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Patterson RA, Brooks H, Mirjalili M, Rashidi-Ranjbar N, Zomorrodi R, Blumberger DM, Fischer CE, Flint AJ, Graff-Guerrero A, Herrmann N, Kennedy JL, Kumar S, Lanctôt KL, Mah L, Mulsant BH, Pollock BG, Voineskos AN, Wang W, Rajji TK. Neurophysiological and other features of working memory in older adults at risk for dementia. Cogn Neurodyn 2024; 18:795-811. [PMID: 38826646 PMCID: PMC11143125 DOI: 10.1007/s11571-023-09938-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 01/19/2023] [Accepted: 01/31/2023] [Indexed: 03/06/2023] Open
Abstract
Theta-gamma coupling (TGC) is a neurophysiological process that supports working memory. Working memory is associated with other clinical and biological features. The extent to which TGC is associated with these other features and whether it contributes to working memory beyond these features is unknown. Two-hundred-and-three older participants at risk for Alzheimer's dementia-98 with mild cognitive impairment (MCI), 39 with major depressive disorder (MDD) in remission, and 66 with MCI and MDD (MCI + MDD)-completed a clinical assessment, N-back-EEG, and brain MRI. Among them, 190 completed genetic testing, and 121 completed [11C] Pittsburgh Compound B ([11C] PIB) PET imaging. Hierarchical linear regressions were used to assess whether TGC is associated with demographic and clinical variables; Alzheimer's disease-related features (APOE ε4 carrier status and β-amyloid load); and structural features related to working memory. Then, linear regressions were used to assess whether TGC is associated with 2-back performance after accounting for these features. Other than age, TGC was not associated with any non-neurophysiological features. In contrast, TGC (β = 0.27; p = 0.006), age (β = - 0.29; p = 0.012), and parietal cortical thickness (β = 0.24; p = 0.020) were associated with 2-back performance. We also examined two other EEG features that are linked to working memory-theta event-related synchronization and alpha event-related desynchronization-and found them not to be associated with any feature or performance after accounting for TGC. Our findings suggest that TGC is a process that is independent of other clinical, genetic, neurochemical, and structural variables, and supports working memory in older adults at risk for dementia. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09938-y.
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Affiliation(s)
| | - Heather Brooks
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
| | - Mina Mirjalili
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
| | | | - Reza Zomorrodi
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
| | - Daniel M. Blumberger
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- Temerty Centre for Therapeutic Brain Intervention, CAMH, Toronto, ON M6J 1H1 Canada
| | - Corinne E. Fischer
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON M5B, 1T8 Canada
| | - Alastair J. Flint
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- University Health Network, Toronto, ON M5G 1L7 Canada
| | - Ariel Graff-Guerrero
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
| | - Nathan Herrmann
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- Sunnybrook Health Sciences Centre, ON M4N 3M5 Toronto, Canada
| | - James L. Kennedy
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
| | - Sanjeev Kumar
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- Toronto Dementia Research Alliance, University of Toronto, ON M5S 1A1 Toronto, Canada
| | - Krista L. Lanctôt
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- Sunnybrook Health Sciences Centre, ON M4N 3M5 Toronto, Canada
| | - Linda Mah
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- Rotman Research Institute, Baycrest, Toronto, ON M6A 2E1 Canada
| | - Benoit H. Mulsant
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- Temerty Centre for Therapeutic Brain Intervention, CAMH, Toronto, ON M6J 1H1 Canada
| | - Bruce G. Pollock
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- Toronto Dementia Research Alliance, University of Toronto, ON M5S 1A1 Toronto, Canada
| | - Aristotle N. Voineskos
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
| | - Wei Wang
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
| | - Tarek K. Rajji
- Centre for Addiction and Mental Health, Toronto, ON M6J 1H4 Canada
- Department of Psychiatry, TemertyFaculty of Medicine, University of Toronto, Toronto, ON M5S 1A1 Canada
- Toronto Dementia Research Alliance, University of Toronto, ON M5S 1A1 Toronto, Canada
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Chizhikova AA. [Electroencephalography: features of the obtained data and its applicability in psychiatry]. Zh Nevrol Psikhiatr Im S S Korsakova 2024; 124:31-39. [PMID: 38884427 DOI: 10.17116/jnevro202412405131] [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] [Indexed: 06/18/2024]
Abstract
Presently, there is an increased interest in expanding the range of diagnostic and scientific applications of electroencephalography (EEG). The method is attractive due to non-invasiveness, availability of equipment with a wide range of modifications for various purposes, and the ability to track the dynamics of brain electrical activity directly and with high temporal resolution. Spectral, coherency and other types of analysis provide volumetric information about its power, frequency distribution, spatial organization of signal and its self-similarity in dynamics or in different sections at a time. The development of computing technologies provides processing of volumetric data obtained using EEG and a qualitatively new level of their analysis using various mathematical models. This review discusses benefits and limitations of using the EEG in scientific research, currently known interpretation of the obtained data and its physiological and pathological correlates. It is expected to determine the complex relationship between the parameters of brain electrical activity and various functional and pathological conditions. The possibility of using EEG characteristics as biomarkers of various physiological and pathological conditions is being considered. Electronic databases, including MEDLINE (on PubMed), Google Scholar and Russian Scientific Citation Index (RSCI, on elibrary.ru), scientific journals and books were searched to find relevant studies.
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Affiliation(s)
- A A Chizhikova
- Centre for Strategic Planning and Management of Biomedical Health Risks of the Federal Medical Biological Agency, Moscow, Russia
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3
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Liu M, Liu B, Ye Z, Wu D. Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022. Front Neurosci 2023; 17:1128851. [PMID: 37021134 PMCID: PMC10067679 DOI: 10.3389/fnins.2023.1128851] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/06/2023] [Indexed: 03/22/2023] Open
Abstract
BackgroundElectroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI.MethodsRelated publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses.ResultsBetween 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”.ConclusionThe application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI.
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Affiliation(s)
- Mingrui Liu
- Department of Rehabilitation, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Baohu Liu
- Department of Rehabilitation, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zelin Ye
- Department of Cardiovascular, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dongyu Wu
- Department of Rehabilitation, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Dongyu Wu,
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Ponomareva NV, Andreeva TV, Protasova M, Konovalov RN, Krotenkova MV, Kolesnikova EP, Malina DD, Kanavets EV, Mitrofanov AA, Fokin VF, Illarioshkin SN, Rogaev EI. Genetic association of apolipoprotein E genotype with EEG alpha rhythm slowing and functional brain network alterations during normal aging. Front Neurosci 2022; 16:931173. [PMID: 35979332 PMCID: PMC9376365 DOI: 10.3389/fnins.2022.931173] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/27/2022] [Indexed: 12/02/2022] Open
Abstract
The ε4 allele of the apolipoprotein E (APOE4+) genotype is a major genetic risk factor for Alzheimer’s disease (AD), but the mechanisms underlying its influence remain incompletely understood. The study aimed to investigate the possible effect of the APOE genotype on spontaneous electroencephalogram (EEG) alpha characteristics, resting-state functional MRI (fMRI) connectivity (rsFC) in large brain networks and the interrelation of alpha rhythm and rsFC characteristics in non-demented adults during aging. We examined the EEG alpha subband’s relative power, individual alpha peak frequency (IAPF), and fMRI rsFC in non-demented volunteers (age range 26–79 years) stratified by the APOE genotype. The presence of the APOE4+ genotype was associated with lower IAPF and lower relative power of the 11–13 Hz alpha subbands. The age related decrease in EEG IAPF was more pronounced in the APOE4+ carriers than in the APOE4+ non-carriers (APOE4-). The APOE4+ carriers had a stronger fMRI positive rsFC of the interhemispheric regions of the frontoparietal, lateral visual and salience networks than the APOE4– individuals. In contrast, the negative rsFC in the network between the left hippocampus and the right posterior parietal cortex was reduced in the APOE4+ carriers compared to the non-carriers. Alpha rhythm slowing was associated with the dysfunction of hippocampal networks. Our results show that in adults without dementia APOE4+ genotype is associated with alpha rhythm slowing and that this slowing is age-dependent. Our data suggest predominant alterations of inhibitory processes in large-scale brain network of non-demented APOE4+ carriers. Moreover, dysfunction of large-scale hippocampal network can influence APOE-related alpha rhythm vulnerability.
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Affiliation(s)
- Natalya V. Ponomareva
- Research Center of Neurology, Moscow, Russia
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- *Correspondence: Natalya V. Ponomareva,
| | - Tatiana V. Andreeva
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Moscow, Russia
| | - Maria Protasova
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Moscow, Russia
| | | | | | | | | | | | | | | | | | - Evgeny I. Rogaev
- Center for Genetics and Life Science, Sirius University of Science and Technology, Sochi, Russia
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Moscow, Russia
- Brudnick Neuropsychiatric Research Institute (BNRI), University of Massachusetts Medical School, Worcester, MA, United States
- Evgeny I. Rogaev,
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Briels CT, Stam CJ, Scheltens P, Gouw AA. The predictive value of normal EEGs in dementia due to Alzheimer's disease. Ann Clin Transl Neurol 2021; 8:1038-1048. [PMID: 33835723 PMCID: PMC8108419 DOI: 10.1002/acn3.51339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 02/13/2021] [Accepted: 02/21/2021] [Indexed: 12/05/2022] Open
Abstract
Objective To determine differences in clinical presentation and disease progression between patients with dementia due to AD with visually normal and abnormal EEG recordings. We hypothesized that patients with normal electroencephalographs (EEGs) are a representation of the heterogeneity of AD. We expected this group to have a phenotype with relatively predominant hippocampal atrophy, memory deficits, and a slower disease progression. Methods Patients were included based on diagnosis of dementia due to AD, positive amyloid and tau cerebrospinal fluid (CSF) biomarkers, and the availability of EEG recordings. Patients were categorized in groups of normal (N = 208) and abnormal (N = 336) EEG recordings based on visual assessment by experienced neurophysiologists. At baseline demographics, cognitive, MRI, and CSF measures were compared between groups. Cognitive data from follow‐up visits were assessed by linear mixed‐effects models (LMMs), and corrected for baseline value, sex, age, and educational level, to compare cognitive deterioration over time between groups. Results About 1 in 4.5 patients with AD dementia had a visually normal EEG and this group showed better overall cognitive performance compared to the abnormal group, where memory was the most prominent affected domain. The normal group showed less global and parietal but similar medial temporal atrophy. Follow‐up data showed a slower deterioration on all tested cognitive domains in the normal EEG group. Interpretation Patients with dementia due to AD and visually normal EEG recordings showed a milder clinical presentation and had a milder disease progression compared to patients with an abnormal EEG. These results provide evidence of clinical and biological heterogeneity within AD dementia.
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Affiliation(s)
- Casper T Briels
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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6
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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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7
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Gaubert S, Raimondo F, Houot M, Corsi MC, Naccache L, Diego Sitt J, Hermann B, Oudiette D, Gagliardi G, Habert MO, Dubois B, De Vico Fallani F, Bakardjian H, Epelbaum S. EEG evidence of compensatory mechanisms in preclinical Alzheimer’s disease. Brain 2019; 142:2096-2112. [DOI: 10.1093/brain/awz150] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 04/03/2019] [Accepted: 04/07/2019] [Indexed: 11/13/2022] Open
Abstract
Abstract
Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer’s disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer’s disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer’s disease. However, the impact of amyloid-β deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer’s disease in 318 cognitively normal individuals aged 70–85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer’s disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer’s disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer’s disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to ‘suspected non-Alzheimer’s pathophysiology’. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer’s disease.
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Affiliation(s)
- Sinead Gaubert
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
| | - Federico Raimondo
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Laboratorio de Inteligencia Artificial Aplicada, Departamento de computación, FCEyN, Universidad de Buenos Aires, Argentina
- GIGA Consciousness, University of Liège, Liège, Belgium
- Coma Science Group, University Hospital of Liège, Liège, Belgium
| | - Marion Houot
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
- Center for Clinical Investigation (CIC) Neurosciences, Institut du Cerveau et de la Moelle épinière (ICM), Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’Hôpital, Paris, France
| | - Marie-Constance Corsi
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Lionel Naccache
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- AP-HP, Groupe hospitalier Pitié-Salpêtrière, Department of Neurophysiology, Paris, France
| | - Jacobo Diego Sitt
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Bertrand Hermann
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Delphine Oudiette
- AP-HP, Hôpital Pitié-Salpêtrière, Service des Pathologies du Sommeil (Département ‘R3S’), Paris, France
- Sorbonne Université, IHU@ICM, INSERM, CNRS UMR 7225, équipe MOV’IT, Paris, France
| | - Geoffroy Gagliardi
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
| | - Marie-Odile Habert
- Laboratoire d’Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U1146, CNRS UMR 7371, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Department of Nuclear Medicine, Paris, France
- Centre d’Acquisition et de Traitement des Images, CATI neuroimaging platform, France (www.cati-neuroimaging.com)
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
| | - Fabrizio De Vico Fallani
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Hovagim Bakardjian
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
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8
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Koelewijn L, Lancaster TM, Linden D, Dima DC, Routley BC, Magazzini L, Barawi K, Brindley L, Adams R, Tansey KE, Bompas A, Tales A, Bayer A, Singh K. Oscillatory hyperactivity and hyperconnectivity in young APOE-ɛ4 carriers and hypoconnectivity in Alzheimer's disease. eLife 2019; 8:e36011. [PMID: 31038453 PMCID: PMC6491037 DOI: 10.7554/elife.36011] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 04/17/2019] [Indexed: 11/14/2022] Open
Abstract
We studied resting-state oscillatory connectivity using magnetoencephalography in healthy young humans (N = 183) genotyped for APOE-ɛ4, the greatest genetic risk for Alzheimer's disease (AD). Connectivity across frequencies, but most prevalent in alpha/beta, was increased in APOE-ɛ4 in a set of mostly right-hemisphere connections, including lateral parietal and precuneus regions of the Default Mode Network. Similar regions also demonstrated hyperactivity, but only in gamma (40-160 Hz). In a separate study of AD patients, hypoconnectivity was seen in an extended bilateral network that partially overlapped with the hyperconnected regions seen in young APOE-ɛ4 carriers. Using machine-learning, AD patients could be distinguished from elderly controls with reasonable sensitivity and specificity, while young APOE-e4 carriers could also be distinguished from their controls with above chance performance. These results support theories of initial hyperconnectivity driving eventual profound disconnection in AD and suggest that this is present decades before the onset of AD symptomology.
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Affiliation(s)
- Loes Koelewijn
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
| | - Thomas M Lancaster
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
- Neuroscience and Mental Health Research Institute, Cardiff UniversityCardiffUnited Kingdom
- MRC Centre for Neuropsychiatric Genetics and GenomicsCardiff UniversityCardiffUnited Kingdom
| | - David Linden
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
- Neuroscience and Mental Health Research Institute, Cardiff UniversityCardiffUnited Kingdom
- MRC Centre for Neuropsychiatric Genetics and GenomicsCardiff UniversityCardiffUnited Kingdom
| | - Diana C Dima
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
| | - Bethany C Routley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
| | - Lorenzo Magazzini
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
| | - Kali Barawi
- MRC Centre for Neuropsychiatric Genetics and GenomicsCardiff UniversityCardiffUnited Kingdom
| | - Lisa Brindley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
| | - Rachael Adams
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
| | - Katherine E Tansey
- Core Bioinformatics and Statistics Team, College of Biomedical and Life Sciences, Cardiff UniversityCardiffUnited Kingdom
| | - Aline Bompas
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
| | - Andrea Tales
- Department of PsychologyCollege of Human and Health Sciences, Swansea UniversitySwanseaUnited Kingdom
| | - Antony Bayer
- School of Medicine, Cardiff UniversityCardiffUnited Kingdom
| | - Krish Singh
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUnited Kingdom
- Neuroscience and Mental Health Research Institute, Cardiff UniversityCardiffUnited Kingdom
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9
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Vecchio F, Miraglia F, Iberite F, Lacidogna G, Guglielmi V, Marra C, Pasqualetti P, Tiziano FD, Rossini PM. Sustainable method for Alzheimer dementia prediction in mild cognitive impairment: Electroencephalographic connectivity and graph theory combined with apolipoprotein E. Ann Neurol 2018; 84:302-314. [PMID: 30014515 DOI: 10.1002/ana.25289] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 07/03/2018] [Accepted: 07/03/2018] [Indexed: 01/17/2023]
Abstract
OBJECTIVE Mild cognitive impairment (MCI) is a condition intermediate between physiological brain aging and dementia. Amnesic-MCI (aMCI) subjects progress to dementia (typically to Alzheimer-Dementia = AD) at an annual rate which is 20 times higher than that of cognitively intact elderly. The present study aims to investigate whether EEG network Small World properties (SW) combined with Apo-E genotyping, could reliably discriminate aMCI subjects who will convert to AD after approximately a year. METHODS 145 aMCI subjects were divided into two sub-groups and, according to the clinical follow-up, were classified as Converted to AD (C-MCI, 71) or Stable (S-MCI, 74). RESULTS Results showed significant differences in SW in delta, alpha1, alpha2, beta2, gamma bands, with C-MCI in the baseline similar to AD. Receiver Operating Characteristic(ROC) curve, based on a first-order polynomial regression of SW, showed 57% sensitivity, 66% specificity and 61% accuracy(area under the curve: AUC=0.64). In 97 out of 145 MCI, Apo-E allele testing was also available. Combining this genetic risk factor with Small Word EEG, results showed: 96.7% sensitivity, 86% specificity and 91.7% accuracy(AUC=0.97). Moreover, using only the Small World values in these 97 subjects, the ROC showed an AUC of 0.63; the resulting classifier presented 50% sensitivity, 69% specificity and 59.6% accuracy. When different types of EEG analysis (power density spectrum) were tested, the accuracy levels were lower (68.86%). INTERPRETATION Concluding, this innovative EEG analysis, in combination with a genetic test (both low-cost and widely available), could evaluate on an individual basis with great precision the risk of MCI progression. This evaluation could then be used to screen large populations and quickly identify aMCI in a prodromal stage of dementia. Ann Neurol 2018 Ann Neurol 2018;84:302-314.
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Affiliation(s)
| | - Francesca Miraglia
- Brain Connectivity Laboratory, IRCCS San Raffaele Pisana.,Institute of Neurology, Area of Neuroscience, Catholic University of The Sacred Heart
| | | | | | | | - Camillo Marra
- Institute of Neurology, Area of Neuroscience, Catholic University of The Sacred Heart.,Neuropsychological Center, Catholic University of The Sacred Heart
| | - Patrizio Pasqualetti
- Service of Medical Statistics and Information Technology, Fatebenefratelli Foundation for Health Research and Education, AFaR Division
| | | | - Paolo Maria Rossini
- Institute of Neurology, Area of Neuroscience, Catholic University of The Sacred Heart.,Fondazione Policlinico Universitario A.Gemelli IRCCS, Rome, Italy
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10
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Guo Z, Wu X, Liu J, Yao L, Hu B. Altered electroencephalography functional connectivity in depression during the emotional face-word Stroop task. J Neural Eng 2018; 15:056014. [PMID: 29923500 DOI: 10.1088/1741-2552/aacdbb] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Depression is a severe mental disorder. However, the neural mechanisms underlying affective interference (difficulties in directing attention away from negative distractors) in depression patients are still not well-understood. In particular, the connections between brain regions remain unclear. Using the emotional face-word Stroop task, we aimed to reveal the altered electroencephalography (EEG) functional connectivity in patients with depression, using concepts from event-related potentials (ERPs) and time series clustering. APPROACH In this study, the EEG signals of ten healthy participants and ten depression patients were collected from a 64-sensor cap. Subsequently, EEG signals were segmented into temporal windows corresponding to the ERPs. For each duration, the dynamic time warping algorithm was used to calculate the similarities between EEG signals from different electrodes, and differences of these similarities were compared between the groups. Finally, hierarchical clustering was used to identify functionally connected regions and examine changes in depression. MAIN RESULTS It was observed that during the time interval of 400-600 ms (N450 components), depression patients had more long-range connections than did healthy control patients and exhibited abnormal functional connectivity via the superior and middle frontal gyrus, specifically, the dorsolateral prefrontal cortex (DL-PFC, Brodmann's area 8 and 9), which is related to the control and resolution of affective interference. Moreover, the functionally connected region of depression patients was much larger than that of healthy participants, which is caused by brain resource reorganization. SIGNIFICANCE These findings thus provide new insights into the neural mechanisms of depression and further identify the DL-PFC and connections between certain electrodes as quantitative indicators of depression.
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Affiliation(s)
- Zhenghao Guo
- College of Information Science and Technology, Beijing Normal University, Beijing 100875, People's Republic of China
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11
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Ochoa JF, Alonso JF, Duque JE, Tobón CA, Baena A, Lopera F, Mañanas MA, Hernández AM. Precuneus Failures in Subjects of the PSEN1 E280A Family at Risk of Developing Alzheimer's Disease Detected Using Quantitative Electroencephalography. J Alzheimers Dis 2017; 58:1229-1244. [PMID: 28550254 DOI: 10.3233/jad-161291] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Presenilin-1 (PSEN1) mutations are the most common cause of familial early onset Alzheimer's disease (AD). The PSEN1 E280A (E280A) mutation has an autosomal dominant inheritance and is involved in the production of amyloid-β. The largest family group of carriers with E280A mutation is found in Antioquia, Colombia. The study of mutation carriers provides a unique opportunity to identify brain changes in stages previous to AD. Electroencephalography (EEG) is a low cost and minimally invasiveness technique that enables the following of brain changes in AD. OBJECTIVE To examine how previous reported differences in EEG for Theta and Alpha-2 rhythms in E280A subjects are related to specific regions in cortex and could be tracked across different ages. METHODS EEG signals were acquired during resting state from non-carriers and carriers, asymptomatic and symptomatic subjects from E280A kindred from Antioquia, Colombia. Independent component analysis (ICA) and inverse solution methods were used to locate brain regions related to differences in Theta and Alpha-2 bands. RESULTS ICA identified two components, mainly related to the Precuneus, where the differences in Theta and Alpha-2 exist simultaneously at asymptomatic and symptomatic stages. When the ratio between Theta and Alpha-2 is used, significant correlations exist with age and a composite cognitive scale. CONCLUSION Theta and Alpha-2 rhythms are altered in E280A subjects. The alterations are possible to track at Precuneus regions using EEG, ICA, and inverse solution methods.
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Affiliation(s)
- John Fredy Ochoa
- Bioinstrumentation and Clinical Engineering Research Group, Bioengineering Program, Universidad de Antioquia, Medellín, Colombia
| | - Joan Francesc Alonso
- Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB), Universitat Politènica de Catalunya (UPC), Barcelona, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | - Jon Edinson Duque
- Bioinstrumentation and Clinical Engineering Research Group, Bioengineering Program, Universidad de Antioquia, Medellín, Colombia
| | - Carlos Andrés Tobón
- Neuroscience Group of Antioquia, Medical School, Universidad de Antioquia, Medellín, Colombia.,Neuropsychology and Behavior Group, Medical School, Universidad de Antioquia, Medellín, Colombia
| | - Ana Baena
- Neuroscience Group of Antioquia, Medical School, Universidad de Antioquia, Medellín, Colombia
| | - Francisco Lopera
- Neuroscience Group of Antioquia, Medical School, Universidad de Antioquia, Medellín, Colombia
| | - Miguel Angel Mañanas
- Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB), Universitat Politènica de Catalunya (UPC), Barcelona, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | - Alher Mauricio Hernández
- Bioinstrumentation and Clinical Engineering Research Group, Bioengineering Program, Universidad de Antioquia, Medellín, Colombia
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12
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Chiang HS, Pao SC. An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease. J Med Syst 2016; 40:125. [PMID: 27059738 DOI: 10.1007/s10916-016-0476-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2015] [Accepted: 03/14/2016] [Indexed: 01/19/2023]
Abstract
Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early treatment of Alzheimer's disease can significantly reduce deterioration. Early diagnosis is difficult, and early symptoms are frequently overlooked. While much of the literature focuses on disease detection, the use of electroencephalography (EEG) in Alzheimer's diagnosis has received relatively little attention. This study combines the fuzzy and associative Petri net methodologies to develop a model for the effective and objective detection of Alzheimer's disease. Differences in EEG patterns between normal subjects and Alzheimer patients are used to establish prediction criteria for Alzheimer's disease, potentially providing physicians with a reference for early diagnosis, allowing for early action to delay the disease progression.
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Affiliation(s)
- Hsiu-Sen Chiang
- Department of Information Management, National Taichung University of Science and Technology, No. 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Republic of China.
| | - Shun-Chi Pao
- Department of Information Management, National Taichung University of Science and Technology, No. 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Republic of China
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13
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Impaired cortical oscillatory coupling in mild cognitive impairment: anatomical substrate and ApoE4 effects. Brain Struct Funct 2014; 220:1721-37. [PMID: 24682246 DOI: 10.1007/s00429-014-0757-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Accepted: 03/16/2014] [Indexed: 01/04/2023]
Abstract
Our current knowledge about the anatomical substrate of impaired resting-state cortical oscillatory coupling in mild cognitive impairment is still rudimentary. Here, we show that both resting-state oscillatory coupling and its anatomical correlates clearly distinguish healthy older (HO) adults from individuals with amnestic mild cognitive impairment (aMCI). aMCI showed failures in neural-phase coupling of resting-state electroencephalographic alpha activity mostly evident between fronto-temporal and parietal regions. As oligomers of amyloid-beta (Aβ) are linked to synaptic dysfunction in Alzheimer's disease (AD), we further investigated whether plasma concentrations of these oligomers (Aβ40 and Aβ42) accounted for impaired patterns of oscillatory coupling in aMCI. Results revealed that decreased plasma Aβ42 was associated with augmented coupling of parieto-temporal regions in HO subjects, but no relationship was found in aMCI. Oscillatory coupling of frontal regions was also significantly reduced in aMCI carriers of the ε4 allele of the Apolipoprotein E (ApoE) compared to ε4 noncarriers, although neither neuroanatomical nor plasma Aβ changes accounted for this difference. However, the abnormal pattern of oscillatory coupling in aMCI was negatively related to volume of the angular gyrus, and positively related to volume of the precuneus and the splenium of the corpus callosum. Previous evidence suggests that all these regions are neuropathological targets of AD. The current study takes that scenario one step further, suggesting that this anatomical damage could be responsible for disrupted cortical oscillatory coupling in aMCI. Together, these data shed light on how the MCI status modifies anatomo-functional relationships underlying coordination of large-scale cortical systems in the resting-state.
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14
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de Waal H, Stam CJ, de Haan W, van Straaten ECW, Blankenstein MA, Scheltens P, van der Flier WM. Alzheimer's disease patients not carrying the apolipoprotein E ε4 allele show more severe slowing of oscillatory brain activity. Neurobiol Aging 2013; 34:2158-63. [PMID: 23587637 DOI: 10.1016/j.neurobiolaging.2013.03.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2012] [Revised: 02/22/2013] [Accepted: 03/11/2013] [Indexed: 11/17/2022]
Abstract
The objective of this study was to quantitatively assess the relationship between apolipoprotein (APOE) genotype and electroencephalographic oscillatory brain dynamics in Alzheimer's disease (AD) patients and control subjects and its regional distribution. We obtained resting-state electroencephalographs of 320 AD patients and 246 control subjects, categorized into APOE ε4 carriers and noncarriers. Peak frequency and relative power in 4 different frequency bands were calculated. We tested the associations between APOE genotype and relative power in 4 brain regions. Peak frequency was comparable in APOE ε4 carrying and noncarrying control subjects, but lower in APOE ε4 noncarrying AD patients. In control subjects, APOE ε4 carriers had a different regional distribution of alpha power than noncarriers. We found no APOE effect in beta, delta, and theta bands. In AD, APOE ε4 noncarriers had lower alpha and higher delta power than carriers. This difference was most pronounced in the parieto-occipital region. In the theta band, APOE ε4 noncarriers had a different regional distribution of power compared with carriers. In conclusion, the most pronounced effect of genotype was seen in AD patients, and APOE ε4 noncarriers showed slower activity, especially in parieto-occipital regions.
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Affiliation(s)
- Hanneke de Waal
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands.
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15
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de Haan W. The effect of neuronal activity and connectivity on Alzheimer’s disease: a new direction and its implications for future treatment strategies. Neurodegener Dis Manag 2013. [DOI: 10.2217/nmt.12.79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Willem de Haan
- Alzheimer Center & Department of Neurology, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands
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16
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Structure out of chaos: functional brain network analysis with EEG, MEG, and functional MRI. Eur Neuropsychopharmacol 2013; 23:7-18. [PMID: 23158686 DOI: 10.1016/j.euroneuro.2012.10.010] [Citation(s) in RCA: 80] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2011] [Revised: 09/10/2012] [Accepted: 10/18/2012] [Indexed: 01/21/2023]
Abstract
The brain is the characteristic of a complex structure. By representing brain function, measured with EEG, MEG, and fMRI, as an abstract network, methods for the study of complex systems can be applied. These network studies have revealed insights in the complex, yet organized, architecture that is evidently present in brain function. We will discuss some technical aspects of formation and assessment of the functional brain networks. Moreover, the results that have been reported in this respect in the last years, in healthy brains as well as in functional brain networks of subjects with a neurological or psychiatrical disease, will be reviewed.
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17
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Capturing dynamic patterns of task-based functional connectivity with EEG. Neuroimage 2012; 66:311-7. [PMID: 23142654 DOI: 10.1016/j.neuroimage.2012.10.032] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Revised: 10/04/2012] [Accepted: 10/19/2012] [Indexed: 11/23/2022] Open
Abstract
A new approach to trace the dynamic patterns of task-based functional connectivity, by combining signal segmentation, dynamic time warping (DTW), and Quality Threshold (QT) clustering techniques, is presented. Electroencephalography (EEG) signals of 5 healthy subjects were recorded as they performed an auditory oddball and a visual modified oddball tasks. To capture the dynamic patterns of functional connectivity during the execution of each task, EEG signals are segmented into durations that correspond to the temporal windows of previously well-studied event-related potentials (ERPs). For each temporal window, DTW is employed to measure the functional similarities among channels. Unlike commonly used temporal similarity measures, such as cross correlation, DTW compares time series by taking into consideration that their alignment properties may vary in time. QT clustering analysis is then used to automatically identify the functionally connected regions in each temporal window. For each task, the proposed approach was able to establish a unique sequence of dynamic pattern (observed in all 5 subjects) for brain functional connectivity.
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18
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Canuet L, Tellado I, Couceiro V, Fraile C, Fernandez-Novoa L, Ishii R, Takeda M, Cacabelos R. Resting-state network disruption and APOE genotype in Alzheimer's disease: a lagged functional connectivity study. PLoS One 2012; 7:e46289. [PMID: 23050006 PMCID: PMC3457973 DOI: 10.1371/journal.pone.0046289] [Citation(s) in RCA: 105] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2012] [Accepted: 08/28/2012] [Indexed: 01/07/2023] Open
Abstract
Background The apolipoprotein E epsilon 4 (APOE-4) is associated with a genetic vulnerability to Alzheimer's disease (AD) and with AD-related abnormalities in cortical rhythms. However, it is unclear whether APOE-4 is linked to a specific pattern of intrinsic functional disintegration of the brain after the development of the disease or during its different stages. This study aimed at identifying spatial patterns and effects of APOE genotype on resting-state oscillations and functional connectivity in patients with AD, using a physiological connectivity index called “lagged phase synchronization”. Methodology/Principal Findings Resting EEG was recorded during awake, eyes-closed state in 125 patients with AD and 60 elderly controls. Source current density and functional connectivity were determined using eLORETA. Patients with AD exhibited reduced parieto-occipital alpha oscillations compared with controls, and those carrying the APOE-4 allele had reduced alpha activity in the left inferior parietal and temporo-occipital cortex relative to noncarriers. There was a decreased alpha2 connectivity pattern in AD, involving the left temporal and bilateral parietal cortex. Several brain regions exhibited increased lagged phase synchronization in low frequencies, specifically in the theta band, across and within hemispheres, where temporal lobe connections were particularly compromised. Areas with abnormal theta connectivity correlated with cognitive scores. In patients with early AD, we found an APOE-4-related decrease in interhemispheric alpha connectivity in frontal and parieto-temporal regions. Conclusions/Significance In addition to regional cortical dysfunction, as indicated by abnormal alpha oscillations, there are patterns of functional network disruption affecting theta and alpha bands in AD that associate with the level of cognitive disturbance or with the APOE genotype. These functional patterns of nonlinear connectivity may potentially represent neurophysiological or phenotypic markers of AD, and aid in early detection of the disorder.
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Affiliation(s)
- Leonides Canuet
- EuroEspes Biomedical Research Center, Institute for CNS Disorders and Genomic Medicine, Corunna, Spain.
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19
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de Haan W, Mott K, van Straaten ECW, Scheltens P, Stam CJ. Activity dependent degeneration explains hub vulnerability in Alzheimer's disease. PLoS Comput Biol 2012; 8:e1002582. [PMID: 22915996 PMCID: PMC3420961 DOI: 10.1371/journal.pcbi.1002582] [Citation(s) in RCA: 268] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2011] [Accepted: 05/07/2012] [Indexed: 11/18/2022] Open
Abstract
Brain connectivity studies have revealed that highly connected 'hub' regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-β deposition at an early stage. Recently, excessive local neuronal activity has been shown to increase amyloid deposition. In this study we use a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimer's disease is due to this feature. Cortical brain regions were modeled as neural masses, each describing the average activity (spike density and spectral power) of a large number of interconnected excitatory and inhibitory neurons. The large-scale network consisted of 78 neural masses, connected according to a human DTI-based cortical topology. Spike density and spectral power were positively correlated with structural and functional node degrees, confirming the high activity of hub regions, also offering a possible explanation for high resting state Default Mode Network activity. 'Activity dependent degeneration' (ADD) was simulated by lowering synaptic strength as a function of the spike density of the main excitatory neurons, and compared to random degeneration. Resulting structural and functional network changes were assessed with graph theoretical analysis. Effects of ADD included oscillatory slowing, loss of spectral power and long-range synchronization, hub vulnerability, and disrupted functional network topology. Observed transient increases in spike density and functional connectivity match reports in Mild Cognitive Impairment (MCI) patients, and may not be compensatory but pathological. In conclusion, the assumption of excessive neuronal activity leading to degeneration provides a possible explanation for hub vulnerability in Alzheimer's disease, supported by the observed relation between connectivity and activity and the reproduction of several neurophysiologic hallmarks. The insight that neuronal activity might play a causal role in Alzheimer's disease can have implications for early detection and interventional strategies.
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Affiliation(s)
- Willem de Haan
- Department of Clinical Neurophysiology and MEG, VU University Medical Center, Amsterdam, The Netherlands.
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20
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Hunter JM, Cirrito JR, Restivo JL, Kinley RD, Sullivan PM, Holtzman DM, Koger D, Delong C, Lin S, Zhao L, Liu F, Bales K, Paul SM. Emergence of a seizure phenotype in aged apolipoprotein epsilon 4 targeted replacement mice. Brain Res 2012; 1467:120-32. [PMID: 22682924 DOI: 10.1016/j.brainres.2012.05.048] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2012] [Revised: 05/22/2012] [Accepted: 05/23/2012] [Indexed: 01/29/2023]
Abstract
The apolipoprotein ε4 allele is the strongest genetic risk factor for late-onset Alzheimer's disease (AD) and is associated with earlier age of onset. The incidence of spontaneous seizures has been reported to be increased in sporadic AD as well as in the early onset autosomal dominant forms of AD. We now report the emergence of a seizure phenotype in aged apolipoprotein E4 (apoE4) targeted replacement (TR) mice but not in age-matched apoE2 TR or apoE3 TR mice. Tonic-clonic seizures developed spontaneously after 5 months of age in apoE4 TR mice and are triggered by mild stress. Female mice had increased seizure penetrance compared to male mice, but had slightly reduced overall seizure severity. The majority of seizures were characterized by head and neck jerks, but 25% of aged apoE4 TR mice had more severe tonic-clonic seizures which occasionally progressed to tonic extension and death. Aged apoE4 TR mice progressed through pentylenetetrazol-induced seizure stages more rapidly than did apoE3 TR and apoE2 TR mice. Electroencephalographic (EEG) recordings revealed more frequent bursts of synchronous theta activity in the hippocampus of apoE4 TR mice than in apoE2 TR or apoE3 TR mice. Cortical EEG recordings also revealed sharp spikes and other abnormalities in apoE4 TR mice. Taken together, these findings demonstrate the emergence of an age-dependent seizure phenotype in old apoE4 TR mice in the absence of human amyloid-β peptide (Aβ) overexpression, suggesting increased central nervous system neural network excitability.
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Affiliation(s)
- Jesse M Hunter
- Neuroscience Discovery, Eli Lilly and Co., Indianapolis, IN 46285, USA.
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21
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Sankari Z, Adeli H, Adeli A. Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer’s disease. Clin Neurophysiol 2011; 122:897-906. [PMID: 21056936 DOI: 10.1016/j.clinph.2010.09.008] [Citation(s) in RCA: 132] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2010] [Revised: 08/25/2010] [Accepted: 09/09/2010] [Indexed: 11/30/2022]
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22
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Robson J, Mehta N, Polcz JE, Hermer L. Toward the development of a sensitive, pre-clinical screen for neurological diseases from spontaneous neural coordination in juvenile and young–adult C57BK6 mice. Neurosci Lett 2010; 471:74-8. [DOI: 10.1016/j.neulet.2010.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2009] [Revised: 12/31/2009] [Accepted: 01/10/2010] [Indexed: 11/28/2022]
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Pons AJ, Cantero JL, Atienza M, Garcia-Ojalvo J. Relating structural and functional anomalous connectivity in the aging brain via neural mass modeling. Neuroimage 2010; 52:848-61. [PMID: 20056154 DOI: 10.1016/j.neuroimage.2009.12.105] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2009] [Revised: 12/22/2009] [Accepted: 12/23/2009] [Indexed: 11/28/2022] Open
Abstract
The structural changes that arise as the brain ages influence its functionality. In many cases, the anatomical degradation simply leads to normal aging. In others, the neurodegeneration is large enough to cause neurological disorders (e.g. Alzheimer's disease). Structure and function can be both currently measured using noninvasive techniques, such as magnetic resonance imaging (MRI) and electroencephalography (EEG) respectively. However, a full theoretical scheme linking structural and functional degradation is still lacking. Here we present a neural mass model that aims to bridge both levels of description and that reproduces experimentally observed multichannel EEG recordings of alpha rhythm in young subjects, healthy elderly subjects, and patients with mild cognitive impairment. We focus our attention in the dominant frequency of the signals at different electrodes and in the correlation between specific electrode pairs, measured via the phase-lag index. Our model allows us to study the influence of different structural connectivity pathways, independently of each other, on the normal and aberrantly aging brain. In particular, we study in detail the effect of the thalamic input on specific cortical regions, the long-range connectivity between cortical regions, and the short-range coupling within the same cortical area. Once the influence of each type of connectivity is determined, we characterize the regions of parameter space compatible with the EEG recordings of the populations under study. Our results show that the different types of connectivity must be fine-tuned to maintain the brain in a healthy functioning state independently of its age and brain condition.
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Affiliation(s)
- A J Pons
- Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Edifici GAIA, Rambla Sant Nebridi s/n, 08222 Terrassa, Spain
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de Haan W, Pijnenburg YAL, Strijers RLM, van der Made Y, van der Flier WM, Scheltens P, Stam CJ. Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory. BMC Neurosci 2009; 10:101. [PMID: 19698093 PMCID: PMC2736175 DOI: 10.1186/1471-2202-10-101] [Citation(s) in RCA: 243] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2009] [Accepted: 08/21/2009] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL), a measure of functional connectivity, was calculated for each sensor pair in 0.5-4 Hz, 4-8 Hz, 8-10 Hz, 10-13 Hz, 13-30 Hz and 30-45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity), characteristic path length (global connectivity) and degree correlation (network 'assortativity'). All results were normalized for network size and compared with random control networks. RESULTS In AD, the clustering coefficient decreased in the lower alpha and beta bands (p < 0.001), and the characteristic path length decreased in the lower alpha and gamma bands (p < 0.05) compared to controls. In FTLD no significant differences with controls were found in these measures. The degree correlation decreased in both alpha bands in AD compared to controls (p < 0.05), but increased in the FTLD lower alpha band compared with controls (p < 0.01). CONCLUSION With decreasing local and global connectivity parameters, the large-scale functional brain network organization in AD deviates from the optimal 'small-world' network structure towards a more 'random' type. This is associated with less efficient information exchange between brain areas, supporting the disconnection hypothesis of AD. Surprisingly, FTLD patients show changes in the opposite direction, towards a (perhaps excessively) more 'ordered' network structure, possibly reflecting a different underlying pathophysiological process.
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Affiliation(s)
- Willem de Haan
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands.
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