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Zheng X, Wang B, Liu H, Wu W, Sun J, Fang W, Jiang R, Hu Y, Jin C, Wei X, Chen SSC. Diagnosis of Alzheimer's disease via resting-state EEG: integration of spectrum, complexity, and synchronization signal features. Front Aging Neurosci 2023; 15:1288295. [PMID: 38020761 PMCID: PMC10661409 DOI: 10.3389/fnagi.2023.1288295] [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: 09/04/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Background Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence of more than 55 million people. Electroencephalogram (EEG) has become a suitable, accurate, and highly sensitive biomarker for the identification and diagnosis of AD. Methods In this study, a public database of EEG resting state-closed eye recordings containing 36 AD subjects and 29 normal subjects was used. And then, three types of signal features of resting-state EEG, i.e., spectrum, complexity, and synchronization, were performed by applying various signal processing and statistical methods, to obtain a total of 18 features for each signal epoch. Next, the supervised machine learning classification algorithms of decision trees, random forests, and support vector machine (SVM) were compared in categorizing processed EEG signal features of AD and normal cases with leave-one-person-out cross-validation. Results The results showed that compared to normal cases, the major change in EEG characteristics in AD cases was an EEG slowing, a reduced complexity, and a decrease in synchrony. The proposed methodology achieved a relatively high classification accuracy of 95.65, 95.86, and 88.54% between AD and normal cases for decision trees, random forests, and SVM, respectively, showing that the integration of spectrum, complexity, and synchronization features for EEG signals can enhance the performance of identifying AD and normal subjects. Conclusion This study recommended the integration of EEG features of spectrum, complexity, and synchronization for aiding the diagnosis of AD.
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Affiliation(s)
- Xiaowei Zheng
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
- School of Mathematics, Northwest University, Xian, China
- Medical Big Data Research Center, Northwest University, Xi'an, China
| | - Bozhi Wang
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Hao Liu
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Wencan Wu
- School of Mathematics, Northwest University, Xian, China
| | - Jiamin Sun
- School of Mathematics, Northwest University, Xian, China
| | - Wei Fang
- School of Mathematics, Northwest University, Xian, China
| | - Rundong Jiang
- School of Mathematics, Northwest University, Xian, China
| | - Yajie Hu
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Cheng Jin
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Xin Wei
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
- School of Humanities and Education, Xi'an Eurasia University, Xi'an, China
- Institute of Social Psychology, Xi'an Jiaotong University, Xi'an, China
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Alexandersen CG, de Haan W, Bick C, Goriely A. A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer's disease. J R Soc Interface 2023; 20:20220607. [PMID: 36596460 PMCID: PMC9810432 DOI: 10.1098/rsif.2022.0607] [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] [Indexed: 01/05/2023] Open
Abstract
Alzheimer's disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-β and tau affect neurons at the cellular scale. On a larger scale, Alzheimer's patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein, we model the spreading of both amyloid-β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-β and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-β and tau protein.
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Affiliation(s)
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Christian Bick
- Mathematical Institute, University of Oxford, Oxford, UK,Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands,Amsterdam Neuroscience—Systems and Network Neuroscience, Amsterdam, The Netherlands
| | - Alain Goriely
- Mathematical Institute, University of Oxford, Oxford, UK
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Mitsukura Y, Sumali B, Watanabe H, Ikaga T, Nishimura T. Frontotemporal EEG as potential biomarker for early MCI: a case-control study. BMC Psychiatry 2022; 22:289. [PMID: 35459119 PMCID: PMC9027034 DOI: 10.1186/s12888-022-03932-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 04/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Previous studies using EEG (electroencephalography) as biomarker for dementia have attempted to research, but results have been inconsistent. Most of the studies have extremely small number of samples (average N = 15) and studies with large number of data do not have control group. We identified EEG features that may be biomarkers for dementia with 120 subjects (dementia 10, MCI 33, against control 77). METHODS We recorded EEG from 120 patients with dementia as they stayed in relaxed state using a single-channel EEG device while conducting real-time noise reduction and compared them to healthy subjects. Differences in EEG between patients and controls, as well as differences in patients' severity, were examined using the ratio of power spectrum at each frequency. RESULTS In comparing healthy controls and dementia patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. CONCLUSIONS A study with a larger sample size should be conducted to gauge reproducibility, but the results implied the effectiveness of EEG in clinical practice as a biomarker of MCI (mild cognitive impairment) and/or dementia.
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Affiliation(s)
- Yasue Mitsukura
- Department of System Design Engineering, School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa, Japan.
| | - Brian Sumali
- grid.26091.3c0000 0004 1936 9959Keio Global Institute(KGRI), Keio University, Tokyo, Japan
| | - Hideto Watanabe
- grid.26091.3c0000 0004 1936 9959Department of System Design Engineering, School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa Japan
| | - Toshiharu Ikaga
- grid.26091.3c0000 0004 1936 9959Department of System Design Engineering, School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa Japan
| | - Toshihiko Nishimura
- grid.168010.e0000000419368956Department of Anesthesia, School of Medicine, Stanford University, Stanford, CA USA
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Al-Nuaimi AH, Blūma M, Al-Juboori SS, Eke CS, Jammeh E, Sun L, Ifeachor E. Robust EEG Based Biomarkers to Detect Alzheimer's Disease. Brain Sci 2021; 11:1026. [PMID: 34439645 PMCID: PMC8394244 DOI: 10.3390/brainsci11081026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 11/16/2022] Open
Abstract
Biomarkers to detect Alzheimer's disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).
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Affiliation(s)
- Ali H. Al-Nuaimi
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
- College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Al Adhamiya, Baghdad 10053, Iraq
| | - Marina Blūma
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy;
| | - Shaymaa S. Al-Juboori
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
- College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Al Adhamiya, Baghdad 10053, Iraq
| | - Chima S. Eke
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Emmanuel Jammeh
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Lingfen Sun
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Emmanuel Ifeachor
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
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Engedal K, Barca ML, Høgh P, Bo Andersen B, Winther Dombernowsky N, Naik M, Gudmundsson TE, Øksengaard AR, Wahlund LO, Snaedal J. The Power of EEG to Predict Conversion from Mild Cognitive Impairment and Subjective Cognitive Decline to Dementia. Dement Geriatr Cogn Disord 2021; 49:38-47. [PMID: 32610316 DOI: 10.1159/000508392] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 05/01/2020] [Indexed: 12/21/2022] Open
Abstract
INTRODUCTION The aim of this study was to examine if quantitative electroencephalography (qEEG) using the statistical pattern recognition (SPR) method could predict conversion to dementia in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHODS From 5 Nordic memory clinics, we included 47 SCD patients, 99 MCI patients, and 67 healthy controls. EEGs analyzed with the SPR method together with clinical data recorded at baseline were evaluated. The patients were followed up for a mean of 62.5 (SD 17.6) months and reexamined. RESULTS Of 200 participants with valid clinical information, 70 had converted to dementia, and 52 had developed Alzheimer's disease. Receiver-operating characteristic analysis of the EEG results as defined by a dementia index (DI) ranging from 0 to 100 revealed that the area under the curve was 0.78 (95% CI 0.70-0.85), corresponding to a sensitivity of 71%, specificity of 69%, and accuracy of 69%. A logistic regression analysis showed that by adding results of a cognitive test at baseline to the EEG DI, accuracy could improve. CONCLUSION We conclude that applying qEEG using the automated SPR method can be helpful in identifying patients with SCD and MCI that have a high risk of converting to dementia over a 5-year period. As the discriminant power of the method is of moderate degree, it should be used in addition to routine diagnostic methods.
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Affiliation(s)
- Knut Engedal
- Norwegian Advisory Unit for Aging and Health, Vestfold Health Trust, Tønsberg, Norway, .,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway,
| | - Maria Lage Barca
- Norwegian Advisory Unit for Aging and Health, Vestfold Health Trust, Tønsberg, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Peter Høgh
- Department of Neurology, Regional Dementia Research Center, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Birgitte Bo Andersen
- Department of Neurology, Danish Dementia Research Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Nanna Winther Dombernowsky
- Department of Neurology, Danish Dementia Research Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Mala Naik
- Department of Geriatric Medicine, Haraldsplass Deaconess Hospital, Bergen, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway
| | | | | | - Lars-Olof Wahlund
- Section for Clinical Geriatrics, NVS Department, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
| | - Jon Snaedal
- Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik, Iceland
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Lo Giudice P, Mammone N, Morabito FC, Pizzimenti RG, Ursino D, Virgili L. Leveraging network analysis to support experts in their analyses of subjects with MCI and AD. Med Biol Eng Comput 2019; 57:1961-1983. [PMID: 31301007 DOI: 10.1007/s11517-019-02004-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 06/09/2019] [Indexed: 11/25/2022]
Abstract
In this paper, we propose a network analysis-based approach to help experts in their analyses of subjects with mild cognitive impairment (hereafter, MCI) and Alzheimer's disease (hereafter, AD) and to investigate the evolution of these subjects over time. The inputs of our approach are the electroencephalograms (hereafter, EEGs) of the patients to analyze, performed at a certain time and, again, 3 months later. Given an EEG of a subject, our approach constructs a network with nodes that represent the electrodes and edges that denote connections between electrodes. Then, it applies several network-based techniques allowing the investigation of subjects with MCI and AD and the analysis of their evolution over time. (i) A connection coefficient, supporting experts to distinguish patients with MCI from patients with AD; (ii) A conversion coefficient, supporting experts to verify if a subject with MCI is converting to AD; (iii) Some network motifs, i.e., network patterns very frequent in one kind of patient and absent, or very rare, in the other. Patients with AD, just by the very nature of their condition, cannot be forced to stay motionless while undergoing examinations for a long time. EEG is a non-invasive examination that can be easily done on them. Since AD and MCI, if prodromal to AD, are associated with a loss of cortical connections, the adoption of network analysis appears suitable to investigate the effects of the progression of the disease on EEG. This paper confirms the suitability of this idea Graphical Abstract Ability of our proposed model to distinguish a control subject from a patient with MCI and a patient with AD. Blue edges represent strong connections among the corresponding brain areas; red edges denote middle connections, whereas green edges indicate weak connections. In the control subject (at the top), most connections are blue. In the patient with MCI (at the middle), most connections are red and green. In the patient with AD (at the bottom), most connections are either absent or green. .
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Affiliation(s)
- Paolo Lo Giudice
- DIIES, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
| | - Nadia Mammone
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
| | | | | | | | - Luca Virgili
- DII, Polytechnic University of Marche, Ancona, Italy
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7
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Cassani R, Estarellas M, San-Martin R, Fraga FJ, Falk TH. Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment. DISEASE MARKERS 2018; 2018:5174815. [PMID: 30405860 PMCID: PMC6200063 DOI: 10.1155/2018/5174815] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/12/2018] [Accepted: 07/29/2018] [Indexed: 12/17/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.
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Affiliation(s)
- Raymundo Cassani
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
| | - Mar Estarellas
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
- Department of Bioengineering, Imperial College London, London, UK
| | - Rodrigo San-Martin
- Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Francisco J. Fraga
- Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Tiago H. Falk
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
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de la Torre JC. Are Major Dementias Triggered by Poor Blood Flow to the Brain? Theoretical Considerations. J Alzheimers Dis 2018; 57:353-371. [PMID: 28211814 DOI: 10.3233/jad-161266] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
There is growing evidence that chronic brain hypoperfusion plays a central role in the development of Alzheimer's disease (AD) long before dyscognitive symptoms or amyloid-β accumulation in the brain appear. This commentary proposes that dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and Creutzfeldt-Jakob disease (CJD) may also develop from chronic brain hypoperfusion following a similar but not identical neurometabolic breakdown as AD. The argument to support this conclusion is that chronic brain hypoperfusion, which is found at the early stages of the three dementias reviewed here, will reduce oxygen delivery and lower oxidative phosphorylation promoting a steady decline in the synthesis of the cell energy fuel adenosine triphosphate (ATP). This process is known to lead to oxidative stress. Virtually all neurodegenerative diseases, including FTD, DLB, and CJD, are characterized by oxidative stress that promotes inclusion bodies which differ in structure, location, and origin, as well as which neurological disorder they typify. Inclusion bodies have one thing in common; they are known to diminish autophagic activity, the protective intracellular degradative process that removes malformed proteins, protein aggregates, and damaged subcellular organelles that can disrupt neuronal homeostasis. Neurons are dependent on autophagy for their normal function and survival. When autophagic activity is diminished or impaired in neurons, high levels of unfolded or misfolded proteins overwhelm and downregulate the neuroprotective activity of unfolded protein response which is unable to get rid of dysfunctional organelles such as damaged mitochondria and malformed proteins at the synapse. The endpoint of this neuropathologic process results in damaged synapses, impaired neurotransmission, cognitive decline, and dementia.
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Immunity factor contributes to altered brain functional networks in individuals at risk for Alzheimer's disease: Neuroimaging-genetic evidence. Brain Behav Immun 2016; 56:84-95. [PMID: 26899953 DOI: 10.1016/j.bbi.2016.02.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Revised: 02/14/2016] [Accepted: 02/15/2016] [Indexed: 01/02/2023] Open
Abstract
Clusterin (CLU) is recognized as a secreted protein that is related to the processes of inflammation and immunity in the pathogenesis of Alzheimer's disease (AD). The effects of the risk variant of the C allele at the rs11136000 locus of the CLU gene are associated with variations in the brain structure and function. However, the relationship of the CLU-C allele to architectural disruptions in resting-state networks in amnestic mild cognitive impairment (aMCI) subjects (i.e., individuals with elevated risk of AD) remains relatively unknown. Using resting-state functional magnetic resonance imaging and an imaging genetic approach, this study investigated whether individual brain functional networks, i.e., the default mode network (DMN) and the task-positive network, were modulated by the CLU-C allele (rs11136000) in 50 elderly participants, including 26 aMCI subjects and 24 healthy controls. CLU-by-aMCI interactions were associated with the information-bridging regions between resting-state networks rather than with the DMN itself, especially in cortical midline regions. Interestingly, the complex communications between resting-state networks were enhanced in aMCI subjects with the CLU rs11136000 CC genotype and were modulated by the degree of memory impairment, suggesting a reconstructed balance of the resting-state networks in these individuals with an elevated risk of AD. The neuroimaging-genetic evidence indicates that immunity factors may contribute to alterations in brain functional networks in aMCI. These findings add to the evidence that the CLU gene may represent a potential therapeutic target for slowing disease progression in AD.
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Moretti DV. Electroencephalography-driven approach to prodromal Alzheimer's disease diagnosis: from biomarker integration to network-level comprehension. Clin Interv Aging 2016; 11:897-912. [PMID: 27462146 PMCID: PMC4939982 DOI: 10.2147/cia.s103313] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Decay of the temporoparietal cortex is associated with prodromal Alzheimer's disease (AD). Additionally, shrinkage of the temporoparietal cerebral area has been connected with an increase in α3/α2 electroencephalogram (EEG) power ratio in prodromal AD. Furthermore, a lower regional blood perfusion has been exhibited in patients with a higher α3/α2 proportion when contrasted with low α3/α2 proportion. Furthermore, a lower regional blood perfusion and reduced hippocampal volume has been exhibited in patients with higher α3/α2 when contrasted with lower α3/α2 EEG power ratio. Neuropsychological evaluation, EEG recording, and magnetic resonance imaging were conducted in 74 patients with mild cognitive impairment (MCI). Estimation of cortical thickness and α3/α2 frequency power ratio was conducted for each patient. A subgroup of 27 patients also underwent single-photon emission computed tomography evaluation. In view of α3/α2 power ratio, the patients were divided into three groups. The connections among cortical decay, cerebral perfusion, and memory loss were evaluated by Pearson's r coefficient. Results demonstrated that higher α3/α2 frequency power ratio group was identified with brain shrinkage and cutdown perfusion inside the temporoparietal projections. In addition, decay and cutdown perfusion rate were connected with memory shortfalls in patients with MCI. MCI subgroup with higher α3/α2 EEG power ratio are at a greater risk to develop AD dementia.
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Affiliation(s)
- Davide Vito Moretti
- Rehabilitation in Alzheimer’s Disease Operative Unit, IRCCS San Giovanni di Dio, Fatebenefratelli, Brescia, Italy
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11
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Moretti DV, Benussi L, Fostinelli S, Ciani M, Binetti G, Ghidoni R. Progranulin Mutations Affects Brain Oscillatory Activity in Fronto-Temporal Dementia. Front Aging Neurosci 2016; 8:35. [PMID: 26973510 PMCID: PMC4770190 DOI: 10.3389/fnagi.2016.00035] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 02/10/2016] [Indexed: 12/12/2022] Open
Abstract
Background: Mild cognitive impairment (MCI) is a clinical stage indicating a prodromal phase of dementia. This practical concept could be used also for fronto-temporal dementia (FTD). Progranulin (PGRN) has been recently recognized as a useful diagnostic biomarker for fronto-temporal lobe degeneration (FTLD) due to GRN null mutations. Electroencephalography (EEG) is a reliable tool in detecting brain networks changes. The working hypothesis of the present study is that EEG oscillations could detect different modifications among FTLD stages (FTD-MCI versus overt FTD) as well as differences between GRN mutation carriers versus non-carriers in patients with overt FTD. Materials and Methods: EEG in all patients and PGRN dosage in patients with a clear FTD were detected. The cognitive state has been investigated through mini mental state examination (MMSE). Results: MCI-FTD showed a significant lower spectral power in both alpha and theta oscillations as compared to overt FTD. GRN mutations carriers affected by FTLD show an increase in high alpha and decrease in theta oscillations as compared to non-carriers. Conclusion: EEG frequency rhythms are sensible to different stage of FTD and could detect changes in brain oscillatory activity affected by GRN mutations.
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Affiliation(s)
- Davide V Moretti
- Alzheimer Rehabilitation Research Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia, Italy
| | - Luisa Benussi
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia, Italy
| | - Silvia Fostinelli
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia, Italy
| | - Miriam Ciani
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia, Italy
| | - Giuliano Binetti
- Memory Clinic, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia, Italy
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia, Italy
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