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Cecchetti G, Agosta F, Canu E, Basaia S, Rugarli G, Curti DG, Coraglia F, Cursi M, Spinelli EG, Santangelo R, Caso F, Fanelli GF, Magnani G, Filippi M. Analysis of individual alpha frequency in a large cohort from a tertiary memory center. Eur J Neurol 2024; 31:e16424. [PMID: 39087560 DOI: 10.1111/ene.16424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 06/19/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024]
Abstract
BACKGROUND AND PURPOSE Precise and timely diagnosis is crucial for the optimal use of emerging disease-modifying treatments for Alzheimer disease (AD). Electroencephalography (EEG), which is noninvasive and cost-effective, can capture neural abnormalities linked to various dementias. This study explores the use of individual alpha frequency (IAF) derived from EEG as a diagnostic and prognostic tool in cognitively impaired patients. METHODS This retrospective study included 375 patients from the tertiary Memory Clinic of IRCCS San Raffaele Hospital, Milan, Italy. Participants underwent clinical and neuropsychological assessments, brain imaging, cerebrospinal fluid biomarker analysis, and resting-state EEG. Patients were categorized by amyloid status, the AT(N) classification system, clinical diagnosis, and mild cognitive impairment (MCI) progression to AD dementia. IAF was calculated and compared among study groups. Receiver operating characteristic (ROC) analysis was used to calculate its discriminative performance. RESULTS IAF was higher in amyloid-negative subjects and varied significantly across AT(N) groups. ROC analysis confirmed IAF's ability to distinguish A-T-N- from the A+T+N+ and A+T-N+ groups. IAF was lower in AD and Lewy body dementia patients compared to MCI and other dementia types, with moderate discriminatory capability. Among A+ MCI patients, IAF was significantly lower in those who converted to AD within 2 years compared to stable MCI patients and predicted time to conversion (p < 0.001, R = 0.38). CONCLUSIONS IAF is a valuable tool for dementia diagnosis and prognosis, correlating with amyloid status and neurodegeneration. It effectively predicts MCI progression to AD, supporting its use in early, targeted interventions in the context of disease-modifying treatments.
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Affiliation(s)
- Giordano Cecchetti
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Federica Agosta
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Elisa Canu
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giulia Rugarli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Davide G Curti
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | | | - Marco Cursi
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo G Spinelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Roberto Santangelo
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesca Caso
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Giuseppe Magnani
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Glimmerveen AB, Verhulst MMLH, de Kruijf NLM, van Gils P, Delnoij T, Bonnes J, van Heugten CM, Van Putten MJAM, Hofmeijer J. Resting state EEG relates to short- and long-term cognitive functioning after cardiac arrest. Resuscitation 2024; 201:110253. [PMID: 38797387 DOI: 10.1016/j.resuscitation.2024.110253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Approximately half of cardiac arrest survivors have persistent cognitive impairment. Guidelines recommend early screening to identify patients at risk for cognitive impairment, but there is no consensus on the best screening method. We aimed to identify quantitative EEG measures relating with short- and long-term cognitive function after cardiac arrest for potential to cognitive outcome prediction. METHODS We analyzed data from a prospective longitudinal multicenter cohort study designed to develop a prediction model for cognitive outcome after cardiac arrest. For the current analysis, we used twenty-minute EEG registrations from 80 patients around one week after cardiac arrest. We calculated power spectral density, normalized alpha-to-theta ratio (nATR), peak frequency, and center of gravity (CoG) of this peak frequency. We related these with global cognitive functioning (scores on the Montreal Cognitive Assessment (MoCA)) at one week, three and twelve months follow-up with multivariate mixed effect models, and with performance on standard neuropsychological examination at twelve months using Pearson correlation coefficients. RESULTS Each individual EEG parameter related to MoCA at one week (βnATR = 7.36; P < 0.01; βpeak frequency = 1.73, P < 0.01; βCoG = -9.88, P < 0.01). The nATR also related with the MoCA at three months ((βnATR = 2.49; P 0.01). No EEG metrics significantly related to the MoCA score at twelve months. nATR and peak frequency related with memory performance at twelve months. Results were consistent in sensitivity analyses. CONCLUSION Early resting-state EEG parameters relate with short-term global cognitive functioning and with memory function at one year after cardiac arrest. Additional predictive values in multimodal prediction models need further study.
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Affiliation(s)
- A B Glimmerveen
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
| | - M M L H Verhulst
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - N L M de Kruijf
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - P van Gils
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Maastrich University, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht, The Netherlands; Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands
| | - T Delnoij
- Department of Intensive Care Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - J Bonnes
- Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - C M van Heugten
- Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands; Maastricht University, Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht, The Netherlands
| | - M J A M Van Putten
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - J Hofmeijer
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
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Yu WY, Sun TH, Hsu KC, Wang CC, Chien SY, Tsai CH, Yang YW. Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information. Comput Biol Med 2024; 176:108621. [PMID: 38763067 DOI: 10.1016/j.compbiomed.2024.108621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.
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Affiliation(s)
- Wei-Yang Yu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Chia-Chun Wang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chon-Haw Tsai
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan; Neuroscience Laboratory, Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Neuroscience and Brain Disease Center, College of Medicine, China Medical University, 40402, Taichung, Taiwan
| | - Yu-Wan Yang
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan.
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Niu X, Wang Y, Zhang X, Wang Y, Shao W, Chen L, Yang Z, Peng D. Quantitative electroencephalography (qEEG), apolipoprotein A-I (APOA-I), and apolipoprotein epsilon 4 (APOE ɛ4) alleles for the diagnosis of mild cognitive impairment and Alzheimer's disease. Neurol Sci 2024; 45:547-556. [PMID: 37673807 DOI: 10.1007/s10072-023-07028-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 08/19/2023] [Indexed: 09/08/2023]
Abstract
INTRODUCTION Alzheimer's disease (AD) is the most common type of dementia. Amnestic mild cognitive impairment (aMCI), a pre-dementia stage is an important stage for early diagnosis and intervention. This study aimed to investigate the diagnostic value of qEEG, APOA-I, and APOE ɛ4 allele in aMCI and AD patients and found the correlation between qEEG (Delta + Theta)/(Alpha + Beta) ratio (DTABR) and different cognitive domains. METHODS All participants were divided into three groups: normal controls (NCs), aMCI, and AD, and all received quantitative electroencephalography (qEEG), neuropsychological scale assessment, apolipoprotein epsilon 4 (APOE ɛ4) alleles, and various blood lipid indicators. Different statistical methods were used for different data. RESULTS The cognitive domains except executive ability were all negatively correlated with DTABR in different brain regions while executive ability was positively correlated with DTABR in several brain regions, although without statistical significance. The consequences confirmed that the DTABR of each brain area were related to MMSE, MoCA, instantaneous memory, and the language ability (p < 0.05), and the DTABR in the occipital area was relevant to all cognitive domains (p < 0.01) except executive function (p = 0.272). Also, occipital DTABR was most correlated with language domain when tested by VFT with a moderate level (r = 0.596, p < 0.001). There were significant differences in T3, T5, and P3 DTABR between both AD and NC and aMCI and NCs. As for aMCI diagnosis, the maximum AUC was achieved when using T3 combined with APOA-I and APOE ε4 (0.855) and the maximum AUC was achieved when using T5 combined with APOA-I and APOE ε4 (0.889) for AD diagnosis. CONCLUSION These findings highlight that APOA-I, APOE ɛ4, and qEEG play an important role in aMCI and AD diagnosis. During AD continuum, qEEG DTABR should be taken into consideration for the early detection of AD risk.
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Affiliation(s)
- Xiaoqian Niu
- Department of Neurology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Yuye Wang
- Department of Neurology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
- China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiangfei Zhang
- Department of Neurology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
| | - Yu Wang
- Department of Neurology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Wen Shao
- Department of Neurology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
| | - Leian Chen
- Department of Neurology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
- China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Ziyuan Yang
- Department of Neurology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Dantao Peng
- Department of Neurology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing, 100029, China.
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
- China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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Zawiślak-Fornagiel K, Ledwoń D, Bugdol M, Grażyńska A, Ślot M, Tabaka-Pradela J, Bieniek I, Siuda J. Quantitative EEG Spectral and Connectivity Analysis for Cognitive Decline in Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2024; 97:1235-1247. [PMID: 38217593 DOI: 10.3233/jad-230485] [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: 01/15/2024]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is considered to be the borderline of cognitive changes associated with aging and very early dementia. Cognitive functions in MCI can improve, remain stable or progress to clinically probable AD. Quantitative electroencephalography (qEEG) can become a useful tool for using the analytical techniques to quantify EEG patterns indicating cognitive impairment. OBJECTIVE The aim of our study was to assess spectral and connectivity analysis of the EEG resting state activity in amnestic MCI (aMCI) patients in comparison with healthy control group (CogN). METHODS 30 aMCI patients and 23 CogN group, matched by age and education, underwent equal neuropsychological assessment and EEG recording, according to the same protocol. RESULTS qEEG spectral analysis revealed decrease of global relative beta band power and increase of global relative theta and delta power in aMCI patients. Whereas, decreased coherence in centroparietal right area considered to be an early qEEG biomarker of functional disconnection of the brain network in aMCI patients. In conclusion, the demonstrated changes in qEEG, especially, the coherence patterns are specific biomarkers of cognitive impairment in aMCI. CONCLUSIONS Therefore, qEEG measurements appears to be a useful tool that complements neuropsychological diagnostics, assessing the risk of progression and provides a basis for possible interventions designed to improve cognitive functions or even inhibit the progression of the disease.
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Affiliation(s)
- Katarzyna Zawiślak-Fornagiel
- Department of Neurology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
| | - Daniel Ledwoń
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
| | - Monika Bugdol
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
| | - Anna Grażyńska
- Department of Imaging Diagnostics and Interventional Radiology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
| | - Maciej Ślot
- Department of Solid State Physics, Faculty of Physics and Applied Computer Science, University of Łódź, Łódź, Poland
| | - Justyna Tabaka-Pradela
- Department of Neurology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
| | - Izabela Bieniek
- Department of Neurology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
| | - Joanna Siuda
- Department of Neurology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
- Department of Neurology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
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Donaghy PC, Carrarini C, Ferreira D, Habich A, Aarsland D, Babiloni C, Bayram E, Kane JP, Lewis SJ, Pilotto A, Thomas AJ, Bonanni L. Research diagnostic criteria for mild cognitive impairment with Lewy bodies: A systematic review and meta-analysis. Alzheimers Dement 2023; 19:3186-3202. [PMID: 37096339 PMCID: PMC10695683 DOI: 10.1002/alz.13105] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 04/26/2023]
Abstract
INTRODUCTION Operationalized research criteria for mild cognitive impairment with Lewy bodies (MCI-LB) were published in 2020. The aim of this systematic review and meta-analysis was to review the evidence for the diagnostic clinical features and biomarkers in MCI-LB set out in the criteria. METHODS MEDLINE, PubMed, and Embase were searched on 9/28/22 for relevant articles. Articles were included if they presented original data reporting the rates of diagnostic features in MCI-LB. RESULTS Fifty-seven articles were included. The meta-analysis supported the inclusion of the current clinical features in the diagnostic criteria. Evidence for striatal dopaminergic imaging and meta-iodobenzylguanidine cardiac scintigraphy, though limited, supports their inclusion. Quantitative electroencephalogram (EEG) and fluorodeoxyglucose positron emission tomography (PET) show promise as diagnostic biomarkers. DISCUSSION The available evidence largely supports the current diagnostic criteria for MCI-LB. Further evidence will help refine the diagnostic criteria and understand how best to apply them in clinical practice and research. HIGHLIGHTS A meta-analysis of the diagnostic features of MCI-LB was carried out. The four core clinical features were more common in MCI-LB than MCI-AD/stable MCI. Neuropsychiatric and autonomic features were also more common in MCI-LB. More evidence is needed for the proposed biomarkers. FDG-PET and quantitative EEG show promise as diagnostic biomarkers in MCI-LB.
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Affiliation(s)
- Paul C Donaghy
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Claudia Carrarini
- Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy
- IRCCS San Raffaele Pisana, Rome, Italy
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Annegret Habich
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Centre for Age-Related Diseases, Stavanger University Hospital, Stavanger, Norway
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
- Hospital San Raffaele of Cassino, Cassino, Italy
| | - Ece Bayram
- Parkinson and Other Movement Disorders Center, Department of Neurosciences, University of California San Diego, California, USA
| | - Joseph Pm Kane
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Simon Jg Lewis
- Brain and Mind Centre, School of Medical Sciences, University of Sydney, Sydney, Australia
| | - Andrea Pilotto
- Department of Clinical and Experimental Sciences, Neurology Unit, University of Brescia, Brescia, Italy
| | - Alan J Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
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Wijaya A, Setiawan NA, Ahmad AH, Zakaria R, Othman Z. Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA). AIMS Neurosci 2023; 10:154-171. [PMID: 37426780 PMCID: PMC10323261 DOI: 10.3934/neuroscience.2023012] [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: 04/17/2023] [Revised: 05/27/2023] [Accepted: 06/12/2023] [Indexed: 07/11/2023] Open
Abstract
Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field.
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Affiliation(s)
- Adi Wijaya
- Department of Health Information Management, Universitas Indonesia Maju, Jakarta, Indonesia
| | - Noor Akhmad Setiawan
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Asma Hayati Ahmad
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
| | - Rahimah Zakaria
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
| | - Zahiruddin Othman
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
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Parker JL, Vakulin A, Melaku YA, Wittert GA, Martin SA, D’Rozario AL, Catcheside PG, Lechat B, Toson B, Teare AJ, Appleton SL, Adams RJ. Associations of Baseline Sleep Microarchitecture with Cognitive Function After 8 Years in Middle-Aged and Older Men from a Community-Based Cohort Study. Nat Sci Sleep 2023; 15:389-406. [PMID: 37252206 PMCID: PMC10225127 DOI: 10.2147/nss.s401655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/17/2023] [Indexed: 05/31/2023] Open
Abstract
Purpose Prospective studies examining associations between baseline sleep microarchitecture and future cognitive function recruited from small samples with predominantly short follow-up. This study examined sleep microarchitecture predictors of cognitive function (visual attention, processing speed, and executive function) after 8 years in community-dwelling men. Patients and Methods Florey Adelaide Male Ageing Study participants (n=477) underwent home-based polysomnography (2010-2011), with 157 completing baseline (2007-2010) and follow-up (2018-2019) cognitive assessments (trail-making tests A [TMT-A] and B [TMT-B] and the standardized mini-mental state examination [SMMSE]). Whole-night F4-M1 sleep EEG recordings were processed following artifact exclusion, and quantitative EEG characteristics were obtained using validated algorithms. Associations between baseline sleep microarchitecture and future cognitive function (visual attention, processing speed, and executive function) were examined using linear regression models adjusted for baseline obstructive sleep apnoea, other risk factors, and cognition. Results The final sample included men aged (mean [SD]) 58.9 (8.9) years at baseline, overweight (BMI 28.5 [4.2] kg/m2), and well educated (75.2% ≥Bachelor, Certificate, or Trade), with majorly normal baseline cognition. Median (IQR) follow-up was 8.3 (7.9, 8.6) years. In adjusted analyses, NREM and REM sleep EEG spectral power was not associated with TMT-A, TMT-B, or SMMSE performance (all p>0.05). A significant association of higher N3 sleep fast spindle density with worse TMT-B performance (B=1.06, 95% CI [0.13, 2.00], p=0.026) did not persist following adjustment for baseline TMT-B performance. Conclusion In this sample of community-dwelling men, sleep microarchitecture was not independently associated with visual attention, processing speed, or executive function after 8 years.
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Affiliation(s)
- Jesse L Parker
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia
| | - Andrew Vakulin
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Sydney, NSW, Australia
| | - Yohannes Adama Melaku
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia
| | - Gary A Wittert
- Freemasons Centre for Male Health and Wellbeing, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Sean A Martin
- Freemasons Centre for Male Health and Wellbeing, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Angela L D’Rozario
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Sydney, NSW, Australia
- The University of Sydney, Faculty of Science, School of Psychology, Sydney, NSW, Australia
| | - Peter G Catcheside
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia
| | - Bastien Lechat
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia
| | - Barbara Toson
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Alison J Teare
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia
| | - Sarah L Appleton
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Robert J Adams
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- Respiratory and Sleep Services, Southern Adelaide Local Health Network, Adelaide, SA, Australia
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Brown CW, Chen HY, Panegyres PK. Electroencephalography in young onset dementia. BMC Neurol 2023; 23:202. [PMID: 37221470 DOI: 10.1186/s12883-023-03248-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/16/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Young onset dementia (YOD) is a major diagnostic and management problem. METHODS We set out to explore if electroencephalography (EEG) might be useful in the diagnosis of young onset Alzheimer's disease (YOAD) and young onset frontotemporal dementia (YOFTD). The ARTEMIS project is a 25-year prospective study of YOD based in Perth, Western Australia. 231 participants were included: YOAD: n = 103, YOFTD: n = 28, controls: n = 100. EEGs were performed prospectively, with 30-minute recording time for each subject, without knowledge of diagnosis or other diagnostic data. RESULTS 80.9% of patients with YOD had abnormal EEGs (P < 0.00001). Slow wave changes were more frequent in YOAD that YOFTD (P < 0.00001), but no difference in the frequency of epileptiform activity (P = 0.32), with 38.8% of YOAD and 28.6% of YOFTD patients having epileptiform activity. Slow wave changes were more generalized in YOAD (P = 0.001). Slow wave changes and epileptiform activity were not sensitive to the diagnosis of YOD, but highly specific (97-99%). The absence of slow wave changes and epileptiform activity had a 100% negative predictive value and likelihood radio 0.14 and 0.62 respectively, meaning that those without slow wave changes or epileptiform activity had low probability of having YOD. No relationship was established between EEG findings and the patient's presenting problem. Eleven patients with YOAD developed seizures during the study, and only one with YOFTD. CONCLUSIONS The EEG is highly specific for the diagnosis of YOD with the absence of slow wave changes and epileptiform phenomena making the diagnosis unlikely, with 100% negative predictive value and with low probability for the dementia diagnosis.
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Affiliation(s)
- Casey W Brown
- Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, 6009, Australia
| | - Huei-Yang Chen
- Neurodegenerative Disorders Research Pty Ltd, 4 Lawrence Avenue, West Perth, West Perth, WA, 6005, Australia
| | - Peter K Panegyres
- Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, 6009, Australia.
- Neurodegenerative Disorders Research Pty Ltd, 4 Lawrence Avenue, West Perth, West Perth, WA, 6005, Australia.
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Yeung MK, Chau AKY, Chiu JYC, Shek JTL, Leung JPY, Wong TCH. Differential and subtype-specific neuroimaging abnormalities in amnestic and nonamnestic mild cognitive impairment: A systematic review and meta-analysis. Ageing Res Rev 2022; 80:101675. [PMID: 35724862 DOI: 10.1016/j.arr.2022.101675] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 06/09/2022] [Accepted: 06/14/2022] [Indexed: 11/25/2022]
Abstract
While mild cognitive impairment (MCI) has been classified into amnestic MCI (aMCI) and nonamnestic MCI (naMCI), the neuropathological bases of these two subtypes remain elusive. Here, we performed a systematic review and meta-analysis to determine the subtype specificity of neuroimaging abnormalities in MCI and to identify neural features that may differ between aMCI and naMCI. We synthesized 50 studies that used common neuroimaging modalities, including magnetic resonance imaging and positron emission tomography, to compare brain atrophy, white matter abnormalities, cortical thinning, cerebral hypometabolism, amyloid/tau deposition, or other features among aMCI, naMCI, and normal cognition. Compared with normal cognition, aMCI shows diverse neuroimaging abnormalities of large effect sizes. In contrast, naMCI exhibits restricted abnormalities of small effect sizes. Some features, including medial temporal lobe atrophy and white matter abnormalities, are shared by the two MCI subtypes. Overall, brain abnormalities are worse, if not similar, in aMCI than in naMCI. The only neuroimaging abnormality specific to aMCI is increased amyloid burden; no feature specific to naMCI was found. Taken together, our findings have elucidated the neuropathological changes that occur in aMCI and naMCI. Clarifying the neuroimaging profiles of aMCI and naMCI can improve the early identification, differentiation, and intervention of prodromal dementia.
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Affiliation(s)
- Michael K Yeung
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China; University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
| | - Anson Kwok-Yun Chau
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Jason Yin-Chuen Chiu
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Jay Tsz-Lok Shek
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Jody Po-Yi Leung
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Toby Chun-Ho Wong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
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