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Chen X, Huang Y, Yang S, Wang S, Chen L, Yu X, Gan N, Huang S. In-situ nanozyme catalytic amplification coupled with a universal antibody orientation strategy based electrochemical immunosensor for AD-related biomarker. Biosens Bioelectron 2024; 266:116738. [PMID: 39241336 DOI: 10.1016/j.bios.2024.116738] [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: 07/28/2024] [Revised: 08/26/2024] [Accepted: 09/02/2024] [Indexed: 09/09/2024]
Abstract
An in-situ nanozyme signal tag combined with a DNA-mediated universal antibody-oriented strategy was proposed to establish a high-performance immunosensing platform for Alzheimer's disease (AD)-related biomarker detection. Briefly, a Zr-based metal-organic framework (MOF) with peroxidase (POD)-like activity was synthesized to encapsulating the electroactive molecule methylene blue (MB), and subsequently modified with a layer of gold nanoparticles on its surface. This led to the creation of double POD-like activity nanozymes surrounding the MB molecule to form a nanozyme signal tag. A large number of hydroxyl radicals were generated by the nanozyme signal tag with the help of H2O2, which catalyzed MB molecules in situ to achieve efficient signal amplification. Subsequently, a DNA-aptamer-mediated universal antibody-oriented strategy was proposed to enhance the binding efficiency for the antigen (target). Meanwhile, a poly adenine was incorporated at the end of the aptamer, facilitating binding to the gold electrode and providing anti-fouling properties due to the hydrophilicity of the phosphate group. Under optimal conditions, this platform was successfully employed for highly sensitive detection of AD-associated tau protein and BACE1, achieving limits of detection with concentrations of 3.34 fg/mL and 1.67 fg/mL, respectively. It is worth mentioning that in the tau immunosensing mode, 20 clinical samples from volunteers of varying ages were analyzed, revealing significantly higher tau expression levels in the blood samples of elderly volunteers compared to young volunteers. This suggests that the developed strategy holds great promise for early AD diagnosis.
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Affiliation(s)
- Xiyu Chen
- NMPA Key Laboratory for Clinical Research and Evaluation of Drug for Thoracic Diseases, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China
| | - Yang Huang
- NMPA Key Laboratory for Clinical Research and Evaluation of Drug for Thoracic Diseases, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China
| | - Shuo Yang
- NMPA Key Laboratory for Clinical Research and Evaluation of Drug for Thoracic Diseases, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China
| | - Sheng Wang
- NMPA Key Laboratory for Clinical Research and Evaluation of Drug for Thoracic Diseases, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China
| | - Lin Chen
- Department of Pharmacy, Affiliated Haikou Hospital of Xiangya Medical College, Central South University, Haikou, Hainan, 570208, China
| | - Xiyong Yu
- NMPA Key Laboratory for Clinical Research and Evaluation of Drug for Thoracic Diseases, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China.
| | - Ning Gan
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang, 315211, China; College of Public Health, Guangdong Pharmaceutical University, Guangzhou, 510310, China.
| | - Shengfeng Huang
- NMPA Key Laboratory for Clinical Research and Evaluation of Drug for Thoracic Diseases, School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China.
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Zandbagleh A, Miltiadous A, Sanei S, Azami H. Beta-to-Theta Entropy Ratio of EEG in Aging, Frontotemporal Dementia, and Alzheimer's Dementia. Am J Geriatr Psychiatry 2024; 32:1361-1382. [PMID: 39004533 DOI: 10.1016/j.jagp.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND Aging, frontotemporal dementia (FTD), and Alzheimer's dementia (AD) manifest electroencephalography (EEG) alterations, particularly in the beta-to-theta power ratio derived from linear power spectral density (PSD). Given the brain's nonlinear nature, the EEG nonlinear features could provide valuable physiological indicators of aging and cognitive impairment. Multiscale dispersion entropy (MDE) serves as a sensitive nonlinear metric for assessing the information content in EEGs across biologically relevant time scales. OBJECTIVE To compare the MDE-derived beta-to-theta entropy ratio with its PSD-based counterpart to detect differences between healthy young and elderly individuals and between different dementia subtypes. METHODS Scalp EEG recordings were obtained from two datasets: 1) Aging dataset: 133 healthy young and 65 healthy older adult individuals; and 2) Dementia dataset: 29 age-matched healthy controls (HC), 23 FTD, and 36 AD participants. The beta-to-theta ratios based on MDE vs. PSD were analyzed for both datasets. Finally, the relationships between cognitive performance and the beta-to-theta ratios were explored in HC, FTD, and AD. RESULTS In the Aging dataset, older adults had significantly higher beta-to-theta entropy ratios than young individuals. In the Dementia dataset, this ratio outperformed the beta-to-theta PSD approach in distinguishing between HC, FTD, and AD. The AD participants had a significantly lower beta-to-theta entropy ratio than FTD, especially in the temporal region, unlike its corresponding PSD-based ratio. The beta-to-theta entropy ratio correlated significantly with cognitive performance. CONCLUSION Our study introduces the beta-to-theta entropy ratio using nonlinear MDE for EEG analysis, highlighting its potential as a sensitive biomarker for aging and cognitive impairment.
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Affiliation(s)
- Ahmad Zandbagleh
- School of Electrical Engineering (AZ), Iran University of Science and Technology, Tehran, Iran
| | - Andreas Miltiadous
- Department of Informatics and Telecommunications (AM), University of Ioannina, Arta, Greece
| | - Saeid Sanei
- Electrical and Electronic Engineering Department (SS), Imperial College London, London, UK
| | - Hamed Azami
- Centre for Addiction and Mental Health (HA), University of Toronto, Toronto, ON, Canada.
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Baez S, Hernandez H, Moguilner S, Cuadros J, Santamaria‐Garcia H, Medel V, Migeot J, Cruzat J, Valdes‐Sosa PA, Lopera F, González‐Hernández A, Bonilla‐Santos J, Gonzalez‐Montealegre RA, Aktürk T, Legaz A, Altschuler F, Fittipaldi S, Yener GG, Escudero J, Babiloni C, Lopez S, Whelan R, Lucas AAF, Huepe D, Soto‐Añari M, Coronel‐Oliveros C, Herrera E, Abasolo D, Clark RA, Güntekin B, Duran‐Aniotz C, Parra MA, Lawlor B, Tagliazucchi E, Prado P, Ibanez A. Structural inequality and temporal brain dynamics across diverse samples. Clin Transl Med 2024; 14:e70032. [PMID: 39360669 PMCID: PMC11447638 DOI: 10.1002/ctm2.70032] [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: 06/18/2024] [Revised: 09/02/2024] [Accepted: 09/10/2024] [Indexed: 10/04/2024] Open
Abstract
BACKGROUND Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored. METHODS Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed. FINDINGS Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions. CONCLUSION These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.
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Affiliation(s)
- Sandra Baez
- Departamento de PsicologíaUniversidad de los AndesBogotaColombia
- Global Brain Health Institute (GBHI)University of CaliforniaSan FranciscoCaliforniaUSA
- Global Brain Health Institute (GBHI)Trinity College DublinDublinIreland
| | - Hernan Hernandez
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
| | - Sebastian Moguilner
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- Harvard Medical SchoolHarvard UniversityBostonMassachusettsUSA
| | - Jhosmary Cuadros
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa MaríaValparaísoChile
- Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del TáchiraSan CristóbalVenezuela
| | - Hernando Santamaria‐Garcia
- PhD Program in NeurosciencePontificia Universidad JaverianaBogotaColombia
- Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio BogotáSan IgnacioColombia
| | - Vicente Medel
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
| | - Joaquín Migeot
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
| | - Josephine Cruzat
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
| | | | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, University of AntioquiaMedellínColombia
| | | | | | | | - Tuba Aktürk
- Department of BiophysicsSchool of MedicineIstanbul Medipol UniversityIstanbulTurkey
| | - Agustina Legaz
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- Cognitive Neuroscience Center, Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
- Facultad de Psicología, Universidad Nacional de CórdobaCórdobaArgentina
| | - Florencia Altschuler
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- Cognitive Neuroscience Center, Universidad de San AndrésBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
| | - Sol Fittipaldi
- Global Brain Health Institute (GBHI)University of CaliforniaSan FranciscoCaliforniaUSA
- Global Brain Health Institute (GBHI)Trinity College DublinDublinIreland
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- School of Psychology, Trinity College DublinDublinIreland
| | - Görsev G. Yener
- Faculty of Medicine, Izmir University of EconomicsIzmirTurkey
- Brain Dynamics Multidisciplinary Research CenterDokuz Eylul UniversityIzmirTurkey
- Izmir Biomedicine and Genome CenterIzmirTurkey
| | - Javier Escudero
- School of Engineering, Institute for Imaging, Data and Communications, University of EdinburghScotlandUK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology ‘V. Erspamer’Sapienza University of RomeRomeItaly
- Hospital San Raffaele CassinoCassinoFrosinoneItaly
| | - Susanna Lopez
- Department of Physiology and Pharmacology ‘V. Erspamer’Sapienza University of RomeRomeItaly
| | - Robert Whelan
- Global Brain Health Institute (GBHI)University of CaliforniaSan FranciscoCaliforniaUSA
- Global Brain Health Institute (GBHI)Trinity College DublinDublinIreland
- School of Psychology, Trinity College DublinDublinIreland
| | - Alberto A Fernández Lucas
- Department of Legal MedicinePsychiatry and Pathology at the Complutense University of MadridMadridSpain
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo IbáñezPenalolenChile
| | | | - Carlos Coronel‐Oliveros
- Global Brain Health Institute (GBHI)University of CaliforniaSan FranciscoCaliforniaUSA
- Global Brain Health Institute (GBHI)Trinity College DublinDublinIreland
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de ValparaísoValparaísoChile
| | - Eduar Herrera
- Departamento de Estudios PsicológicosUniversidad IcesiCaliColombia
| | - Daniel Abasolo
- Faculty of Engineering and Physical Sciences, Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, University of SurreyGuildfordUK
| | - Ruaridh A. Clark
- Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowUK
- Department of Electronic and Electrical EngineeringCentre for Signal and Image ProcessingUniversity of StrathclydeGlasgowUK
| | - Bahar Güntekin
- Department of BiophysicsSchool of MedicineIstanbul Medipol UniversityIstanbulTurkey
- Health Sciences and Technology Research Institute (SABITA)Istanbul Medipol UniversityIstanbulTurkey
| | - Claudia Duran‐Aniotz
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
| | - Mario A. Parra
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- Department of Psychological Sciences and HealthUniversity of StrathclydeGlasgowUK
| | - Brian Lawlor
- Global Brain Health Institute (GBHI)University of CaliforniaSan FranciscoCaliforniaUSA
- Global Brain Health Institute (GBHI)Trinity College DublinDublinIreland
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- Department of Psychological Sciences and HealthUniversity of StrathclydeGlasgowUK
| | - Enzo Tagliazucchi
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- University of Buenos AiresBuenos AiresArgentina
| | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San SebastiánSantiagoChile
| | - Agustin Ibanez
- Global Brain Health Institute (GBHI)University of CaliforniaSan FranciscoCaliforniaUSA
- Global Brain Health Institute (GBHI)Trinity College DublinDublinIreland
- Latin American Brain Health InstituteUniversidad Adolfo IbañezSantiago de ChileChile
- Cognitive Neuroscience Center, Universidad de San AndrésBuenos AiresArgentina
- Trinity College Dublin, The University of DublinDublinIreland
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Trabado-Fernández A, García-Colomo A, Cuadrado-Soto E, Peral-Suárez Á, Salas-González MD, Lorenzo-Mora AM, Aparicio A, Delgado-Losada ML, Maestú-Unturbe F, López-Sobaler AM. Association of a DASH diet and magnetoencephalography in dementia-free adults with different risk levels of Alzheimer's disease. GeroScience 2024:10.1007/s11357-024-01361-3. [PMID: 39354239 DOI: 10.1007/s11357-024-01361-3] [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: 07/15/2024] [Accepted: 09/18/2024] [Indexed: 10/03/2024] Open
Abstract
This study explored how adherence to the DASH diet relates to electrophysiological measures in individuals at varying Alzheimer's disease (AD) risk due to family history (FH). There were 179 dementia-free subjects. DASH index was calculated, and participants were classified into different DASH adherence groups. Tertiles of relative alpha power in default mode network (DMN) regions were calculated. Multivariate logistic regression models were used to examine the association. Lower DASH adherence was associated with decreased odds of higher relative alpha power in the DMN, observed across the entire sample and specifically among those without a FH of AD. Logistic regression models indicated that participants with poorer DASH adherence had a reduced likelihood of elevated DMN alpha power, potentially influenced by vascular and amyloid-beta mechanisms. These findings underscore the dietary pattern's potential role in neural activity modulation, particularly in individuals not genetically predisposed to AD.
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Affiliation(s)
- Alfredo Trabado-Fernández
- Department of Nutrition and Food Science, Faculty of Pharmacy, Complutense University of Madrid, Pl. de Ramón y Cajal S/N, 28040, Madrid, Spain
| | - Alejandra García-Colomo
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Faculty of Psychology, Complutense University of Madrid, 28223, Madrid, Spain
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
| | - Esther Cuadrado-Soto
- Department of Nutrition and Food Science, Faculty of Pharmacy, Complutense University of Madrid, Pl. de Ramón y Cajal S/N, 28040, Madrid, Spain.
- VALORNUT Research Group, Department of Nutrition and Food Science, Complutense University of Madrid, 28040, Madrid, Spain.
| | - África Peral-Suárez
- Department of Nutrition and Food Science, Faculty of Pharmacy, Complutense University of Madrid, Pl. de Ramón y Cajal S/N, 28040, Madrid, Spain
- VALORNUT Research Group, Department of Nutrition and Food Science, Complutense University of Madrid, 28040, Madrid, Spain
| | - María Dolores Salas-González
- Department of Nutrition and Food Science, Faculty of Pharmacy, Complutense University of Madrid, Pl. de Ramón y Cajal S/N, 28040, Madrid, Spain
- VALORNUT Research Group, Department of Nutrition and Food Science, Complutense University of Madrid, 28040, Madrid, Spain
| | - Ana María Lorenzo-Mora
- Department of Nutrition and Food Science, Faculty of Pharmacy, Complutense University of Madrid, Pl. de Ramón y Cajal S/N, 28040, Madrid, Spain
- Department of Nursing and Nutrition, Faculty of Biomedical Sciences, Universidad Europea de Madrid, 28670, Villaviciosa de Odón, Madrid, Spain
| | - Aránzazu Aparicio
- Department of Nutrition and Food Science, Faculty of Pharmacy, Complutense University of Madrid, Pl. de Ramón y Cajal S/N, 28040, Madrid, Spain
- VALORNUT Research Group, Department of Nutrition and Food Science, Complutense University of Madrid, 28040, Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), 28040, Madrid, Spain
| | - María Luisa Delgado-Losada
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Faculty of Psychology, Complutense University of Madrid, 28223, Madrid, Spain
- VALORNUT Research Group, Department of Nutrition and Food Science, Complutense University of Madrid, 28040, Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), 28040, Madrid, Spain
| | - Fernando Maestú-Unturbe
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Faculty of Psychology, Complutense University of Madrid, 28223, Madrid, Spain
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, 28223, Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), 28040, Madrid, Spain
| | - Ana M López-Sobaler
- Department of Nutrition and Food Science, Faculty of Pharmacy, Complutense University of Madrid, Pl. de Ramón y Cajal S/N, 28040, Madrid, Spain
- VALORNUT Research Group, Department of Nutrition and Food Science, Complutense University of Madrid, 28040, Madrid, Spain
- Health Research Institute of the Hospital Clínico San Carlos (IdISSC), 28040, Madrid, Spain
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Carrarini C, Nardulli C, Titti L, Iodice F, Miraglia F, Vecchio F, Rossini PM. Neuropsychological and electrophysiological measurements for diagnosis and prediction of dementia: a review on Machine Learning approach. Ageing Res Rev 2024; 100:102417. [PMID: 39002643 DOI: 10.1016/j.arr.2024.102417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/29/2024] [Accepted: 07/07/2024] [Indexed: 07/15/2024]
Abstract
INTRODUCTION Emerging and advanced technologies in the field of Artificial Intelligence (AI) represent promising methods to predict and diagnose neurodegenerative diseases, such as dementia. By using multimodal approaches, Machine Learning (ML) seems to provide a better understanding of the pathological mechanisms underlying the onset of dementia. The purpose of this review was to discuss the current ML application in the field of neuropsychology and electrophysiology, exploring its results in both prediction and diagnosis for different forms of dementia, such as Alzheimer's disease (AD), Vascular Dementia (VaD), Dementia with Lewy bodies (DLB), and Frontotemporal Dementia (FTD). METHODS Main ML-based papers focusing on neuropsychological assessments and electroencephalogram (EEG) studies were analyzed for each type of dementia. RESULTS An accuracy ranging between 70 % and 90 % or even more was observed in all neurophysiological and electrophysiological results trained by ML. Among all forms of dementia, the most significant findings were observed for AD. Relevant results were mostly related to diagnosis rather than prediction, because of the lack of longitudinal studies with appropriate follow-up duration. However, it remains unclear which ML algorithm performs better in diagnosing or predicting dementia. CONCLUSIONS Neuropsychological and electrophysiological measurements, together with ML analysis, may be considered as reliable instruments for early detection of dementia.
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Affiliation(s)
- Claudia Carrarini
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Neuroscience, Catholic University of Sacred Heart, Largo Agostino Gemelli 8, Rome 00168, Italy
| | - Cristina Nardulli
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Laura Titti
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Francesco Iodice
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Francesca Miraglia
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Theoretical and Applied Sciences, eCampus University, via Isimbardi 10, Novedrate 22060, Italy
| | - Fabrizio Vecchio
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Theoretical and Applied Sciences, eCampus University, via Isimbardi 10, Novedrate 22060, Italy
| | - Paolo Maria Rossini
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy.
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Ehteshamzad S. Assessing the Potential of EEG in Early Detection of Alzheimer's Disease: A Systematic Comprehensive Review (2000-2023). J Alzheimers Dis Rep 2024; 8:1153-1169. [PMID: 39247874 PMCID: PMC11380315 DOI: 10.3233/adr-230159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 07/08/2024] [Indexed: 09/10/2024] Open
Abstract
Background As the prevalence of Alzheimer's disease (AD) grows with an aging population, the need for early diagnosis has led to increased focus on electroencephalography (EEG) as a non-invasive diagnostic tool. Objective This review assesses advancements in EEG analysis, including the application of machine learning, for detecting AD from 2000 to 2023. Methods Following PRISMA guidelines, a search across major databases resulted in 25 studies that met the inclusion criteria, focusing on EEG's application in AD diagnosis and the use of novel signal processing and machine learning techniques. Results Progress in EEG analysis has shown promise for early AD identification, with techniques like Hjorth parameters and signal compressibility enhancing detection capabilities. Machine learning has improved the precision of differential diagnosis between AD and mild cognitive impairment. However, challenges in standardizing EEG methodologies and data privacy remain. Conclusions EEG stands out as a valuable tool for early AD detection, with the potential to integrate into multimodal diagnostic approaches. Future research should aim to standardize EEG procedures and explore collaborative, privacy-preserving research methods.
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Affiliation(s)
- Sharareh Ehteshamzad
- Department of Biomedical Engineering, Hygiene Faculty, Medical Branch, Islamic Azad University, Tehran, Iran
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Meghdadi AH, Salat D, Hamilton J, Hong Y, Boeve BF, St Louis EK, Verma A, Berka C. EEG and ERP biosignatures of mild cognitive impairment for longitudinal monitoring of early cognitive decline in Alzheimer's disease. PLoS One 2024; 19:e0308137. [PMID: 39116138 PMCID: PMC11309464 DOI: 10.1371/journal.pone.0308137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 07/12/2024] [Indexed: 08/10/2024] Open
Abstract
Cognitive decline in Alzheimer's disease is associated with electroencephalographic (EEG) biosignatures even at early stages of mild cognitive impairment (MCI). The aim of this work is to provide a unified measure of cognitive decline by aggregating biosignatures from multiple EEG modalities and to evaluate repeatability of the composite measure at an individual level. These modalities included resting state EEG (eyes-closed) and two event-related potential (ERP) tasks on visual memory and attention. We compared individuals with MCI (n = 38) to age-matched healthy controls HC (n = 44). In resting state EEG, the MCI group exhibited higher power in Theta (3-7Hz) and lower power in Beta (13-20Hz) frequency bands. In both ERP tasks, the MCI group exhibited reduced ERP late positive potential (LPP), delayed ERP early component latency, slower reaction time, and decreased response accuracy. Cluster-based permutation analysis revealed significant clusters of difference between the MCI and HC groups in the frequency-channel and time-channel spaces. Cluster-based measures and performance measures (12 biosignatures in total) were selected as predictors of MCI. We trained a support vector machine (SVM) classifier achieving AUC = 0.89, accuracy = 77% in cross-validation using all data. Split-data validation resulted in (AUC = 0.87, accuracy = 76%) and (AUC = 0.75, accuracy = 70%) on testing data at baseline and follow-up visits, respectively. Classification scores at baseline and follow-up visits were correlated (r = 0.72, p<0.001, ICC = 0.84), supporting test-retest reliability of EEG biosignature. These results support the utility of EEG/ERP for prognostic testing, repeated assessments, and tracking potential treatment outcomes in the limited duration of clinical trials.
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Affiliation(s)
- Amir H. Meghdadi
- Advanced Brain Monitoring, Inc., Carlsbad, CA, United States of America
| | - David Salat
- Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | | | - Yue Hong
- Massachusetts General Hospital, Boston, MA, United States of America
| | - Bradley F. Boeve
- Departments of Neurology and Medicine, Division of Pulmonary and Critical Care Medicine, Center for Sleep Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, United States of America
| | - Erik K. St Louis
- Departments of Neurology and Medicine, Division of Pulmonary and Critical Care Medicine, Center for Sleep Medicine, Mayo Clinic College of Medicine and Science, Rochester, MN, United States of America
- Department of Clinical and Translational Research, Mayo Clinic Health System Southwest Wisconsin, La Crosse, WI, United States of America
| | - Ajay Verma
- Formation Venture Engineering, Boston, MA, United States of America
| | - Chris Berka
- Advanced Brain Monitoring, Inc., Carlsbad, CA, United States of America
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8
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Pappalettera C, Carrarini C, Cappa S, Caraglia N, Cotelli M, Marra C, Perani D, Redolfi A, Spadin P, Tagliavini F, Vanacore N, Vecchio F, Rossini PM. Challenges to identifying risk versus protective factors in Alzheimer's disease. Nat Med 2024:10.1038/s41591-024-03158-5. [PMID: 39025966 DOI: 10.1038/s41591-024-03158-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 06/26/2024] [Indexed: 07/20/2024]
Affiliation(s)
- Chiara Pappalettera
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Italy
| | - Claudia Carrarini
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy
| | - Stefano Cappa
- University Institute of Advanced Studies, Pavia, Italy
| | - Naike Caraglia
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Cotelli
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Camillo Marra
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Department of Psychology, Catholic University of Sacred Heart, Milan, Italy
| | | | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Patrizia Spadin
- Associazione Italiana Malattia di Alzheimer - AIMA, Milan, Italy
| | | | - Nicola Vanacore
- National Center for Disease Prevention and Health Promotion, National Institute of Health, Rome, Italy
| | - Fabrizio Vecchio
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Italy
| | - Paolo Maria Rossini
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.
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González-González E, Requena C, Barbosa F. Examining the influence of self-care practices on brain activity in healthy older adults. Front Aging Neurosci 2024; 16:1420072. [PMID: 39026994 PMCID: PMC11254819 DOI: 10.3389/fnagi.2024.1420072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/20/2024] [Indexed: 07/20/2024] Open
Abstract
Introduction Studies on the aging brain often occur in active settings, but comparatively few investigate brain activity in resting states. However, exploring brain activity in a resting state offers valuable insights into spontaneous neural processes unaffected by task-specific influences. Objective: To investigate the relationship between self-care practices, cognitive function, and patterns of brain activity in healthy older adults, taking into account predictions from aging brain models. Methodology 77 older adults aged 61 to 87 completing a self-care practices questionnaire, neuropsychological tests, and resting-state electroencephalogram (EEG) recordings. Participants were classified into two groups according to their self-care practices: traditional self-care (T-SC) and developmental self-care (D-SC). Results Although neuropsychological tests did not yield significant differences between the D-SC and T-SC groups, patterns of brain activity revealed distinct behaviors. The T-SC group demonstrated patterns more consistent with established aging brain models, contrasting with the D-SC group, which exhibited brain activity akin to that observed in younger adults. Specifically, the T-SC group displayed hyperactivation related to memory and executive function performance, alongside heightened alpha power in posterior regions. Furthermore, bilateral frontal activation in the beta band was evident. Conclusions The findings suggest a nuanced relationship between self-care practices and brain activity in older adults. While the T-SC group demonstrated brain activity patterns consistent with conservative aging, indicating the preservation of typical aging characteristics, the D-SC group displayed activity suggestive of a potential protective effect. This effect may be linked to self-care strategies that foster development and resilience in cognitive aging.
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Affiliation(s)
| | - Carmen Requena
- Laboratory of Lab-EEG-Lifespan, University of León, León, Spain
| | - Fernando Barbosa
- Laboratory of Neuropsychophysiology, University of Porto, Porto, Portugal
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10
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Lindhout JE, Richard E, Hafdi M, Perry M, Moll van Charante E, van Gool WA. The Association of Ancillary Diagnostic Tests With Outcome in Dementia. J Am Med Dir Assoc 2024; 25:105040. [PMID: 38796169 DOI: 10.1016/j.jamda.2024.105040] [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: 09/01/2023] [Revised: 04/12/2024] [Accepted: 04/14/2024] [Indexed: 05/28/2024]
Abstract
OBJECTIVES Dementia is a clinical diagnosis without curative treatment. It is uncertain whether ancillary testing is beneficial for patients. This study investigates the association between use of diagnostic tests and time to poor outcome and health care costs. DESIGN Nationwide register-based cohort study using health care reimbursement data in the Netherlands. SETTING AND PARTICIPANTS All Dutch hospitals, including 13,312 patients diagnosed with dementia in 2018. METHODS Diagnostic testing included computed tomography or magnetic resonance imaging (CT/MRI), neuropsychological examination (NPE), nuclear imaging (PET/SPECT), electroencephalography (EEG), and cerebrospinal fluid (CSF) testing. We compared time to poor outcome (institutionalization or death) and costs per month from 2018 to 2021 between those who underwent a specific diagnostic test in previous years to controls, propensity score matched for age, sex, type of hospital, and comorbidity. RESULTS Time to poor outcome in those who underwent CT/MRI, EEG, or CSF testing was similar to those who did not, but was longer for those who underwent NPE. Time to poor outcome was shorter in patients who underwent PET/SPECT. Patients who underwent CSF testing or PET/SPECT had higher mean total health care costs as compared to controls (CSF €248, 95% CI 64-433; PET/SPECT: €315, 95% CI 179-451). NPE during the diagnostic trajectory was associated with lower total health care cost (-€127, 95% CI -62, -193). CONCLUSION AND IMPLICATIONS NPE was associated with longer time to poor outcome and lower health care costs, potentially due to confounding by indication. Patients who underwent neuroimaging (CT, MRI, SPECT/PET), CSF testing, or EEG for dementia diagnostics did not experience a longer time to poor outcome or lower health care costs. This emphasizes the importance of clinical examination as anchor for the diagnosis of dementia.
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Affiliation(s)
- Josephine E Lindhout
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behavior, Nijmegen, The Netherlands; Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
| | - Edo Richard
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behavior, Nijmegen, The Netherlands; Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Melanie Hafdi
- Department of Neurology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Marieke Perry
- Radboudumc Alzheimer Center, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Primary and Community Care, Donders Institute for Brain, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Eric Moll van Charante
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Willem A van Gool
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition, and Behavior, Nijmegen, The Netherlands; Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
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11
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Vecchio F, Miraglia F, Pappalettera C, Nucci L, Cacciotti A, Rossini PM. Small World derived index to distinguish Alzheimer's type dementia and healthy subjects. Age Ageing 2024; 53:afae121. [PMID: 38935531 PMCID: PMC11210397 DOI: 10.1093/ageing/afae121] [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: 12/27/2023] [Revised: 04/26/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns. METHODS Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones. RESULTS Results showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones. CONCLUSIONS These findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients.
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Affiliation(s)
- Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
| | - Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
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Babiloni C, Jakhar D, Tucci F, Del Percio C, Lopez S, Soricelli A, Salvatore M, Ferri R, Catania V, Massa F, Arnaldi D, Famà F, Güntekin B, Yener G, Stocchi F, Vacca L, Marizzoni M, Giubilei F, Yıldırım E, Hanoğlu L, Hünerli D, Frisoni GB, Noce G. Resting state electroencephalographic alpha rhythms are sensitive to Alzheimer's disease mild cognitive impairment progression at a 6-month follow-up. Neurobiol Aging 2024; 137:19-37. [PMID: 38402780 DOI: 10.1016/j.neurobiolaging.2024.01.013] [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: 10/07/2022] [Revised: 10/31/2023] [Accepted: 01/26/2024] [Indexed: 02/27/2024]
Abstract
Are posterior resting-state electroencephalographic (rsEEG) alpha rhythms sensitive to the Alzheimer's disease mild cognitive impairment (ADMCI) progression at a 6-month follow-up? Clinical, cerebrospinal, neuroimaging, and rsEEG datasets in 52 ADMCI and 60 Healthy old seniors (equivalent groups for demographic features) were available from an international archive (www.pdwaves.eu). The ADMCI patients were arbitrarily divided into two groups: REACTIVE and UNREACTIVE, based on the reduction (reactivity) in the posterior rsEEG alpha eLORETA source activities from the eyes-closed to eyes-open condition at ≥ -10% and -10%, respectively. 75% of the ADMCI patients were REACTIVE. Compared to the UNREACTIVE group, the REACTIVE group showed (1) less abnormal posterior rsEEG source activity during the eyes-closed condition and (2) a decrease in that activity at the 6-month follow-up. These effects could not be explained by neuroimaging and neuropsychological biomarkers of AD. Such a biomarker might reflect abnormalities in cortical arousal in quiet wakefulness to be used for clinical studies in ADMCI patients using 6-month follow-ups.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino (FR), Italy.
| | - Dharmendra Jakhar
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy; Department of Medical, Movement and Wellbeing Sciences, University of Naples Parthenope, Naples, Italy
| | | | | | | | - Federico Massa
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy; Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Dario Arnaldi
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy; Neurofisiopatologia, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Francesco Famà
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy; Neurofisiopatologia, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey; Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir University of Economics, Faculty of Medicine, Izmir, Turkey
| | | | | | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Ebru Yıldırım
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Duygu Hünerli
- Health Sciences Institute, Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
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13
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Zhou H, Yin L, Su R, Zhang Y, Yuan Y, Xie P, Li X. STCGRU: A hybrid model based on CNN and BiGRU for mild cognitive impairment diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108123. [PMID: 38471292 DOI: 10.1016/j.cmpb.2024.108123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/28/2023] [Accepted: 03/07/2024] [Indexed: 03/14/2024]
Abstract
BACKGROUND AND OBJECTIVE Early diagnosis of mild cognitive impairment (MCI) is one of the essential measures to prevent its further development into Alzheimer's disease (AD). In this paper, we propose a hybrid deep learning model for early diagnosis of MCI, called spatio-temporal convolutional gated recurrent unit network (STCGRU). METHODS The STCGRU comprises three bespoke convolutional neural network (CNN) modules and a bi-directional gated recurrent unit (BiGRU) module, which can effectively extract the spatial and temporal features of EEG and obtain excellent diagnostic results. We use a publicly available EEG dataset that has not undergone pre-processing to verify the robustness and accuracy of the model. Ablation experiments on STCGRU are conducted to showcase the individual performance improvement of each module. RESULTS Compared with other state-of-the-art approaches using the same publicly available EEG dataset, the results show that STCGRU is more suitable for early diagnosis of MCI. After 10-fold cross-validation, the average classification accuracy of the hybrid model reached 99.95 %, while the average kappa value reached 0.9989. CONCLUSIONS The experimental results show that the hybrid model proposed in this paper can directly extract compelling spatio-temporal features from the raw EEG data for classification. The STCGRU allows for accurate diagnosis of patients with MCI and has a high practical value.
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Affiliation(s)
- Hao Zhou
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Liyong Yin
- The First Hospital of Qinhuangdao, Qinhuangdao, PR China
| | - Rui Su
- Hebei Medical University, Shijiazhuang, PR China
| | - Ying Zhang
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Yi Yuan
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Ping Xie
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China
| | - Xin Li
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, PR China.
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14
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Easwaran K, Ramakrishnan K, Jeyabal SN. Classification of cognitive impairment using electroencephalography for clinical inspection. Proc Inst Mech Eng H 2024; 238:358-371. [PMID: 38366360 DOI: 10.1177/09544119241228912] [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: 02/18/2024]
Abstract
Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.
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Affiliation(s)
- Karuppathal Easwaran
- Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
| | - Kalpana Ramakrishnan
- Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
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15
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Cecchetti G, Basaia S, Canu E, Cividini C, Cursi M, Caso F, Santangelo R, Fanelli GF, Magnani G, Agosta F, Filippi M. EEG Correlates in the 3 Variants of Primary Progressive Aphasia. Neurology 2024; 102:e207993. [PMID: 38165298 DOI: 10.1212/wnl.0000000000207993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND AND OBJECTIVES The 3 clinical presentations of primary progressive aphasia (PPA) reflect heterogenous neuropathology, which is difficult to be recognized in vivo. Resting-state (RS) EEG is promising for the investigation of brain electrical substrates in neurodegenerative conditions. In this study, we aim to explore EEG cortical sources in the characterization of the 3 variants of PPA. METHODS This is a cross-sectional, single-center, memory center-based cohort study. Patients with PPA and healthy controls were consecutively recruited at the Neurology Unit, IRCCS San Raffaele Scientific Institute (Milan, Italy). Each participant underwent an RS 19-channel EEG. Using standardized low-resolution brain electromagnetic tomography, EEG current source densities were estimated at voxel level and compared among study groups. Using an RS functional MRI-driven model of source reconstruction, linear lagged connectivity (LLC) values within language and extra-language brain networks were obtained and analyzed among groups. RESULTS Eighteen patients with logopenic PPA variant (lvPPA; mean age = 72.7 ± 6.6; % female = 52.4), 21 patients with nonfluent/agrammatic PPA variant (nfvPPA; mean age = 71.7 ± 8.1; % female = 66.6), and 9 patients with semantic PPA variant (svPPA; mean age = 65.0 ± 6.9; % female = 44.4) were enrolled in the study, together with 21 matched healthy controls (mean age = 69.2 ± 6.5; % female = 57.1). Patients with lvPPA showed a higher delta density than healthy controls (p < 0.01) and patients with nfvPPA (p < 0.05) and svPPA (p < 0.05). Patients with lvPPA also displayed a greater theta density over the left posterior hemisphere (p < 0.01) and lower alpha2 values (p < 0.05) over the left frontotemporal regions than controls. Patients with nfvPPA showed a diffuse greater theta density than controls (p < 0.05). LLC was altered in all patients relative to controls (p < 0.05); the alteration was greater at slow frequency bands and within language networks than extra-language networks. Patients with lvPPA also showed greater LLC values at theta band than patients with nfvPPA (p < 0.05). DISCUSSION EEG findings in patients with PPA suggest that lvPPA-related pathology is associated with a characteristic disruption of the cortical electrical activity, which might help in the differential diagnosis from svPPA and nfvPPA. EEG connectivity was disrupted in all PPA variants, with distinct findings in disease-specific PPA groups. CLASSIFICATION OF EVIDENCE This study provides Class IV evidence that EEG analysis can distinguish PPA due to probable Alzheimer disease from PPA due to probable FTD from normal aging.
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Affiliation(s)
- Giordano Cecchetti
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Basaia
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisa Canu
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Camilla Cividini
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marco Cursi
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Francesca Caso
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Roberto Santangelo
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giovanna F Fanelli
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Giuseppe Magnani
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Massimo Filippi
- From the Neurology Unit (G.C., F.C., R.S., G.M., F.A., M.F.), Neurophysiology Service (G.C., M.C., R.S., G.F.F., M.F.), and Neuroimaging Research Unit (G.C., S.B., E.C., C.C., F.A., M.F.), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (G.C., F.A., M.F.); and Neurorehabilitation Unit (M.F.), IRCCS San Raffaele Scientific Institute, Milan, Italy
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Arjmandi-Rad S, Vestergaard Nieland JD, Goozee KG, Vaseghi S. The effects of different acetylcholinesterase inhibitors on EEG patterns in patients with Alzheimer's disease: A systematic review. Neurol Sci 2024; 45:417-430. [PMID: 37843690 DOI: 10.1007/s10072-023-07114-y] [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: 06/21/2023] [Accepted: 10/01/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVE Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia. The early diagnosis of AD is an important factor for the control of AD progression. Electroencephalography (EEG) can be used for early diagnosis of AD. Acetylcholinesterase inhibitors (AChEIs) are also used for the amelioration of AD symptoms. In this systematic review, we reviewed the effect of different AChEIs including donepezil, rivastigmine, tacrine, physostigmine, and galantamine on EEG patterns in patients with AD. METHODS PubMed electronic database was searched and 122 articles were found. After removal of unrelated articles, 24 articles were selected for the present study. RESULTS AChEIs can decrease beta, theta, and delta frequency bands in patients with AD. However, conflicting results were found for alpha band. Some studies have shown increased alpha frequency, while others have shown decreased alpha frequency following treatment with AChEIs. The only difference was the type of drug. CONCLUSIONS We found that studies reporting the decreased alpha frequency used donepezil and galantamine, while studies reporting the increased alpha frequency used rivastigmine and tacrine. It was suggested that future studies should focus on the effect of different AChEIs on EEG bands, especially alpha frequency in patients with AD, to compare their effects and find the reason for their different influence on EEG patterns. Also, differences between the effects of AChEIs on oligodendrocyte differentiation and myelination may be another important factor. This is the first article investigating the effect of different AChEIs on EEG patterns in patients with AD.
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Affiliation(s)
- Shirin Arjmandi-Rad
- Institute for Cognitive & Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | | | - Kathryn G Goozee
- KaRa Institute of Neurological Diseases Pty Ltd, Macquarie, NSW, Australia
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, Australia
| | - Salar Vaseghi
- Cognitive Neuroscience Lab, Medicinal Plants Research Center, Institute of Medicinal Plants, ACECR, Karaj, Iran.
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Gorgoni M, Cenani J, Scarpelli S, D'Atri A, Alfonsi V, Annarumma L, Pietrogiacomi F, Ferrara M, Marra C, Rossini PM, De Gennaro L. The role of the sleep K-complex on the conversion from mild cognitive impairment to Alzheimer's disease. J Sleep Res 2024; 33:e14046. [PMID: 37718942 DOI: 10.1111/jsr.14046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/24/2023] [Accepted: 08/29/2023] [Indexed: 09/19/2023]
Abstract
The present literature points to an alteration of the human K-complex during non-rapid eye movement sleep in Alzheimer's disease. Nevertheless, the few findings on the K-complex changes in mild cognitive impairment and their possible predictive role on the Alzheimer's disease conversion show mixed findings, lack of replication, and a main interest for the frontal region. The aim of the present study was to assess K-complex measures in amnesic mild cognitive impairment subsequently converted in Alzheimer's disease over different cortical regions, comparing them with healthy controls and stable amnesic mild cognitive impairment. We assessed baseline K-complex density, amplitude, area under the curve and overnight changes in frontal, central and parietal midline derivations of 12 amnesic mild cognitive impairment subsequently converted in Alzheimer's disease, 12 stable amnesic mild cognitive impairment and 12 healthy controls. We also assessed delta electroencephalogram power, to determine if K-complex alterations in amnesic mild cognitive impairment occur with modification of the electroencephalogram power in the frequency range of the slow-wave activity. We found a reduced parietal K-complex density in amnesic mild cognitive impairment subsequently converted in Alzheimer's disease compared with stable amnesic mild cognitive impairment and healthy controls, without changes in K-complex morphology and overnight modulation. Both amnesic mild cognitive impairment groups showed decreased slow-wave sleep percentage compared with healthy controls. No differences between groups were observed in slow-wave activity power. Our findings suggest that K-complex alterations in mild cognitive impairment may be observed earlier in parietal regions, likely mirroring the topographical progression of Alzheimer's disease-related brain pathology, and express a frontal predominance only in a full-blown phase of Alzheimer's disease. Consistently with previous results, such K-complex modification occurs in the absence of significant electroencephalogram power changes in the slow oscillations range.
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Affiliation(s)
- Maurizio Gorgoni
- Department of Psychology, Sapienza University of Rome, Rome, Italy
- Body and Action Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Jessica Cenani
- Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Serena Scarpelli
- Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Aurora D'Atri
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | | | | | | | - Michele Ferrara
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Camillo Marra
- Institute of Neurology, Catholic University, Rome, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Luigi De Gennaro
- Department of Psychology, Sapienza University of Rome, Rome, Italy
- Body and Action Lab, IRCCS Fondazione Santa Lucia, Rome, Italy
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18
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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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19
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Sun J, Sun Y, Shen A, Li Y, Gao X, Lu B. An ensemble learning model for continuous cognition assessment based on resting-state EEG. NPJ AGING 2024; 10:1. [PMID: 38167843 PMCID: PMC10762083 DOI: 10.1038/s41514-023-00129-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 11/09/2023] [Indexed: 01/05/2024]
Abstract
One critical manifestation of neurological deterioration is the sign of cognitive decline. Causes of cognitive decline include but are not limited to: aging, cerebrovascular disease, Alzheimer's disease, and trauma. Currently, the primary tool used to examine cognitive decline is scale. However, scale examination has drawbacks such as its clinician subjectivity and inconsistent results. This study attempted to use resting-state EEG to construct a cognitive assessment model that is capable of providing a more scientific and robust evaluation on cognition levels. In this study, 75 healthy subjects, 99 patients with Mild Cognitive Impairment (MCI), and 78 patients with dementia were involved. Their resting-state EEG signals were collected twice, and the recording devices varied. By matching these EEG and traditional scale results, the proposed cognition assessment model was trained based on Adaptive Boosting (AdaBoost) and Support Vector Machines (SVM) methods, mapping subjects' cognitive levels to a 0-100 test score with a mean error of 4.82 (<5%). This study is the first to establish a continuous evaluation model of cognitive decline on a large sample dataset. Its cross-device usability also suggests universality and robustness of this EEG model, offering a more reliable and affordable way to assess cognitive decline for clinical diagnosis and treatment as well. Furthermore, the interpretability of features involved may further contribute to the early diagnosis and superior treatment evaluation of Alzheimer's disease.
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Affiliation(s)
- Jingnan Sun
- Department of Biomedical Engineering, Tsinghua University, 100084, Beijing, China
| | - Yike Sun
- Department of Biomedical Engineering, Tsinghua University, 100084, Beijing, China
| | - Anruo Shen
- Department of Biomedical Engineering, Tsinghua University, 100084, Beijing, China
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, 200092, Shanghai, China
| | - Xiaorong Gao
- Department of Biomedical Engineering, Tsinghua University, 100084, Beijing, China.
| | - Bai Lu
- School of Pharmaceutical Sciences, IDG/McGovern Institute for Brain Research, Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, 100084, Beijing, China.
- Beijing Academy of Artificial Intelligence, 100080, Beijing, China.
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20
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Kopčanová M, Tait L, Donoghue T, Stothart G, Smith L, Flores-Sandoval AA, Davila-Perez P, Buss S, Shafi MM, Pascual-Leone A, Fried PJ, Benwell CSY. Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes. Neurobiol Dis 2024; 190:106380. [PMID: 38114048 DOI: 10.1016/j.nbd.2023.106380] [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: 07/13/2023] [Revised: 11/30/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasize the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.
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Affiliation(s)
- Martina Kopčanová
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK.
| | - Luke Tait
- Centre for Systems Modelling and Quantitative Biomedicine, School of Medical and Dental Sciences, University of Birmingham, UK; Cardiff University Brain Research Imaging Centre, Cardiff, UK
| | - Thomas Donoghue
- Department of Biomedical Engineering, Columbia University, New York, USA
| | | | - Laura Smith
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Aimee Arely Flores-Sandoval
- Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Paula Davila-Perez
- Rey Juan Carlos University Hospital (HURJC), Department of Clinical Neurophysiology, Móstoles, Madrid, Spain; Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Stephanie Buss
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, United States of America
| | - Peter J Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Christopher S Y Benwell
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
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21
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Yamasaki T, Sugi T, Doniger GM. Editorial: New management strategies for older adults with cognitive decline. Front Med (Lausanne) 2023; 10:1282436. [PMID: 38105893 PMCID: PMC10722418 DOI: 10.3389/fmed.2023.1282436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/23/2023] [Indexed: 12/19/2023] Open
Affiliation(s)
- Takao Yamasaki
- Department of Neurology, Minkodo Minohara Hospital, Fukuoka, Japan
- Kumagai Institute of Health Policy, Fukuoka, Japan
- School of Health Sciences at Fukuoka, International University of Health and Welfare, Fukuoka, Japan
| | - Takenao Sugi
- Faculty of Science and Engineering, Saga University, Saga, Japan
| | - Glen M. Doniger
- Department of Clinical Research, NeuroTrax Corporation, Modiin, Israel
- School of Business Administration, Bar-Ilan University, Ramat Gan, Israel
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22
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Belyaev M, Murugappan M, Velichko A, Korzun D. Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:8609. [PMID: 37896703 PMCID: PMC10610702 DOI: 10.3390/s23208609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023]
Abstract
This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analysis of the effectiveness of entropy calculation methods, fuzzy entropy showed the best results in diagnosing and monitoring PD using rs-EEG, with classification accuracy (ARKF) of ~99.9%. The most important frequency range of rs-EEG for PD-based diagnostics lies in the range of 0-4 Hz, and the most informative signals were mainly received from the right hemisphere of the head. It was also found that ARKF significantly decreased as the length of rs-EEG segments decreased from 1000 to 150 samples. Using a procedure for selecting the most informative features, it was possible to reduce the computational costs of classification by 11 times, while maintaining an ARKF ~99.9%. The proposed method can be used in the healthcare internet of things (H-IoT), where low-performance edge devices can implement ML sensors to enhance human resilience to PD.
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Affiliation(s)
- Maksim Belyaev
- Institute of Physics and Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia;
| | - Murugappan Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Kuwait City 13133, Kuwait;
- Department of Electronics and Communication Engineering, Faculty of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai 600117, India
- Centre of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia;
| | - Dmitry Korzun
- Department of Computer Science, Institute of Mathematics and Information Technology, Petrozavodsk State University, 185910 Petrozavodsk, Russia;
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23
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Klepl D, He F, Wu M, Blackburn DJ, Sarrigiannis P. Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease Using EEG Data. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3978-3987. [PMID: 37792656 DOI: 10.1109/tnsre.2023.3321634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer's disease (AD), remains a relatively unexplored area of research. Previous studies have relied on functional connectivity methods to infer brain graph structures and used simple GNN architectures for the diagnosis of AD. In this work, we propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a correlation-based measure of power spectral density similarity. Furthermore, the gated graph convolution can dynamically weigh the contribution of various spatial scales. The proposed model achieves high accuracy in both eyes-closed and eyes-open conditions, indicating the stability of learned representations. Finally, we demonstrate that the proposed AGGCN model generates consistent explanations of its predictions that might be relevant for further study of AD-related alterations of brain networks.
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24
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Rodríguez-González V, Núñez P, Gómez C, Shigihara Y, Hoshi H, Tola-Arribas MÁ, Cano M, Guerrero Á, García-Azorín D, Hornero R, Poza J. Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses. Neuroimage 2023; 280:120332. [PMID: 37619796 DOI: 10.1016/j.neuroimage.2023.120332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/05/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.
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Affiliation(s)
- Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain.
| | - Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain
| | | | | | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Mónica Cano
- Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Ángel Guerrero
- Hospital Clínico Universitario, Valladolid, Spain; Department of Medicine, University of Valladolid, Spain
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
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25
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Flores-Sandoval AA, Davila-Pérez P, Buss SS, Donohoe K, O'Connor M, Shafi MM, Pascual-Leone A, Benwell CSY, Fried PJ. Spectral power ratio as a measure of EEG changes in mild cognitive impairment due to Alzheimer's disease: a case-control study. Neurobiol Aging 2023; 130:50-60. [PMID: 37459658 PMCID: PMC10614059 DOI: 10.1016/j.neurobiolaging.2023.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/12/2023] [Accepted: 05/17/2023] [Indexed: 08/13/2023]
Abstract
Adopting preventive strategies in individuals with subclinical Alzheimer's disease (AD) has the potential to delay dementia onset and reduce healthcare costs. Thus, it is extremely important to identify inexpensive, scalable, sensitive, and specific markers to track disease progression. The electroencephalography spectral power ratio (SPR: the fast to slow spectral power ratio), a measure of the shift in power distribution from higher to lower frequencies, holds potential for aiding clinical practice. The SPR is altered in patients with AD, correlates with cognitive functions, and can be easily implemented in clinical settings. However, whether the SPR is sensitive to pathophysiological changes in the prodromal stage of AD is unclear. We explored the SPR of individuals diagnosed with amyloid-positive amnestic mild cognitive impairment (Aβ+aMCI) and its association with both cognitive function and amyloid load. The SPR was lower in Aβ+aMCI than in the cognitively unimpaired individuals and correlated with executive function scores but not with amyloid load. Hypothesis-generating analyses suggested that aMCI participants with a lower SPR had an increased probability of a positive amyloid positron emission tomography. Future research may explore the potential of this measure to classify aMCI individuals according to their AD biomarker status.
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Affiliation(s)
- Aimee A Flores-Sandoval
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Paula Davila-Pérez
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Department of Clinical Neurophysiology, Hospital Universitario Rey Juan Carlos, Móstoles, Spain; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Stephanie S Buss
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Kevin Donohoe
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Margaret O'Connor
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Hinda and Arthur Marcus Institute for Aging Research, and Deanna and Sidney Wolk Center for Memory Health, Hebrew Senior Life, Boston, MA, USA
| | - Christopher S Y Benwell
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
| | - Peter J Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA.
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Fu Y, Xu C, Fan H, Yang X, Ou J, Yao L, Wang W. Traumatic brain injury and rTMS-ERPs: Case report and literature review. Open Life Sci 2023; 18:20220677. [PMID: 37724119 PMCID: PMC10505337 DOI: 10.1515/biol-2022-0677] [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: 04/14/2023] [Revised: 06/20/2023] [Accepted: 07/18/2023] [Indexed: 09/20/2023] Open
Abstract
Currently, there are no cases of targeted, individualized repeated transcranial magnetic stimulation (rTMS) treatment based on event-related potential (ERPs) results showing the activation of functional brain regions. The identification and treatment of mild cognitive impairment after traumatic brain injury are challenging. rTMS has shown unique advantages in previous studies, with positive effects on noninvasive modulation and neuroplasticity after brain injury. The selection of the rTMS parameters and targets remains controversial. ERPs indicate the cortical activity involved in cognitive processing in patients. Therefore, this study proposes that ERPs can be used as biomarkers of cognitive recovery. The results of this study will guide the development of rTMS protocols for patient treatment. To help clinicians better apply rTMS and ERPs in combination, we conducted a relevant literature review and discussion, detailing the therapeutic mechanisms of the combination of ERPs and rTMS. This will facilitate the precise assessment and personalized treatment of such patients, improve the abnormal processing patterns of patients, and promote their return to life and society.
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Affiliation(s)
- Yutong Fu
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China
| | - Chunyan Xu
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China
| | - Hong Fan
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China
| | - Xue Yang
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China
| | - Jibing Ou
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China
| | - Liqing Yao
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China
| | - Wenli Wang
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China
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27
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Azami H, Zrenner C, Brooks H, Zomorrodi R, Blumberger DM, Fischer CE, Flint A, Herrmann N, Kumar S, Lanctôt K, Mah L, Mulsant BH, Pollock BG, Rajji TK. Beta to theta power ratio in EEG periodic components as a potential biomarker in mild cognitive impairment and Alzheimer's dementia. Alzheimers Res Ther 2023; 15:133. [PMID: 37550778 PMCID: PMC10405483 DOI: 10.1186/s13195-023-01280-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/27/2023] [Indexed: 08/09/2023]
Abstract
BACKGROUND Alzheimer's dementia (AD) is associated with electroencephalography (EEG) abnormalities including in the power ratio of beta to theta frequencies. EEG studies in mild cognitive impairment (MCI) have been less consistent in identifying such abnormalities. One potential reason is not excluding the EEG aperiodic components, which are less associated with cognition than the periodic components. Here, we investigate whether aperiodic and periodic EEG components are disrupted differently in AD or MCI vs. healthy control (HC) individuals and whether a periodic based beta/theta ratio differentiates better MCI from AD and HC groups than a ratio based on the full spectrum. METHODS Data were collected from 44 HC (mean age (SD) = 69.1 (5.3)), 114 MCI (mean age (SD) = 72.2 (7.5)), and 41 AD (mean age (SD) = 75.7 (6.5)) participants. Aperiodic and periodic components and full spectrum EEG were compared among the three groups. Receiver operating characteristic curves obtained via logistic regression classifications were used to distinguish the groups. Last, we explored the relationships between cognitive performance and the beta/theta ratios based on the full or periodic spectrum. RESULTS Aperiodic EEG components did not differ among the three groups. In contrast, AD participants showed an increase in full spectrum and periodic relative powers for delta, theta, and gamma and a decrease for beta when compared to HC or MCI participants. As predicted, MCI group differed from HC participants on the periodic based beta/theta ratio (Bonferroni corrected p-value = 0.036) measured over the occipital region. Classifiers based on beta/theta power ratio in EEG periodic components distinguished AD from HC and MCI participants, and outperformed classifiers based on beta/theta power ratio in full spectrum EEG. Beta/theta ratios were comparable in their association with cognition. CONCLUSIONS In contrast to a full spectrum EEG analysis, a periodic-based analysis shows that MCI individuals are different on beta/theta ratio when compared to healthy individuals. Focusing on periodic components in EEG studies with or without other biological markers of neurodegenerative diseases could result in more reliable findings to separate MCI from healthy aging, which would be valuable for designing preventative interventions.
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Affiliation(s)
- Hamed Azami
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Christoph Zrenner
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Heather Brooks
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Reza Zomorrodi
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Daniel M Blumberger
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Corinne E Fischer
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Alastair Flint
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
| | - Nathan Herrmann
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Sanjeev Kumar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Krista Lanctôt
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Linda Mah
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, ON, Canada
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Bruce G Pollock
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Tarek K Rajji
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada.
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada.
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Chen X, Li Y, Li R, Yuan X, Liu M, Zhang W, Li Y. Multiple cross-frequency coupling analysis of resting-state EEG in patients with mild cognitive impairment and Alzheimer's disease. Front Aging Neurosci 2023; 15:1142085. [PMID: 37600515 PMCID: PMC10436577 DOI: 10.3389/fnagi.2023.1142085] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 07/11/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction Electroencephalographic (EEG) abnormalities are seen in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) with characteristic features of cognitive impairment. The most common findings of EEG features in AD and MCI patients are increased relative power of slow oscillations (delta and theta rhythms) and decreased relative power of fast oscillations (alpha, beta and gamma rhythms). However, impairments in cognitive processes in AD and MCI are not sufficiently reflected by brain oscillatory activity in a particular frequency band. MCI patients are at high risk of progressing to AD. Cross-frequency coupling (CFC), which refers to coupling between different frequency bands, is a crucial tool for comprehending changes in brain oscillations and cognitive performance. CFC features exhibit some specificity in patients with AD and MCI, but a comparison between CFC features in individuals with these disorders is still lacking. The aim of this study was to explore changes in CFC properties in MCI and AD and to explore the relationship between CFC properties and multiple types of cognitive functional performance. Methods We recorded resting-state EEG (rsEEG) signals in 46 MCI patients, 43 AD patients, and 43 cognitively healthy controls (HCs) and analyzed the changes in CFC as well as the relationship between CFC and scores on clinical tests of cognitive function. Results and discussion Multiple couplings between low-frequency oscillations and high-frequency oscillations were found to be significantly enhanced in AD patients compared to those of HCs and MCI, while delta-gamma as well as theta-gamma couplings in the right temporal and parietal lobes were significantly enhanced in MCI patients compared to HCs. Moreover, theta-gamma coupling in the right temporal lobe tended to be stronger in MCI patients than in HCs, and it was stronger in AD than in MCI. Multiple CFC properties were found to correlate significantly with various cognitive domains, especially the memory function domain. Overall, these findings suggest that AD and MCI patients must use more neural resources to maintain a resting brain state and that alterations in theta-gamma coupling in the temporal lobe become progressively obvious during disease progression and are likely to be a valuable indicator of MCI and AD pathology.
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Affiliation(s)
- Xi Chen
- School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Yingjie Li
- College of International Education, Shanghai University, Shanghai, China
- School of Life Science, Institute of Biomedical Engineering, Shanghai University, Shanghai, China
| | - Renren Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiao Yuan
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Meng Liu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Shanghai Changhai Hospital, the Second Military Medical University, Shanghai, China
| | - Wei Zhang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, China
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29
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Sharma A, Angnes L, Sattarahmady N, Negahdary M, Heli H. Electrochemical Immunosensors Developed for Amyloid-Beta and Tau Proteins, Leading Biomarkers of Alzheimer's Disease. BIOSENSORS 2023; 13:742. [PMID: 37504140 PMCID: PMC10377038 DOI: 10.3390/bios13070742] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/27/2023] [Accepted: 07/14/2023] [Indexed: 07/29/2023]
Abstract
Alzheimer's disease (AD) is the most common neurological disease and a serious cause of dementia, which constitutes a threat to human health. The clinical evidence has found that extracellular amyloid-beta peptides (Aβ), phosphorylated tau (p-tau), and intracellular tau proteins, which are derived from the amyloid precursor protein (APP), are the leading biomarkers for accurate and early diagnosis of AD due to their central role in disease pathology, their correlation with disease progression, their diagnostic value, and their implications for therapeutic interventions. Their detection and monitoring contribute significantly to understanding AD and advancing clinical care. Available diagnostic techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are mainly used to validate AD diagnosis. However, these methods are expensive, yield results that are difficult to interpret, and have common side effects such as headaches, nausea, and vomiting. Therefore, researchers have focused on developing cost-effective, portable, and point-of-care alternative diagnostic devices to detect specific biomarkers in cerebrospinal fluid (CSF) and other biofluids. In this review, we summarized the recent progress in developing electrochemical immunosensors for detecting AD biomarkers (Aβ and p-tau protein) and their subtypes (AβO, Aβ(1-40), Aβ(1-42), t-tau, cleaved-tau (c-tau), p-tau181, p-tau231, p-tau381, and p-tau441). We also evaluated the key characteristics and electrochemical performance of developed immunosensing platforms, including signal interfaces, nanomaterials or other signal amplifiers, biofunctionalization methods, and even primary electrochemical sensing performances (i.e., sensitivity, linear detection range, the limit of detection (LOD), and clinical application).
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Affiliation(s)
- Abhinav Sharma
- Solar Center, Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Lúcio Angnes
- Department of Fundamental Chemistry, Institute of Chemistry, University of São Paulo, Av. Prof. Lineu Prestes, 748, São Paulo 05508-000, Brazil
| | - Naghmeh Sattarahmady
- Department of Medical Physics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
- Nanomedicine and Nanobiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Masoud Negahdary
- Department of Fundamental Chemistry, Institute of Chemistry, University of São Paulo, Av. Prof. Lineu Prestes, 748, São Paulo 05508-000, Brazil
| | - Hossein Heli
- Nanomedicine and Nanobiology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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30
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Cuadros J, Z-Rivera L, Castro C, Whitaker G, Otero M, Weinstein A, Martínez-Montes E, Prado P, Zañartu M. DIVA Meets EEG: Model Validation Using Formant-Shift Reflex. APPLIED SCIENCES (BASEL, SWITZERLAND) 2023; 13:7512. [PMID: 38435340 PMCID: PMC10906992 DOI: 10.3390/app13137512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
The neurocomputational model 'Directions into Velocities of Articulators' (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.
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Affiliation(s)
- Jhosmary Cuadros
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Lucía Z-Rivera
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | - Christian Castro
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | - Grace Whitaker
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
| | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago 8420524, Chile
- Centro Basal Ciencia & Vida, Universidad San Sebastián, Santiago 8580000, Chile
| | - Alejandro Weinstein
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | | | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago 7510602, Chile
| | - Matías Zañartu
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
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31
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Kopčanová M, Tait L, Donoghue T, Stothart G, Smith L, Sandoval AAF, Davila-Perez P, Buss S, Shafi MM, Pascual-Leone A, Fried PJ, Benwell CS. Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.11.544491. [PMID: 37398162 PMCID: PMC10312609 DOI: 10.1101/2023.06.11.544491] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasise the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.
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Affiliation(s)
- Martina Kopčanová
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
| | - Luke Tait
- Centre for Systems Modelling and Quantitative Biomedicine, School of Medical and Dental Sciences, University of Birmingham, UK
- Cardiff University Brain Research Imaging Centre, Cardiff, UK
| | - Thomas Donoghue
- Department of Biomedical Engineering, Columbia University, New York, USA
| | | | - Laura Smith
- School of Psychology, University of Kent, Kent, UK
| | - Aimee Arely Flores Sandoval
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117, Berlin, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Paula Davila-Perez
- Rey Juan Carlos University Hospital (HURJC), Department of Clinical Neurophysiology, Móstoles, Madrid, Spain
- Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Stephanie Buss
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Mouhsin M. Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Alvaro Pascual-Leone
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston MA
| | - Peter J. Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher S.Y. Benwell
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
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32
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Vicchietti ML, Ramos FM, Betting LE, Campanharo ASLO. Computational methods of EEG signals analysis for Alzheimer's disease classification. Sci Rep 2023; 13:8184. [PMID: 37210397 DOI: 10.1038/s41598-023-32664-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/30/2023] [Indexed: 05/22/2023] Open
Abstract
Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer's disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.
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Affiliation(s)
- Mário L Vicchietti
- Department of Biodiversity and Biostatistics, Institute of Biosciences, São Paulo State University, Botucatu, 18618-689, Brazil
| | - Fernando M Ramos
- National Institute for Space Research, Earth System Science Center, São José dos Campos, 12227-010, Brazil
| | - Luiz E Betting
- Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, São Paulo State University, Botucatu, 18618-687, Brazil
| | - Andriana S L O Campanharo
- Department of Biodiversity and Biostatistics, Institute of Biosciences, São Paulo State University, Botucatu, 18618-689, Brazil.
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33
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Prado P, Moguilner S, Mejía JA, Sainz-Ballesteros A, Otero M, Birba A, Santamaria-Garcia H, Legaz A, Fittipaldi S, Cruzat J, Tagliazucchi E, Parra M, Herzog R, Ibáñez A. Source space connectomics of neurodegeneration: One-metric approach does not fit all. Neurobiol Dis 2023; 179:106047. [PMID: 36841423 PMCID: PMC11170467 DOI: 10.1016/j.nbd.2023.106047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 02/05/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023] Open
Abstract
Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Jhony A Mejía
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Ingeniería Biomédica, Universidad de Los Andes, Bogotá, Colombia
| | | | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile; Centro BASAL Ciencia & Vida, Universidad San Sebastián, Santiago, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Hernando Santamaria-Garcia
- PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Global Brain Health Institute, University of California San Francisco, San Francisco, California; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Trinity College Dublin (TCD), Dublin, Ireland.
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Hatlestad-Hall C, Bruña R, Liljeström M, Renvall H, Heuser K, Taubøll E, Maestú F, Haraldsen IH. Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough? Clin Neurophysiol 2023; 150:1-16. [PMID: 36972647 DOI: 10.1016/j.clinph.2023.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/03/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities. METHODS EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested. RESULTS The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated. CONCLUSIONS Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data. SIGNIFICANCE Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.
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Affiliation(s)
| | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Kjell Heuser
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; BrainSymph AS, Oslo, Norway
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35
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Sibilano E, Brunetti A, Buongiorno D, Lassi M, Grippo A, Bessi V, Micera S, Mazzoni A, Bevilacqua V. An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG. J Neural Eng 2023; 20. [PMID: 36745929 DOI: 10.1088/1741-2552/acb96e] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023]
Abstract
Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals.Approach. EEG recordings of 17 healthy controls (HCs), 56 subjective cognitive decline (SCD) and 45 mild cognitive impairment (MCI) subjects were acquired at resting state. After preprocessing, we selected sections corresponding to eyes-closed condition. Five different datasets were created by extracting delta, theta, alpha, beta and delta-to-theta frequency bands using bandpass filters. To classify SCDvsMCI and HCvsSCDvsMCI, we propose a framework based on the transformer architecture, which uses multi-head attention to focus on the most relevant parts of the input signals. We trained and validated the model on each dataset with a leave-one-subject-out cross-validation approach, splitting the signals into 10 s epochs. Subjects were assigned to the same class as the majority of their epochs. Classification performances of the transformer were assessed for both epochs and subjects and compared with other DL models.Main results. Results showed that the delta dataset allowed our model to achieve the best performances for the discrimination of SCD and MCI, reaching an Area Under the ROC Curve (AUC) of 0.807, while the highest results for the HCvsSCDvsMCI classification were obtained on alpha and theta with a micro-AUC higher than 0.74.Significance. We demonstrated that DL approaches can support the adoption of non-invasive and economic techniques as EEG to stratify patients in the clinical population at risk for AD. This result was achieved since the attention mechanism was able to learn temporal dependencies of the signal, focusing on the most discriminative patterns, achieving state-of-the-art results by using a deep model of reduced complexity. Our results were consistent with clinical evidence that changes in brain activity are progressive when considering early stages of AD.
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Affiliation(s)
- Elena Sibilano
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
| | - Antonio Brunetti
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
| | - Domenico Buongiorno
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
| | - Michael Lassi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
| | | | - Valentina Bessi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera Careggi, Florence, Italy
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy.,Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, Institute of Bioengineering, School of Engineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
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36
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Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer's Disease Diagnosis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:3198066. [PMID: 36818579 PMCID: PMC9931465 DOI: 10.1155/2023/3198066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/15/2022] [Accepted: 01/11/2023] [Indexed: 02/11/2023]
Abstract
Biomarkers based on resting-state electroencephalography (EEG) signals have emerged as a promising tool in the study of Alzheimer's disease (AD). Recently, a state-of-the-art biomarker was found based on visual inspection of power modulation spectrograms where three "patches" or regions from the modulation spectrogram were proposed and used for AD diagnostics. Here, we propose the use of deep neural networks, in particular convolutional neural networks (CNNs) combined with saliency maps, trained on power modulation spectrogram inputs to find optimal patches in a data-driven manner. Experiments are conducted on EEG data collected from fifty-four participants, including 20 healthy controls, 19 patients with mild AD, and 15 moderate-to-severe AD patients. Five classification tasks are explored, including the three-class problem, early-stage detection (control vs. mild-AD), and severity level detection (mild vs. moderate-to-severe). Experimental results show the proposed biomarkers outperform the state-of-the-art benchmark across all five tasks, as well as finding complementary modulation spectrogram regions not previously seen via visual inspection. Lastly, experiments are conducted on the proposed biomarkers to test their sensitivity to age, as this is a known confound in AD characterization. Across all five tasks, none of the proposed biomarkers showed a significant relationship with age, thus further highlighting their usefulness for automated AD diagnostics.
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37
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Lista S, Vergallo A, Teipel SJ, Lemercier P, Giorgi FS, Gabelle A, Garaci F, Mercuri NB, Babiloni C, Gaire BP, Koronyo Y, Koronyo-Hamaoui M, Hampel H, Nisticò R. Determinants of approved acetylcholinesterase inhibitor response outcomes in Alzheimer's disease: relevance for precision medicine in neurodegenerative diseases. Ageing Res Rev 2023; 84:101819. [PMID: 36526257 DOI: 10.1016/j.arr.2022.101819] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 11/11/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
Acetylcholinesterase inhibitors (ChEI) are the global standard of care for the symptomatic treatment of Alzheimer's disease (AD) and show significant positive effects in neurodegenerative diseases with cognitive and behavioral symptoms. Although experimental and large-scale clinical evidence indicates the potential long-term efficacy of ChEI, primary outcomes are generally heterogeneous across outpatient clinics and regional healthcare systems. Sub-optimal dosing or slow tapering, heterogeneous guidelines about the timing for therapy initiation (prodromal versus dementia stages), healthcare providers' ambivalence to treatment, lack of disease awareness, delayed medical consultation, prescription of ChEI in non-AD cognitive disorders, contribute to the negative outcomes. We present an evidence-based overview of determinants, spanning genetic, molecular, and large-scale networks, involved in the response to ChEI in patients with AD and other neurodegenerative diseases. A comprehensive understanding of cerebral and retinal cholinergic system dysfunctions along with ChEI response predictors in AD is crucial since disease-modifying therapies will frequently be prescribed in combination with ChEI. Therapeutic algorithms tailored to genetic, biological, clinical (endo)phenotypes, and disease stages will help leverage inter-drug synergy and attain optimal combined response outcomes, in line with the precision medicine model.
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Affiliation(s)
- Simone Lista
- Memory Resources and Research Center (CMRR), Neurology Department, Gui de Chauliac University Hospital, Montpellier, France; School of Pharmacy, University of Rome "Tor Vergata", Rome, Italy.
| | - Andrea Vergallo
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE) Rostock/Greifswald, Rostock, Germany; Department of Psychosomatic Medicine and Psychotherapy, University Medicine Rostock, Rostock, Germany
| | - Pablo Lemercier
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Filippo Sean Giorgi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Audrey Gabelle
- Memory Resources and Research Center (CMRR), Neurology Department, Gui de Chauliac University Hospital, Montpellier, France
| | - Francesco Garaci
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy; Casa di Cura "San Raffaele Cassino", Cassino, Italy
| | - Nicola B Mercuri
- Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy; IRCCS Santa Lucia Foundation, Rome, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, Italy
| | - Bhakta Prasad Gaire
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences, Division of Applied Cell Biology and Physiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harald Hampel
- Sorbonne University, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Robert Nisticò
- School of Pharmacy, University of Rome "Tor Vergata", Rome, Italy; Laboratory of Pharmacology of Synaptic Plasticity, EBRI Rita Levi-Montalcini Foundation, Rome, Italy.
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Rosales-Lagarde A, Cubero-Rego L, Menéndez-Conde F, Rodríguez-Torres EE, Itzá-Ortiz B, Martínez-Alcalá C, Vázquez-Tagle G, Vázquez-Mendoza E, Eraña Díaz ML. Dissociation of Arousal Index Between REM and NREM Sleep in Elderly Adults with Cognitive Impairment, No Dementia: A Pilot Study. J Alzheimers Dis 2023; 95:477-491. [PMID: 37574730 DOI: 10.3233/jad-230101] [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: 08/15/2023]
Abstract
BACKGROUND Sleep disruption in elderly has been associated with an increased risk of cognitive impairment and its transition into Alzheimer's disease (AD). High arousal indices (AIs) during sleep may serve as an early-stage biomarker of cognitive impairment non-dementia (CIND). OBJECTIVE Using full-night polysomnography (PSG), we investigated whether CIND is related to different AIs between NREM and REM sleep stages. METHODS Fourteen older adults voluntarily participated in this population-based study that included Mini-Mental State Examination, Neuropsi battery, Katz Index of Independence in Activities of Daily Living, and single-night PSG. Subjects were divided into two groups (n = 7 each) according to their results in Neuropsi memory and attention subtests: cognitively unimpaired (CU), with normal results; and CIND, with -2.5 standard deviations in memory and/or attention subtests. AIs per hour of sleep during N1, N2, N3, and REM stages were obtained and correlated with Neuropsi total score (NTS). RESULTS AI (REM) was significantly higher in CU group than in CIND group. For the total sample, a positive correlation between AI (REM) and NTS was found (r = 0.68, p = 0.006), which remained significant when controlling for the effect of age and education. In CIND group, the AI (N2) was significantly higher than the AI (REM) . CONCLUSION In CIND older adults, this attenuation of normal arousal mechanisms in REM sleep are dissociated from the relative excess of arousals observed in stage N2. We propose as probable etiology an early hypoactivity at the locus coeruleus noradrenergic system, associated to its early pathological damage, present in the AD continuum.
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Affiliation(s)
- Alejandra Rosales-Lagarde
- CONACyT Chairs, National Council of Science and Technology, Mexico
- National Institute of Psychiatry Ramón de la Fuente Muñiz, Mexico
| | - Lourdes Cubero-Rego
- Neurodevelopmental Research Unit, Institute of Neurobiology, National Autonomous University of Mexico, Campus Juriquilla-Queretaro, Querétaro, México
| | | | | | - Benjamín Itzá-Ortiz
- Mathematics Research Center, Autonomous University of the State of Hidalgo, Mexico
| | - Claudia Martínez-Alcalá
- CONACyT Chairs, National Council of Science and Technology, Mexico
- Institute of Health Sciences, Autonomous University of the State of Hidalgo, Mexico
| | | | | | - Marta L Eraña Díaz
- Center for Research in Engineering and Applied Sciences, Autonomous University of the State of Morelos, Mexico
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39
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Azami H, Daftarifard E, Humeau-Heurtier A, Fernandez A, Abasolo D, Rajji TK. Assessment and Comparison of Nonlinear Measures in Resting-State Magnetoencephalograms in Alzheimer's Disease and Mild Cognitive Impairment. J Alzheimers Dis 2023; 96:1151-1162. [PMID: 37980661 DOI: 10.3233/jad-230544] [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: 11/21/2023]
Abstract
BACKGROUND Nonlinear dynamical measures, such as fractal dimension (FD), entropy, and Lempel-Ziv complexity (LZC), have been extensively investigated individually for detecting information content in magnetoencephalograms (MEGs) from patients with Alzheimer's disease (AD). OBJECTIVE To compare systematically the performance of twenty conventional and recently introduced nonlinear dynamical measures in studying AD versus mild cognitive impairment (MCI) and healthy control (HC) subjects using MEG. METHODS We compared twenty nonlinear measures to distinguish MEG recordings from 36 AD (mean age = 74.06±6.95 years), 18 MCI (mean age = 74.89±5.57 years), and 26 HC subjects (mean age = 71.77±6.38 years) in different brain regions and also evaluated the effect of the length of MEG epochs on their performance. We also studied the correlation between these measures and cognitive performance based on the Mini-Mental State Examination (MMSE). RESULTS The results obtained by LZC, zero-crossing rate (ZCR), FD, and dispersion entropy (DispEn) measures showed significant differences among the three groups. There was no significant difference between HC and MCI. The highest Hedge's g effect sizes for HC versus AD and MCI versus AD were respectively obtained by Higuchi's FD (HFD) and fuzzy DispEn (FuzDispEn) in the whole brain and was most prominent in left lateral. The results obtained by HFD and FuzDispEn had a significant correlation with the MMSE scores. DispEn-based techniques, LZC, and ZCR, compared with HFD, were less sensitive to epoch length in distinguishing HC form AD. CONCLUSIONS FuzDispEn was the most consistent technique to distinguish MEG dynamical patterns in AD compared with HC and MCI.
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Affiliation(s)
- Hamed Azami
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Elham Daftarifard
- Department of Pharmaceutics, Mazandaran University of Medical Sciences, Sari, Iran
| | | | - Alberto Fernandez
- Department of Legal Medicine, Psychiatry and Pathology, Complutense University of Madrid, Madrid, Spain
| | - Daniel Abasolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, University of Surrey, Guildford, UK
| | - Tarek K Rajji
- Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada
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40
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Azami H, Moguilner S, Penagos H, Sarkis RA, Arnold SE, Gomperts SN, Lam AD. EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease. J Alzheimers Dis 2023; 91:1557-1572. [PMID: 36641682 PMCID: PMC10039707 DOI: 10.3233/jad-221152] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales. OBJECTIVE To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD. METHODS We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function. RESULTS SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power. CONCLUSION SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.
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Affiliation(s)
- Hamed Azami
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sebastian Moguilner
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Hector Penagos
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rani A. Sarkis
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Steven E. Arnold
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Stephen N. Gomperts
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Alice D. Lam
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
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Ganapathi AS, Glatt RM, Bookheimer TH, Popa ES, Ingemanson ML, Richards CJ, Hodes JF, Pierce KP, Slyapich CB, Iqbal F, Mattinson J, Lampa MG, Gill JM, Tongson YM, Wong CL, Kim M, Porter VR, Kesari S, Meysami S, Miller KJ, Bramen JE, Merrill DA, Siddarth P. Differentiation of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using qEEG/ERP-Based Cognitive Testing and Volumetric MRI in an Outpatient Specialty Memory Clinic. J Alzheimers Dis 2022; 90:1761-1769. [PMID: 36373320 PMCID: PMC9789480 DOI: 10.3233/jad-220616] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND Distinguishing between subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia in a scalable, accessible way is important to promote earlier detection and intervention. OBJECTIVE We investigated diagnostic categorization using an FDA-cleared quantitative electroencephalographic/event-related potential (qEEG/ERP)-based cognitive testing system (eVox® by Evoke Neuroscience) combined with an automated volumetric magnetic resonance imaging (vMRI) tool (Neuroreader® by Brainreader). METHODS Patients who self-presented with memory complaints were assigned to a diagnostic category by dementia specialists based on clinical history, neurologic exam, neuropsychological testing, and laboratory results. In addition, qEEG/ERP (n = 161) and quantitative vMRI (n = 111) data were obtained. A multinomial logistic regression model was used to determine significant predictors of cognitive diagnostic category (SCD, MCI, or dementia) using all available qEEG/ERP features and MRI volumes as the independent variables and controlling for demographic variables. Area under the Receiver Operating Characteristic curve (AUC) was used to evaluate the diagnostic accuracy of the prediction models. RESULTS The qEEG/ERP measures of Reaction Time, Commission Errors, and P300b Amplitude were significant predictors (AUC = 0.79) of cognitive category. Diagnostic accuracy increased when volumetric MRI measures, specifically left temporal lobe volume, were added to the model (AUC = 0.87). CONCLUSION This study demonstrates the potential of a primarily physiological diagnostic model for differentiating SCD, MCI, and dementia using qEEG/ERP-based cognitive testing, especially when combined with volumetric brain MRI. The accessibility of qEEG/ERP and vMRI means that these tools can be used as adjuncts to clinical assessments to help increase the diagnostic certainty of SCD, MCI, and dementia.
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Affiliation(s)
- Aarthi S. Ganapathi
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Ryan M. Glatt
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Tess H. Bookheimer
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Emily S. Popa
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | | | - Casey J. Richards
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - John F. Hodes
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Kyron P. Pierce
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Colby B. Slyapich
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Fatima Iqbal
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Jenna Mattinson
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Melanie G. Lampa
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Jaya M. Gill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,Providence Saint John’s Cancer Institute, Santa Monica, CA, USA
| | - Ynez M. Tongson
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Claudia L. Wong
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Mihae Kim
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Verna R. Porter
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Santosh Kesari
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,Providence Saint John’s Health Center, Santa Monica, CA, USA,Providence Saint John’s Cancer Institute, Santa Monica, CA, USA
| | - Somayeh Meysami
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,Providence Saint John’s Cancer Institute, Santa Monica, CA, USA
| | - Karen J. Miller
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, USA
| | - Jennifer E. Bramen
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,Providence Saint John’s Cancer Institute, Santa Monica, CA, USA
| | - David A. Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,Providence Saint John’s Health Center, Santa Monica, CA, USA,Providence Saint John’s Cancer Institute, Santa Monica, CA, USA,
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, USA,Correspondence to: David A. Merrill, MD, PhD, Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA. Tel.: +1 310 582 7547; Fax: +1 310 829 0124; E-mail:
| | - Prabha Siddarth
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA,
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, USA
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Rossini PM, Miraglia F, Vecchio F. Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis. Alzheimers Dement 2022; 18:2699-2706. [PMID: 35388959 PMCID: PMC10083993 DOI: 10.1002/alz.12645] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/12/2022] [Accepted: 02/03/2022] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence. METHODS A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods. RESULTS Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers. DISCUSSION On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.
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Affiliation(s)
- Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
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Herzog R, Rosas FE, Whelan R, Fittipaldi S, Santamaria-Garcia H, Cruzat J, Birba A, Moguilner S, Tagliazucchi E, Prado P, Ibanez A. Genuine high-order interactions in brain networks and neurodegeneration. Neurobiol Dis 2022; 175:105918. [PMID: 36375407 PMCID: PMC11195446 DOI: 10.1016/j.nbd.2022.105918] [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: 08/13/2022] [Revised: 10/18/2022] [Accepted: 11/03/2022] [Indexed: 11/13/2022] Open
Abstract
Brain functional networks have been traditionally studied considering only interactions between pairs of regions, neglecting the richer information encoded in higher orders of interactions. In consequence, most of the connectivity studies in neurodegeneration and dementia use standard pairwise metrics. Here, we developed a genuine high-order functional connectivity (HOFC) approach that captures interactions between 3 or more regions across spatiotemporal scales, delivering a more biologically plausible characterization of the pathophysiology of neurodegeneration. We applied HOFC to multimodal (electroencephalography [EEG], and functional magnetic resonance imaging [fMRI]) data from patients diagnosed with behavioral variant of frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and healthy controls. HOFC revealed large effect sizes, which, in comparison to standard pairwise metrics, provided a more accurate and parsimonious characterization of neurodegeneration. The multimodal characterization of neurodegeneration revealed hypo and hyperconnectivity on medium to large-scale brain networks, with a larger contribution of the former. Regions as the amygdala, the insula, and frontal gyrus were associated with both effects, suggesting potential compensatory processes in hub regions. fMRI revealed hypoconnectivity in AD between regions of the default mode, salience, visual, and auditory networks, while in bvFTD between regions of the default mode, salience, and somatomotor networks. EEG revealed hypoconnectivity in the γ band between frontal, limbic, and sensory regions in AD, and in the δ band between frontal, temporal, parietal and posterior areas in bvFTD, suggesting additional pathophysiological processes that fMRI alone can not capture. Classification accuracy was comparable with standard biomarkers and robust against confounders such as sample size, age, education, and motor artifacts (from fMRI and EEG). We conclude that high-order interactions provide a detailed, EEG- and fMRI compatible, biologically plausible, and psychopathological-specific characterization of different neurodegenerative conditions.
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Affiliation(s)
- Rubén Herzog
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile
| | - Fernando E Rosas
- Fundación para el Estudio de la Conciencia Humana (EcoH), Chile; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, UK; Data Science Institute, Imperial College London, UK; Centre for Complexity Science, Imperial College London, UK; Department of Informatics, University of Sussex, Brighton, UK
| | - Robert Whelan
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin 2, Ireland
| | - Sol Fittipaldi
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin 2, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | | | - Josephine Cruzat
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile
| | - Agustina Birba
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Sebastian Moguilner
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Enzo Tagliazucchi
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires, Argentina
| | - Pavel Prado
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Agustin Ibanez
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin 2, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), CA, USA.
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van Nifterick AM, Gouw AA, van Kesteren RE, Scheltens P, Stam CJ, de Haan W. A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing. Alzheimers Res Ther 2022; 14:101. [PMID: 35879779 PMCID: PMC9310500 DOI: 10.1186/s13195-022-01041-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 07/02/2022] [Indexed: 01/30/2023]
Abstract
Background Neuronal hyperexcitability and inhibitory interneuron dysfunction are frequently observed in preclinical animal models of Alzheimer’s disease (AD). This study investigates whether these microscale abnormalities explain characteristic large-scale magnetoencephalography (MEG) activity in human early-stage AD patients. Methods To simulate spontaneous electrophysiological activity, we used a whole-brain computational network model comprised of 78 neural masses coupled according to human structural brain topology. We modified relevant model parameters to simulate six literature-based cellular scenarios of AD and compare them to one healthy and six contrast (non-AD-like) scenarios. The parameters include excitability, postsynaptic potentials, and coupling strength of excitatory and inhibitory neuronal populations. Whole-brain spike density and spectral power analyses of the simulated data reveal mechanisms of neuronal hyperactivity that lead to oscillatory changes similar to those observed in MEG data of 18 human prodromal AD patients compared to 18 age-matched subjects with subjective cognitive decline. Results All but one of the AD-like scenarios showed higher spike density levels, and all but one of these scenarios had a lower peak frequency, higher spectral power in slower (theta, 4–8Hz) frequencies, and greater total power. Non-AD-like scenarios showed opposite patterns mainly, including reduced spike density and faster oscillatory activity. Human AD patients showed oscillatory slowing (i.e., higher relative power in the theta band mainly), a trend for lower peak frequency and higher total power compared to controls. Combining model and human data, the findings indicate that neuronal hyperactivity can lead to oscillatory slowing, likely due to hyperexcitation (by hyperexcitability of pyramidal neurons or greater long-range excitatory coupling) and/or disinhibition (by reduced excitability of inhibitory interneurons or weaker local inhibitory coupling strength) in early AD. Conclusions Using a computational brain network model, we link findings from different scales and models and support the hypothesis of early-stage neuronal hyperactivity underlying E/I imbalance and whole-brain network dysfunction in prodromal AD. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01041-4.
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Wu JY, Ching CTS, Wang HMD, Liao LD. Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications. BIOSENSORS 2022; 12:1097. [PMID: 36551064 PMCID: PMC9776100 DOI: 10.3390/bios12121097] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 11/15/2022] [Indexed: 06/17/2023]
Abstract
Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.
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Affiliation(s)
- Ju-Yu Wu
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli County 35053, Taiwan
- Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, South District, Taichung City 402, Taiwan
| | - Congo Tak-Shing Ching
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, South District, Taichung City 402, Taiwan
- Department of Electrical Engineering, National Chi Nan University, No. 1 University Road, Puli Township, Nantou County 545301, Taiwan
| | - Hui-Min David Wang
- Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, South District, Taichung City 402, Taiwan
- Graduate Institute of Biomedical Engineering, National Chung Hsing University, South District, Taichung City 402, Taiwan
| | - Lun-De Liao
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli County 35053, Taiwan
- Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, South District, Taichung City 402, Taiwan
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Kandiah N, Choi SH, Hu CJ, Ishii K, Kasuga K, Mok VC. Current and Future Trends in Biomarkers for the Early Detection of Alzheimer's Disease in Asia: Expert Opinion. J Alzheimers Dis Rep 2022; 6:699-710. [PMID: 36606209 PMCID: PMC9741748 DOI: 10.3233/adr-220059] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/23/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) poses a substantial healthcare burden in the rapidly aging Asian population. Early diagnosis of AD, by means of biomarkers, can lead to interventions that might alter the course of the disease. The amyloid, tau, and neurodegeneration (AT[N]) framework, which classifies biomarkers by their core pathophysiological features, is a biomarker measure of amyloid plaques and neurofibrillary tangles. Our current AD biomarker armamentarium, comprising neuroimaging biomarkers and cerebrospinal fluid biomarkers, while clinically useful, may be invasive and expensive and hence not readily available to patients. Several studies have also investigated the use of blood-based measures of established core markers for detection of AD, such as amyloid-β and phosphorylated tau. Furthermore, novel non-invasive peripheral biomarkers and digital biomarkers could potentially expand access to early AD diagnosis to patients in Asia. Despite the multiplicity of established and potential biomarkers in AD, a regional framework for their optimal use to guide early AD diagnosis remains lacking. A group of experts from five regions in Asia gathered at a meeting in March 2021 to review the current evidence on biomarkers in AD diagnosis and discuss best practice around their use, with the goal of developing practical guidance that can be implemented easily by clinicians in Asia to support the early diagnosis of AD. This article summarizes recent key evidence on AD biomarkers and consolidates the experts' insights into the current and future use of these biomarkers for the screening and early diagnosis of AD in Asia.
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Affiliation(s)
- Nagaendran Kandiah
- Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore,Correspondence to: Nagaendran Kandiah, Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore 308232. Tel.: +65 6592 2653; Fax: +65 6339 2889; E-mail: ; ORCID: 0000-0001-9244-4298
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Republic of Korea
| | - Chaur-Jong Hu
- Department of Neurology, Dementia Center, Shuang Ho Hospital, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Kenji Ishii
- Team for Neuroimaging Research, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Kensaku Kasuga
- Department of Molecular Genetics, Center for Bioresources, Brain Research Institute, Niigata University, Niigata, Japan
| | - Vincent C.T. Mok
- Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China,Li Ka Shing Institute of Health Sciences, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong, China
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Fei HX, Qian CF, Wu XM, Wei YH, Huang JY, Wei LH. Role of micronutrients in Alzheimer's disease: Review of available evidence. World J Clin Cases 2022; 10:7631-7641. [PMID: 36158513 PMCID: PMC9372870 DOI: 10.12998/wjcc.v10.i22.7631] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/29/2022] [Accepted: 06/26/2022] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most common age-related neurodegenerative disorders that have been studied for more than 100 years. Although an increased level of amyloid precursor protein is considered a key contributor to the development of AD, the exact pathogenic mechanism remains known. Multiple factors are related to AD, such as genetic factors, aging, lifestyle, and nutrients. Both epidemiological and clinical evidence has shown that the levels of micronutrients, such as copper, zinc, and iron, are closely related to the development of AD. In this review, we summarize the roles of eight micronutrients, including copper, zinc, iron, selenium, silicon, manganese, arsenic, and vitamin D in AD based on recently published studies.
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Affiliation(s)
- Hong-Xin Fei
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Chao-Fan Qian
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Xiang-Mei Wu
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Yu-Hua Wei
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Jin-Yu Huang
- Department of Neurology, The First Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Li-Hua Wei
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
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Fleming V, Hom CL, Clare ICH, Hurd-Thomas SL, Krinsky-McHale S, Handen B, Hartley SL. Cognitive outcome measures for tracking Alzheimer's disease in Down syndrome. INTERNATIONAL REVIEW OF RESEARCH IN DEVELOPMENTAL DISABILITIES 2022; 62:227-263. [PMID: 37396708 PMCID: PMC10312212 DOI: 10.1016/bs.irrdd.2022.05.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Down syndrome (DS) is now viewed as a genetic type of Alzheimer's disease (AD), given the near-universal presence of AD pathology in middle adulthood and the elevated risk for developing clinical AD in DS. As the field of DS prepares for AD clinical intervention trials, there is a strong need to identify cognitive measures that are specific and sensitive to the transition from being cognitively stable to the prodromal (e.g., Mild Cognitive Impairment-Down syndrome) and clinical AD (e.g., Dementia) stages of the disease in DS. It is also important to determine cognitive measures that map onto biomarkers of early AD pathology during the transition from the preclinical to the prodromal stage of the disease, as this transition period is likely to be targeted and tracked in AD clinical trials. The present chapter discusses the current state of research on cognitive measures that could be used to screen/select study participants and as potential outcome measures in future AD clinical trials with adults with DS. In this chapter, we also identify key challenges that need to be overcome and questions that need to be addressed by the DS field as it prepares for AD clinical trials in the coming years.
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Affiliation(s)
- Victoria Fleming
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- School of Human Ecology, University of Wisconsin-Madison, Madison, WI, United States
| | - Christy L Hom
- Department of Psychiatry and Human Behavior, University of California, Irvine School of Medicine, Orange, CA, United States
| | - Isabel C H Clare
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | | | - Sharon Krinsky-McHale
- New York State Institute for Basic Research in Developmental Disabilities, Staten Island, NY, United States
| | - Benjamin Handen
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sigan L Hartley
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- School of Human Ecology, University of Wisconsin-Madison, Madison, WI, United States
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Mora Pinzon M, Krainer J, LeCaire T, Houston S, Green‐Harris G, Norris N, Barnes S, Clark LR, Gleason CE, Hermann BP, Ramon H, Buckingham W, Chin NA, Asthana S, Johnson SC, Walaszek A, Carlsson CM. The Wisconsin Alzheimer's Institute Dementia Diagnostic Clinic Network: A community of practice to improve dementia care. J Am Geriatr Soc 2022; 70:2121-2133. [PMID: 35362093 PMCID: PMC9542133 DOI: 10.1111/jgs.17768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/05/2022] [Accepted: 03/02/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND The Wisconsin Alzheimer's Institute (WAI) Dementia Diagnostic Clinic Network is a community of practice formed in 1998 as a collaboration of community-based clinics from various healthcare systems throughout the state. Its purpose is to promote the use of evidence-based strategies to provide high quality care throughout Wisconsin for people with dementia. The purpose of this study is to describe the use of a community of practice to facilitate education of healthcare providers on best practices in dementia care, and the implementation of an interprofessional approach to diagnose and manage dementia and related disorders. METHODS Cross-sectional study of the members of the WAI's Dementia Diagnosis Clinic Network. Characteristics of clinics and healthcare teams, learners' participation in educational events and educational outcomes were collected from evaluation forms. Number and characteristics of patients seen in the memory clinics were collected from de-identified data forms submitted by members to a centralized location for data analysis. RESULTS The clinic network currently has 38 clinics affiliated with 26 different healthcare systems or independent medical groups in 21 of 72 Wisconsin counties. Most (56%) are based in primary care, 15% in psychiatry, and 29% in neurology. Between 2018 and 2021, we received data on 4710 patients; 92% were ≥65 years old, 60% were female, and 92% were white. Network members meet in-person twice a year to learn about innovations in the field of dementia care and to share best practices. Educational events associated with the network are shown to be relevant, useful, and improve knowledge and skills of participants. CONCLUSION Communities of practice provide added value via shared best practices and educational resources, continuing education of the health workforce, continuous quality improvement of clinical practices, and adoption of new diagnostic and management approaches in dementia care.
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Affiliation(s)
- Maria Mora Pinzon
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
- Department of Family Medicine and Community HealthUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
| | - Jody Krainer
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
| | - Tamara LeCaire
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
| | - Stephanie Houston
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
| | - Gina Green‐Harris
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
| | - Nia Norris
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
| | - Stacy Barnes
- College of NursingMarquette UniversityMilwaukeeWisconsinUSA
| | - Lindsay R. Clark
- Department of Medicine, Division of Geriatrics and GerontologyUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- VA Geriatric Research, Education and Clinical Center (GRECC)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Carey E. Gleason
- Department of Medicine, Division of Geriatrics and GerontologyUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- VA Geriatric Research, Education and Clinical Center (GRECC)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Bruce P. Hermann
- Department of NeurologyUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | | | - Will Buckingham
- Center for Health Disparities Research (CHDR), School of Medicine and Public HealthUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Nathaniel A. Chin
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
- Department of Medicine, Division of Geriatrics and GerontologyUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Sanjay Asthana
- Department of Medicine, Division of Geriatrics and GerontologyUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- VA Geriatric Research, Education and Clinical Center (GRECC)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Sterling C. Johnson
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
- Department of Medicine, Division of Geriatrics and GerontologyUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- VA Geriatric Research, Education and Clinical Center (GRECC)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
| | - Art Walaszek
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Cynthia M. Carlsson
- Wisconsin Alzheimer's InstituteUniversity of Wisconsin, School of Medicine and Public HealthMadisonWisconsinUSA
- Department of Medicine, Division of Geriatrics and GerontologyUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
- VA Geriatric Research, Education and Clinical Center (GRECC)William S. Middleton Memorial Veterans HospitalMadisonWisconsinUSA
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Lozupone M, Berardino G, Mollica A, Sardone R, Dibello V, Zupo R, Lampignano L, Castellana F, Bortone I, Stallone R, Daniele A, Altamura M, Bellomo A, Solfrizzi V, Panza F. ALZT-OP1: An experimental combination regimen for the treatment of Alzheimer's Disease. Expert Opin Investig Drugs 2022; 31:759-771. [PMID: 35758153 DOI: 10.1080/13543784.2022.2095261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
INTRODUCTION For Alzheimer's disease (AD) treatment, US FDA granted accelerated approval for aducanumab due to its amyloid-β (Aβ)-lowering effects, notwithstanding the reported poor correlation between amyloid plaque reduction and clinical change for this drug. The diversification of drug targets appears to be the future of the AD field and from this perspective, drugs modulating microglia dysfunction and combination treatment regimens offer some promise. AREAS COVERED The aim of the present article was to provide a comprehensive review of ALZT-OP1 (cromolyn sodium plus ibuprofen), an experimental combination treatment regimen for AD, discussing their mechanisms of action targeting Aβ and neuroinflammation, examining the role of microglia in AD and offering our own insights on the role of present and alternative approaches directed toward neuroinflammation. EXPERT OPINION Enrolling high-risk participants with elevated brain amyloid could help to slow cognitive decline in secondary prevention trials during AD preclinical stages. Long-term follow-up indicated that non-steroidal anti-inflammatory drugs use begun when the brain was still normal may benefit these patients, suggesting that the timing of therapy could be crucial. However, previous clinical failures and the present incomplete understanding of the Aβ pathophysiological role in AD put this novel experimental combination regimen at substantial risk of failure.
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Affiliation(s)
- Madia Lozupone
- Neurodegenerative Disease Unit, Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Berardino
- Psychiatric Unit, Department of Clinical & Experimental Medicine, University of Foggia, Foggia
| | - Anita Mollica
- Psychiatric Unit, Department of Clinical & Experimental Medicine, University of Foggia, Foggia
| | - Rodolfo Sardone
- Unit of Research Methodology and Data Sciences for Population Health, National Institute of Gastroenterology and Research Hospital IRCCS "S. De Bellis" Castellana Grotte, Bari, Italy
| | - Vittorio Dibello
- Department of Orofacial Pain and Dysfunction, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Roberta Zupo
- Unit of Research Methodology and Data Sciences for Population Health, National Institute of Gastroenterology and Research Hospital IRCCS "S. De Bellis" Castellana Grotte, Bari, Italy
| | - Luisa Lampignano
- Unit of Research Methodology and Data Sciences for Population Health, National Institute of Gastroenterology and Research Hospital IRCCS "S. De Bellis" Castellana Grotte, Bari, Italy
| | - Fabio Castellana
- Unit of Research Methodology and Data Sciences for Population Health, National Institute of Gastroenterology and Research Hospital IRCCS "S. De Bellis" Castellana Grotte, Bari, Italy
| | - Ilaria Bortone
- Unit of Research Methodology and Data Sciences for Population Health, National Institute of Gastroenterology and Research Hospital IRCCS "S. De Bellis" Castellana Grotte, Bari, Italy
| | - Roberta Stallone
- Neuroscience and Education, Human Resources Excellence in Research, University of Foggia, Foggia, Italy
| | - Antonio Daniele
- Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy.,Neurology Unit, IRCCS Fondazione Policlinico Universitario A. Gemelli, Rome, Italy
| | - Mario Altamura
- Psychiatric Unit, Department of Clinical & Experimental Medicine, University of Foggia, Foggia
| | - Antonello Bellomo
- Psychiatric Unit, Department of Clinical & Experimental Medicine, University of Foggia, Foggia
| | - Vincenzo Solfrizzi
- "Cesare Frugoni" Internal and Geriatric Medicine and Memory Unit, University of Bari "Aldo Moro", Bari, Italy
| | - Francesco Panza
- Unit of Research Methodology and Data Sciences for Population Health, National Institute of Gastroenterology and Research Hospital IRCCS "S. De Bellis" Castellana Grotte, Bari, Italy
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