Migdady
- CSMIS Department, Oman College of Management and Technology, 320, Barka, Oman
| | - Laith Abualigah
- Jadara Research Center, Jadara University, Irbid, 21110, Jordan.
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan.
- MEU Research Unit, Middle East University, Amman, 11831, Jordan.
- Applied science research center, Applied science private university, Amman, 11931, Jordan.
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Şeker M, Özerdem MS. Dementia rhythms: Unveiling the EEG dynamics for MCI detection through spectral and synchrony neuromarkers. J Neurosci Methods 2024; 409:110216. [PMID: 38964474 DOI: 10.1016/j.jneumeth.2024.110216] [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: 02/01/2024] [Revised: 06/20/2024] [Accepted: 06/29/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND Neurological disorders arise primarily from the dysfunction of brain cells, leading to various impairments. Electroencephalography (EEG) stands out as the most popular method in the discovery of neuromarkers indicating neurological disorders. The proposed study investigates the effectiveness of spectral and synchrony neuromarkers derived from resting state EEG in the detection of Mild Cognitive Impairment (MCI) with controls. NEW METHODS The dataset is composed of 10 MCI and 10 HC groups. Spectral features and synchrony measures are utilized to detect slowing patterns in MCI. Efficient neuro-markers are classified by 25 classification algorithm. Independent samples t-test and Pearson's Correlation Coefficients are applied to reveal group differences for spectral markers, and repeated measures ANOVA is tested for wPLI-based markers. RESULTS Lower peak amplitudes are prominent in MCI participants for high frequencies indicating slower physiological behavior of the demented EEG. The MCI and HC groups are correctly classified with 95 % acc. using peak amplitudes of beta band with LGBM classifier. Higher wPLI values are calculated for HC participants in high frequencies. The alpha wPLI values achieve a classification accuracy of 99 % using the LGBM algorithm for MCI detection. COMPARISON WITH EXISTING METHODS The neuro-markers including peak amplitudes, frequencies, and wPLIs with advanced machine learning techniques showcases the innovative nature of this research. CONCLUSION The findings suggest that peak amplitudes and wPLI in high frequency bands derived from resting state EEG are effective neuromarkers for detection of MCI. Spectral and synchrony neuro-markers hold great promise for accurate MCI detection.
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Affiliation(s)
- Mesut Şeker
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
| | - Mehmet Siraç Özerdem
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
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Trinh TT, Liu YH, Wu CT, Peng WH, Hou CL, Weng CH, Lee CY. PLI-Based Connectivity in Resting-EEG is a Robust and Generalizable Feature for Detecting MCI and AD: A Validation on a Diverse Multisite Clinical Dataset. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083569 DOI: 10.1109/embc40787.2023.10340854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The high prevalence rate of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has been a serious public health threat to the modern society. Recently, many studies have demonstrated the potential of using non-invasive electroencephalography (EEG) and machine learning to assist the diagnosis of AD/MCI. However, the majority of these research recorded EEG signals from a single center, leading to significant concerns regarding the generalizability of the findings in clinical settings. The current study aims to reevaluate the effectiveness of EEG-based machine learning model for the detection of AD/MCI in the case of a relatively large and diverse data set. We collected resting-state EEG data from 150 participants across six hospitals and examined the classification performances of Linear Discriminative Analysis (LDA) classifiers on the phase lag index (PLI) feature. We also compared the performance of PLI over the other commonly-used EEG features and other classifiers. The model was first tested on a training set to select the feature subset and then further validated with an independent test set. The results demonstrate that PLI performs the best compared to other features. The LDA classifier trained with the optimal PLI features can provide 82.50% leave-one-participant-out cross-validation (LOPO-CV) accuracy on the training set and maintain a good enough performance with 75.00% accuracy on the test set. Our results suggest that PLI-based functional connectivity could be considered as a reliable bio-maker to detect AD/MCI in the real-world clinical settings.
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Zheng X, Yu X, Yin Y, Li T, Yan X. Three‐dimensional feature maps and convolutional neural network‐based emotion recognition. INT J INTELL SYST 2021. [DOI: 10.1002/int.22551] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Xiangwei Zheng
- School of Information Science and Engineering Shandong Normal University Jinan China
- State Key Laboratory of High‐end Server & Storage Technology Jinan China
| | - Xiaomei Yu
- School of Information Science and Engineering Shandong Normal University Jinan China
- State Key Laboratory of High‐end Server & Storage Technology Jinan China
| | - Yongqiang Yin
- School of Information Science and Engineering Shandong Normal University Jinan China
- State Key Laboratory of High‐end Server & Storage Technology Jinan China
| | - Tiantian Li
- Faculty of Education Shandong Normal University Jinan China
| | - Xiaoyan Yan
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan China
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He M, Liu F, Nummenmaa A, Hämäläinen M, Dickerson BC, Purdon PL. Age-Related EEG Power Reductions Cannot Be Explained by Changes of the Conductivity Distribution in the Head Due to Brain Atrophy. Front Aging Neurosci 2021; 13:632310. [PMID: 33679380 PMCID: PMC7929986 DOI: 10.3389/fnagi.2021.632310] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 01/18/2021] [Indexed: 11/28/2022] Open
Abstract
Electroencephalogram (EEG) power reductions in the aging brain have been described by numerous previous studies. However, the underlying mechanism for the observed brain signal power reduction remains unclear. One possible cause for reduced EEG signals in elderly subjects might be the increased distance from the primary neural electrical currents on the cortex to the scalp electrodes as the result of cortical atrophies. While brain shrinkage itself reflects age-related neurological changes, the effects of changes in the distribution of electrical conductivity are often not distinguished from altered neural activity when interpreting EEG power reductions. To address this ambiguity, we employed EEG forward models to investigate whether brain shrinkage is a major factor for the signal attenuation in the aging brain. We simulated brain shrinkage in spherical and realistic brain models and found that changes in the conductor geometry cannot fully account for the EEG power reductions even when the brain was shrunk to unrealistic sizes. Our results quantify the extent of power reductions from brain shrinkage and pave the way for more accurate inferences about deficient neural activity and circuit integrity based on EEG power reductions in the aging population.
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Affiliation(s)
- Mingjian He
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States
| | - Feng Liu
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Bradford C Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.,Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Patrick L Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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