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Holston EC. An Integrative Review about Electrophysiological Biomarkers of Cognitive Impairment in Alzheimer's Disease: A Developing Relationship. Issues Ment Health Nurs 2024; 45:746-757. [PMID: 38954497 DOI: 10.1080/01612840.2024.2352011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Background: Electrophysiological biomarkers are being examined as potential diagnostic measures of cognitive impairment and its manifestations for psychiatric nurses' use in the care of Alzheimer's disease (AD). However, there is no integrative review describing the themes from the current research about electrophysiological biomarkers and the developing relationship among the themes. Characterizing this developing relationship is imperative for any possible integration of biomarkers into the care of AD by psychiatric nurses. Objective: The purpose of this integrative review is to identify themes from the current research about electrophysiological biomarkers of AD and the developing relationship among the themes, the conceivable relational premise for psychiatric nurses to integrate electrophysiological biomarkers into the screening, assessment, diagnosis, and treatment of AD for the care of persons with AD. Methods: A literature search was executed with PUBMED (accessing Medline and Elsevier) and CINAHL databases that focused on studies about electrophysiological biomarkers of AD from 2015 to 2022. Twenty-seven peer-reviewed studies met this review's inclusion criteria. Results: Five themes emerged: (1) assessing/screening, (2) assessment differential, (3) diagnosing, (4) diagnostic accuracy, and (5) treating. These themes related sequentially and linearly, establishing a developing relationship about the risk, the onset, and the progression of AD. Discussion: Electrophysiological biomarkers associated to cognitive impairment in AD, supporting the accepted understanding of the symptoms of AD. Changes in behavior and functioning were not examined, limiting the possible integration of electrophysiological biomarkers. Further investigations are warranted with an expansion of the clinical symptoms and diverse study populations.
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Affiliation(s)
- Ezra C Holston
- Orvis School of Nursing, University of Nevada Reno, Reno, Nevada, USA
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2
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Zhou H, Yin L, Su R, Zhang Y, Yuan Y, Xie P, Li X. STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108123. [PMID: 38471292 DOI: 10.1016/j.cmpb.2024.108123] [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: 09/19/2023] [Revised: 11/28/2023] [Accepted: 03/07/2024] [Indexed: 03/14/2024]
Abstract
BACKGROUND AND OBJECTIVE Early diagnosis of mild cognitive impairment (MCI) is one of the essential measures to prevent its further development into Alzheimer's disease (AD). In this paper, we propose a hybrid deep learning model for early diagnosis of MCI, called spatio-temporal convolutional gated recurrent unit network (STCGRU). METHODS The STCGRU comprises three bespoke convolutional neural network (CNN) modules and a bi-directional gated recurrent unit (BiGRU) module, which can effectively extract the spatial and temporal features of EEG and obtain excellent diagnostic results. We use a publicly available EEG dataset that has not undergone pre-processing to verify the robustness and accuracy of the model. Ablation experiments on STCGRU are conducted to showcase the individual performance improvement of each module. RESULTS Compared with other state-of-the-art approaches using the same publicly available EEG dataset, the results show that STCGRU is more suitable for early diagnosis of MCI. After 10-fold cross-validation, the average classification accuracy of the hybrid model reached 99.95 %, while the average kappa value reached 0.9989. CONCLUSIONS The experimental results show that the hybrid model proposed in this paper can directly extract compelling spatio-temporal features from the raw EEG data for classification. The STCGRU allows for accurate diagnosis of patients with MCI and has a high practical value.
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Affiliation(s)
- Hao Zhou
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Liyong Yin
- The First Hospital of Qinhuangdao, Qinhuangdao, PR China
| | - Rui Su
- Hebei Medical University, Shijiazhuang, PR China
| | - Ying Zhang
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Yi Yuan
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Ping Xie
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Xin Li
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China.
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AlSharabi K, Salamah YB, Aljalal M, Abdurraqeeb AM, Alturki FA. EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques. Front Hum Neurosci 2023; 17:1190203. [PMID: 37719771 PMCID: PMC10501399 DOI: 10.3389/fnhum.2023.1190203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 08/14/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Despite the existence of numerous clinical techniques for identifying neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows great promise as a means of detecting Alzheimer's disease (AD) at an early stage. The main goal of this research is to create a reliable and accurate clinical decision support system leveraging EEG signal processing to detect AD in its initial phases. Methods The research utilized a dataset consisting of 35 neurotypical individuals, 31 patients with mild AD, and 22 patients with moderate AD. Data were collected while participants were at rest. To extract features from the EEG signals, a band-pass filter was applied to the dataset and the Empirical Mode Decomposition (EMD) technique was employed to decompose the filtered signals. The EMD technique was then leveraged to generate feature vectors by combining multiple signal features, thereby enhancing diagnostic performance. Various artificial intelligence approaches were also explored and compared to identify features of the extracted EEG signals distinguishing mild AD, moderate AD, and neurotypical cases. The performance of the classifiers was evaluated using k-fold cross-validation and leave-one-subject-out (LOSO) cross-validation methods. Results The results of this study provided valuable insights into potential avenues for the early diagnosis of AD. The performance of the various offered methodologies has been compared and evaluated by computing the overall diagnosis precision, recall, and accuracy. The proposed methodologies achieved a maximum classification accuracy of 99.9 and 94.8% for k-fold and LOSO cross-validation techniques, respectively. Conclusion The study aims to assess and compare different proposed methodologies and determine the most effective combination approach for the early detection of AD. Our research findings strongly suggest that the proposed diagnostic support technique is a highly promising supplementary tool for discovering prospective diagnostic biomarkers that can greatly aid in the early clinical diagnosis of AD.
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Affiliation(s)
- Khalil AlSharabi
- Electrical Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia
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Plaza-Rosales I, Brunetti E, Montefusco-Siegmund R, Madariaga S, Hafelin R, Ponce DP, Behrens MI, Maldonado PE, Paula-Lima A. Visual-spatial processing impairment in the occipital-frontal connectivity network at early stages of Alzheimer's disease. Front Aging Neurosci 2023; 15:1097577. [PMID: 36845655 PMCID: PMC9947357 DOI: 10.3389/fnagi.2023.1097577] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/20/2023] [Indexed: 02/11/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is the leading cause of dementia worldwide, but its pathophysiological phenomena are not fully elucidated. Many neurophysiological markers have been suggested to identify early cognitive impairments of AD. However, the diagnosis of this disease remains a challenge for specialists. In the present cross-sectional study, our objective was to evaluate the manifestations and mechanisms underlying visual-spatial deficits at the early stages of AD. Methods We combined behavioral, electroencephalography (EEG), and eye movement recordings during the performance of a spatial navigation task (a virtual version of the Morris Water Maze adapted to humans). Participants (69-88 years old) with amnesic mild cognitive impairment-Clinical Dementia Rating scale (aMCI-CDR 0.5) were selected as probable early AD (eAD) by a neurologist specialized in dementia. All patients included in this study were evaluated at the CDR 0.5 stage but progressed to probable AD during clinical follow-up. An equal number of matching healthy controls (HCs) were evaluated while performing the navigation task. Data were collected at the Department of Neurology of the Clinical Hospital of the Universidad de Chile and the Department of Neuroscience of the Faculty of Universidad de Chile. Results Participants with aMCI preceding AD (eAD) showed impaired spatial learning and their visual exploration differed from the control group. eAD group did not clearly prefer regions of interest that could guide solving the task, while controls did. The eAD group showed decreased visual occipital evoked potentials associated with eye fixations, recorded at occipital electrodes. They also showed an alteration of the spatial spread of activity to parietal and frontal regions at the end of the task. The control group presented marked occipital activity in the beta band (15-20 Hz) at early visual processing time. The eAD group showed a reduction in beta band functional connectivity in the prefrontal cortices reflecting poor planning of navigation strategies. Discussion We found that EEG signals combined with visual-spatial navigation analysis, yielded early and specific features that may underlie the basis for understanding the loss of functional connectivity in AD. Still, our results are clinically promising for early diagnosis required to improve quality of life and decrease healthcare costs.
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Affiliation(s)
- Iván Plaza-Rosales
- Department of Medical Technology, Faculty of Medicine, Universidad de Chile, Santiago, Chile,Biomedical Neuroscience Institute, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Enzo Brunetti
- Biomedical Neuroscience Institute, Faculty of Medicine, Universidad de Chile, Santiago, Chile,Institute of Neurosurgery and Brain Research Dr. Alfonso Asenjo, Santiago, Chile,Department of Neuroscience, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Rodrigo Montefusco-Siegmund
- Faculty of Medicine, Institute of Locomotor System and Rehabilitation, Universidad Austral de Chile, Valdivia, Chile
| | - Samuel Madariaga
- Biomedical Neuroscience Institute, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Rodrigo Hafelin
- Biomedical Neuroscience Institute, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Daniela P. Ponce
- Department of Neurology and Neurosurgery, Hospital Clínico Universidad de Chile, Santiago, Chile,Faculty of Medicine, Center for Advanced Clinical Research, Universidad de Chile, Santiago, Chile
| | - María Isabel Behrens
- Department of Neuroscience, Faculty of Medicine, Universidad de Chile, Santiago, Chile,Department of Neurology and Neurosurgery, Hospital Clínico Universidad de Chile, Santiago, Chile,Faculty of Medicine, Center for Advanced Clinical Research, Universidad de Chile, Santiago, Chile,Department of Neurology and Psychiatry, Clínica Alemana-Universidad del Desarrollo, Santiago, Chile
| | - Pedro E. Maldonado
- Biomedical Neuroscience Institute, Faculty of Medicine, Universidad de Chile, Santiago, Chile,Department of Neuroscience, Faculty of Medicine, Universidad de Chile, Santiago, Chile,Pedro E. Maldonado,
| | - Andrea Paula-Lima
- Biomedical Neuroscience Institute, Faculty of Medicine, Universidad de Chile, Santiago, Chile,Department of Neuroscience, Faculty of Medicine, Universidad de Chile, Santiago, Chile,Institute for Research in Dental Sciences, Faculty of Dentistry, Universidad de Chile, Santiago, Chile,*Correspondence: Andrea Paula-Lima,
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Influence of PICALM and CLU risk variants on beta EEG activity in Alzheimer's disease patients. Sci Rep 2021; 11:20465. [PMID: 34650147 PMCID: PMC8516883 DOI: 10.1038/s41598-021-99589-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 09/27/2021] [Indexed: 11/09/2022] Open
Abstract
PICALM and CLU genes have been linked to alterations in brain biochemical processes that may have an impact on Alzheimer’s disease (AD) development and neurophysiological dynamics. The aim of this study is to analyze the relationship between the electroencephalographic (EEG) activity and the PICALM and CLU alleles described as conferring risk or protective effects on AD patients and healthy controls. For this purpose, EEG activity was acquired from: 18 AD patients and 12 controls carrying risk alleles of both PICALM and CLU genes, and 35 AD patients and 12 controls carrying both protective alleles. Relative power (RP) in the conventional EEG frequency bands (delta, theta, alpha, beta, and gamma) was computed to quantify the brain activity at source level. In addition, spatial entropy (SE) was calculated in each band to characterize the regional distribution of the RP values throughout the brain. Statistically significant differences in global RP and SE at beta band (p-values < 0.05, Mann–Whitney U-test) were found between genotypes in the AD group. Furthermore, RP showed statistically significant differences in 58 cortical regions out of the 68 analyzed in AD. No statistically significant differences were found in the control group at any frequency band. Our results suggest that PICALM and CLU AD-inducing genotypes are involved in physiological processes related to disruption in beta power, which may be associated with physiological disturbances such as alterations in beta-amyloid and neurotransmitter metabolism.
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Yan Y, Zhao A, Ying W, Qiu Y, Ding Y, Wang Y, Xu W, Deng Y. Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer's Disease. Curr Alzheimer Res 2021; 18:513-522. [PMID: 34598666 DOI: 10.2174/1567205018666211001110824] [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] [Received: 11/09/2020] [Revised: 06/24/2021] [Accepted: 08/22/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer's Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD. METHODS We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores. RESULTS In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores. CONCLUSION Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.
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Affiliation(s)
- Yi Yan
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Aonan Zhao
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weina Ying
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinghui Qiu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanfei Ding
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Wang
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Xu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yulei Deng
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Trinh TT, Tsai CF, Hsiao YT, Lee CY, Wu CT, Liu YH. Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs. Front Comput Neurosci 2021; 15:700467. [PMID: 34421565 PMCID: PMC8373435 DOI: 10.3389/fncom.2021.700467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/30/2021] [Indexed: 11/22/2022] Open
Abstract
Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.
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Affiliation(s)
- Thanh-Tung Trinh
- Neural Engineering and Smart Systems Laboratory, Graduate Institute of Manufacturing Technology, College of Mechanical and Electrical Engineering, National Taipei University of Technology (Taipei Tech), Taipei, Taiwan
| | - Chia-Fen Tsai
- Department of Psychiatry, Division of Geriatric Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Tsung Hsiao
- Neural Engineering and Smart Systems Laboratory, Graduate Institute of Mechatronic Engineering, National Taipei University of Technology (Taipei Tech), Taipei, Taiwan
| | - Chun-Ying Lee
- Department of Mechanical Engineering, National Taipei University of Technology (Taipei Tech), Taipei, Taiwan
| | - Chien-Te Wu
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
| | - Yi-Hung Liu
- Department of Mechanical Engineering, National Taiwan University of Science and Technology (Taiwan Tech), Taipei, Taiwan
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Revilla-Vallejo M, Poza J, Gomez-Pilar J, Hornero R, Tola-Arribas MÁ, Cano M, Gómez C. Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer's Disease. ENTROPY (BASEL, SWITZERLAND) 2021; 23:500. [PMID: 33922270 PMCID: PMC8146430 DOI: 10.3390/e23050500] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/10/2021] [Accepted: 04/19/2021] [Indexed: 11/17/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.
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Affiliation(s)
- Marcos Revilla-Vallejo
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
| | - Jesús Poza
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, 47011 Valladolid, Spain
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
- IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, 47011 Valladolid, Spain
| | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
- Department of Neurology, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, Río Hortega University Hospital, 47012 Valladolid, Spain;
| | - Carlos Gómez
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, 47011 Valladolid, Spain; (J.P.); (J.G.-P.); (R.H.); (C.G.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain;
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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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10
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Ruiz-Gómez SJ, Hornero R, Poza J, Santamaría-Vázquez E, Rodríguez-González V, Maturana-Candelas A, Gomez C. A new method to build multiplex networks using Canonical Correlation Analysis for the characterization of the Alzheimer's disease continuum. J Neural Eng 2021; 18. [PMID: 33395667 DOI: 10.1088/1741-2552/abd82c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 01/04/2021] [Indexed: 01/21/2023]
Abstract
OBJECTIVE The aim of this study was to solve one of the current limitations for the characterization of the brain network in the Alzheimer's disease (AD) continuum. Nowadays, frequency-dependent approaches have reached contradictory results depending on the frequency band under study, tangling the possible clinical interpretations. APPROACH To overcome this issue, we proposed a new method to build multiplex networks based on canonical correlation analysis (CCA). Our method determines two basis vectors using the source and electrodelevel frequency-specific network parameters for a reference group, and then project the results for the rest of the groups into these hyperplanes to make them comparable. It was applied to: (i) synthetic signals generated with a Kuramoto-based model; and (ii) a resting-state EEG database formed by recordings from 51 cognitively healthy controls, 51 mild cognitive impairment subjects, 51 mild AD patients, 50 moderate AD patients, and 50 severe AD patients. MAIN RESULTS Our results using synthetic signals showed that the interpretation of the proposed CCA-based multiplex parameters (multiplex average node degree, multiplex characteristic path length and multiplex clustering coefficient) can be analogous to their frequency-specific counterparts, as they displayed similar behaviors in terms of average connectivity, integration, and segregation. Findings using real EEG recordings revealed that dementia due to AD is characterized by a significant increase in average connectivity, and by a loss of integration and segregation. SIGNIFICANCE We can conclude that CCA can be used to build multiplex networks based from frequency-specific results, summarizing all the available information and avoiding the limitations of possible frequency-specific conflicts. Additionally, our method supposes a novel approach for the construction and analysis of multiplex networks during AD continuum.
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Affiliation(s)
- Saúl J Ruiz-Gómez
- Teoría de la señal y comunicaciones e Ingeniería telemática, Universidad de Valladolid, Paseo de Belén 15, Valladolid, Valladolid, 47011, SPAIN
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, ETSI Telecomunicacion, Paseo Belen 15, Valladolid, 47011, SPAIN
| | - Jesus Poza
- Communications and Signal Theory, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, Valladolid, 47011, SPAIN
| | - Eduardo Santamaría-Vázquez
- Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, Plaza de Santa Cruz, 8, Valladolid, Valladolid, 47002, SPAIN
| | - Víctor Rodríguez-González
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, Paseo Belen 15, Valladolid, 47011, SPAIN
| | | | - Carlos Gomez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, E. T. S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, Valladolid, Valladolid, 47011, SPAIN
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11
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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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12
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Gutiérrez-de Pablo V, Gómez C, Poza J, Maturana-Candelas A, Martins S, Gomes I, Lopes AM, Pinto N, Hornero R. Relationship between the Presence of the ApoE ε4 Allele and EEG Complexity along the Alzheimer's Disease Continuum. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3849. [PMID: 32664228 PMCID: PMC7411888 DOI: 10.3390/s20143849] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 06/29/2020] [Accepted: 07/08/2020] [Indexed: 12/15/2022]
Abstract
Alzheimer's disease (AD) is the most prevalent cause of dementia, being considered a major health problem, especially in developed countries. Late-onset AD is the most common form of the disease, with symptoms appearing after 65 years old. Genetic determinants of AD risk are vastly unknown, though, ε 4 allele of the ApoE gene has been reported as the strongest genetic risk factor for AD. The objective of this study was to analyze the relationship between brain complexity and the presence of ApoE ε 4 alleles along the AD continuum. For this purpose, resting-state electroencephalography (EEG) activity was analyzed by computing Lempel-Ziv complexity (LZC) from 46 healthy control subjects, 49 mild cognitive impairment subjects, 45 mild AD patients, 44 moderate AD patients and 33 severe AD patients, subdivided by ApoE status. Subjects with one or more ApoE ε 4 alleles were included in the carriers subgroups, whereas the ApoE ε 4 non-carriers subgroups were formed by subjects without any ε 4 allele. Our results showed that AD continuum is characterized by a progressive complexity loss. No differences were observed between AD ApoE ε 4 carriers and non-carriers. However, brain activity from healthy subjects with ApoE ε 4 allele (carriers subgroup) is more complex than from non-carriers, mainly in left temporal, frontal and posterior regions (p-values < 0.05, FDR-corrected Mann-Whitney U-test). These results suggest that the presence of ApoE ε 4 allele could modify the EEG complexity patterns in different brain regions, as the temporal lobes. These alterations might be related to anatomical changes associated to neurodegeneration, increasing the risk of suffering dementia due to AD before its clinical onset. This interesting finding might help to advance in the development of new tools for early AD diagnosis.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
| | - Carlos Gómez
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
| | - Sandra Martins
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Iva Gomes
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Alexandra M. Lopes
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Nádia Pinto
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
- Center of Mathematics of the University of Porto (CMUP), 4169-007 Porto, Portugal
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
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13
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Maturana-Candelas A, Gómez C, Poza J, Pinto N, Hornero R. EEG Characterization of the Alzheimer's Disease Continuum by Means of Multiscale Entropies. ENTROPY 2019; 21:e21060544. [PMID: 33267258 PMCID: PMC7515033 DOI: 10.3390/e21060544] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 05/24/2019] [Accepted: 05/24/2019] [Indexed: 01/31/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and complexity can be studied applying entropy-based measures throughout multiple temporal scales. In this regard, multiscale sample entropy (MSE) and refined multiscale spectral entropy (rMSSE) were calculated from electroencephalographic (EEG) data. Five minutes of resting-state EEG activity were recorded from 51 healthy controls, 51 mild cognitive impaired (MCI) subjects, 51 mild AD patients (ADMIL), 50 moderate AD patients (ADMOD), and 50 severe AD patients (ADSEV). Our results show statistically significant differences (p-values < 0.05, FDR-corrected Kruskal-Wallis test) between the five groups at each temporal scale. Additionally, average slope values and areas under MSE and rMSSE curves revealed significant changes in complexity mainly for controls vs. MCI, MCI vs. ADMIL and ADMOD vs. ADSEV comparisons (p-values < 0.05, FDR-corrected Mann-Whitney U-test). These findings indicate that MSE and rMSSE reflect the neuronal disturbances associated with the development of dementia, and may contribute to the development of new tools to track the AD progression.
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Affiliation(s)
- Aarón Maturana-Candelas
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain
- Correspondence: ; Tel.: +34-983-423-981
| | - Jesús Poza
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
- Instituto de Neurociencias de Castilla y León (INCYL), Universidad de Salamanca, 37007 Salamanca, Spain
| | - Nadia Pinto
- Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), 4200-135 Porto, Portugal
- Instituto de Investigação e Inovação em Saúde (i3S), 4200-135 Porto, Portugal
- Center of Mathematics of the University of Porto (CMUP), 4169-007 Porto, Portugal
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
- Instituto de Neurociencias de Castilla y León (INCYL), Universidad de Salamanca, 37007 Salamanca, Spain
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