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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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Hoshi H, Hirata Y, Fukasawa K, Kobayashi M, Shigihara Y. Oscillatory characteristics of resting-state magnetoencephalography reflect pathological and symptomatic conditions of cognitive impairment. Front Aging Neurosci 2024; 16:1273738. [PMID: 38352236 PMCID: PMC10861731 DOI: 10.3389/fnagi.2024.1273738] [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: 08/07/2023] [Accepted: 01/12/2024] [Indexed: 02/16/2024] Open
Abstract
Background Dementia and mild cognitive impairment are characterised by symptoms of cognitive decline, which are typically assessed using neuropsychological assessments (NPAs), such as the Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). Magnetoencephalography (MEG) is a novel clinical assessment technique that measures brain activities (summarised as oscillatory parameters), which are associated with symptoms of cognitive impairment. However, the relevance of MEG and regional cerebral blood flow (rCBF) data obtained using single-photon emission computed tomography (SPECT) has not been examined using clinical datasets. Therefore, this study aimed to investigate the relationships among MEG oscillatory parameters, clinically validated biomarkers computed from rCBF, and NPAs using outpatient data retrieved from hospital records. Methods Clinical data from 64 individuals with mixed pathological backgrounds were retrieved and analysed. MEG oscillatory parameters, including relative power (RP) from delta to high gamma bands, mean frequency, individual alpha frequency, and Shannon's spectral entropy, were computed for each cortical region. For SPECT data, three pathological parameters-'severity', 'extent', and 'ratio'-were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved. Results MEG oscillatory parameters were correlated with eZIS parameters. The eZIS parameters associated with Alzheimer's disease pathology were reflected in theta power augmentation and slower shift of the alpha peak. Moreover, MEG oscillatory parameters were found to reflect NPAs. Global slowing and loss of diversity in neural oscillatory components correlated with MMSE and FAB scores, whereas the associations between eZIS parameters and NPAs were sparse. Conclusion MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care.
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Affiliation(s)
- Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | - Yoko Hirata
- Department of Neurosurgery, Kumagaya General Hospital, Kumagaya, Japan
| | | | - Momoko Kobayashi
- Precision Medicine Centre, Kumagaya General Hospital, Kumagaya, Japan
| | - Yoshihito Shigihara
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
- Precision Medicine Centre, Kumagaya General Hospital, Kumagaya, Japan
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Mehdipour P, Fathi N, Nosratabadi M. Personalized clinical managements through exploring circulating neural cells and electroencephalography. World J Exp Med 2023; 13:75-94. [PMID: 37767542 PMCID: PMC10520756 DOI: 10.5493/wjem.v13.i4.75] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 05/22/2023] [Accepted: 07/11/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND Since an initial diagnosis of Alzheimer disease (AD) in 1907, early detection, was unavailable through 116 years. Up-regulation of V-Ets erythroblastosis virus E26 oncogene homolog 2 (Ets2) is capable to enhance neuronal susceptibility and degeneration. Protein expression (PE) of Ets2 has functional impact on AD and Down's syndrome, with diverse intensity. PE of Ets2 has an influential pathogenic impact on AD. Clinical aspects of neurological disorders directly interact with psychological maladies. However, deterioration requires an early management including programmed based protection. AIM To include cell biology in neuro-genetics; personalized, prognostics, predictive, preventive, predisposing (5xP) platform, accompanied by stratifying brain channels behavior pre- and post-intervention by light music in the AD-patients. METHODS Include exploration of PE assay and electroencephalography of brain channels. The processes are applied according to: (1) Triangle style, by application of cellular network; and (2) PE assay of Ets2 in the peripheral blood of the patients with AD, by Manual single cell based analysis, and Flow-cytometry. (1) Applying the Genetic counselling and pedigree analysis; (2) considering the psychological status of the referral cases; (3) considering the macro-and/or micro-environmental factors; (4) performing the required Genetics' analysis; and (5) applying the required complementary test(s). RESULTS PE of Ets2 has pathogenic role in AD. PE unmasked the nature of heterogeneity/diversity/course of evolution by exploring Ets2, D1853N polymorphism in Ataxia Telangiectasia mutated gene (ATM), vascular endothelial growth factor (VEGF), epidermal growth factor (EGF) and course of evolution at the single cell level of the brain. Ets2 revealed different cellular behavior in the blood and suggested the strategy as 'Gene Product-Based Therapy' and the personalized managements for the patients. PE reflected weak expression of ATM, mosaic pattern of Ets2; remarkable expression of VEGF and EGF by highlighting an early detective platform, considering circulating neural cells (CNCs) and the required molecular investigation, for the target individual(s) predisposed to AD or other neural disease including brain neoplasia. Brain channels-cooperation with diverse/interactive-ratios lead to strategic balancing for improving the life-quality in AD. CONCLUSION We highlighted application of the single CNCs and correlated Ratio based between Brain channels by providing the 5xP personalized clinical management model for an early detection and therapy of the patients with AD and their targeted/predisposed relatives. Novel-evolutionary/hypothetic/heterogenic-results in brain-channels offer personalizd/constructive markers with unlimited cooperation in health and disease.
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Affiliation(s)
- Parvin Mehdipour
- Department of Medical Genetics, Tehran University of Medical Sciences, Tehran 14176-1315, Iran
| | - Nima Fathi
- Neuro-Science, Paarand Specialized Center for Human Enhancement, Tehran 157699304, Iran
| | - Masoud Nosratabadi
- Department of Research, Paarand Specialized Center for Human Enhancement, Tehran 157699304, Iran
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Liang B, Alosco ML, Armañanzas R, Martin BM, Tripodis Y, Stern RA, Prichep LS. Long-Term Changes in Brain Connectivity Reflected in Quantitative Electrophysiology of Symptomatic Former National Football League Players. J Neurotrauma 2023; 40:309-317. [PMID: 36324216 PMCID: PMC9902050 DOI: 10.1089/neu.2022.0029] [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] [Indexed: 11/06/2022] Open
Abstract
Exposure to repetitive head impacts (RHI) has been associated with long-term disturbances in cognition, mood, and neurobehavioral dysregulation, and reflected in neuroimaging. Distinct patterns of changes in quantitative features of the brain electrical activity (quantitative electroencephalogram [qEEG]) have been demonstrated to be sensitive to brain changes seen in neurodegenerative disorders and in traumatic brain injuries (TBI). While these qEEG biomarkers are highly sensitive at time of injury, the long-term effects of exposure to RHI on brain electrical activity are relatively unexplored. Ten minutes of eyes closed resting EEG data were collected from a frontal and frontotemporal electrode montage (BrainScope Food and Drug Administration-cleared EEG acquisition device), as well as assessments of neuropsychiatric function and age of first exposure (AFE) to American football. A machine learning methodology was used to derive a qEEG-based algorithm to discriminate former National Football League (NFL) players (n = 87, 55.40 ± 7.98 years old) from same-age men without history of RHI (n = 68, 54.94 ± 7.63 years old), and a second algorithm to discriminate former players with AFE <12 years (n = 33) from AFE ≥12 years (n = 54). The algorithm separating NFL retirees from controls had a specificity = 80%, a sensitivity = 60%, and an area under curve (AUC) = 0.75. Within the NFL population, the algorithm separating AFE <12 from AFE ≥12 resulted in a sensitivity = 76%, a specificity = 52%, and an AUC = 0.72. The presence of a profile of EEG abnormalities in the NFL retirees and in those with younger AFE includes features associated with neurodegeneration and the disruption of neuronal transmission between regions. These results support the long-term consequences of RHI and the potential of EEG as a biomarker of persistent changes in brain function.
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Affiliation(s)
- Bo Liang
- BrainScope Company, Chevy Chase, Maryland, USA
| | - Michael L. Alosco
- Boston University CTE Center, Boston University, Boston, Massachusetts, USA
- Department of Neurology, Boston University, Boston, Massachusetts, USA
| | - Ruben Armañanzas
- BrainScope Company, Chevy Chase, Maryland, USA
- Institute for Data Science and Artificial Intelligence, Universidad de Navarra, Pamplona, Spain
- Tecnun School of Engineering, Universidad de Navarra, Donostia-San Sebastian, Spain
| | - Brett M. Martin
- Boston University CTE Center, Boston University, Boston, Massachusetts, USA
| | - Yorghos Tripodis
- Boston University CTE Center, Boston University, Boston, Massachusetts, USA
- Department of Biostatistics, Boston University, Boston, Massachusetts, USA
| | - Robert A. Stern
- Boston University CTE Center, Boston University, Boston, Massachusetts, USA
- Department of Neurology, Boston University, Boston, Massachusetts, USA
- Departments of Neurosurgery and Anatomy & Neurobiology, Boston University, Boston, Massachusetts, USA
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5
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Giustiniani A, Danesin L, Bozzetto B, Macina A, Benavides-Varela S, Burgio F. Functional changes in brain oscillations in dementia: a review. Rev Neurosci 2023; 34:25-47. [PMID: 35724724 DOI: 10.1515/revneuro-2022-0010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/16/2022] [Indexed: 01/11/2023]
Abstract
A growing body of evidence indicates that several characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) play a functional role in cognition and could be linked to the progression of cognitive decline in some neurological diseases such as dementia. The present paper reviews previous studies investigating changes in brain oscillations associated to the most common types of dementia, namely Alzheimer's disease (AD), frontotemporal degeneration (FTD), and vascular dementia (VaD), with the aim of identifying pathology-specific patterns of alterations and supporting differential diagnosis in clinical practice. The included studies analysed changes in frequency power, functional connectivity, and event-related potentials, as well as the relationship between electrophysiological changes and cognitive deficits. Current evidence suggests that an increase in slow wave activity (i.e., theta and delta) as well as a general reduction in the power of faster frequency bands (i.e., alpha and beta) characterizes AD, VaD, and FTD. Additionally, compared to healthy controls, AD exhibits alteration in latencies and amplitudes of the most common event related potentials. In the reviewed studies, these changes generally correlate with performances in many cognitive tests. In conclusion, particularly in AD, neurophysiological changes can be reliable early markers of dementia.
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Affiliation(s)
| | - Laura Danesin
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
| | | | - AnnaRita Macina
- Department of Developmental Psychology and Socialization, University of Padua, via Venezia 8, 35131 Padova, Italy
| | - Silvia Benavides-Varela
- Department of Developmental Psychology and Socialization, University of Padua, via Venezia 8, 35131 Padova, Italy.,Department of Neuroscience, University of Padova, 35128 Padova, Italy.,Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Francesca Burgio
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
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Lopez S, Del Percio C, Lizio R, Noce G, Padovani A, Nobili F, Arnaldi D, Famà F, Moretti DV, Cagnin A, Koch G, Benussi A, Onofrj M, Borroni B, Soricelli A, Ferri R, Buttinelli C, Giubilei F, Güntekin B, Yener G, Stocchi F, Vacca L, Bonanni L, Babiloni C. Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms. Front Aging Neurosci 2023; 15:780014. [PMID: 36776437 PMCID: PMC9908964 DOI: 10.3389/fnagi.2023.780014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 01/05/2023] [Indexed: 01/28/2023] Open
Abstract
Introduction Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections). Methods Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used. Results Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants. Discussion In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.
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Affiliation(s)
- Susanna Lopez
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy,*Correspondence: Susanna Lopez, ✉
| | - Claudio Del Percio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Roberta Lizio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | | | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Dario Arnaldi
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Francesco Famà
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Davide V. Moretti
- Alzheimer’s Disease Rehabilitation Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Giacomo Koch
- Non-Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy,Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University “G. D’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Türkiye,Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Türkiye
| | - Görsev Yener
- Department of Neurology, Dokuz Eylül University Medical School, Izmir, Türkiye,Faculty of Medicine, Izmir University of Economics, Izmir, Türkiye
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy,Telematic University San Raffaele, Rome, Italy
| | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. D’Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy,San Raffaele of Cassino, Cassino, Italy
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Alexandersen CG, de Haan W, Bick C, Goriely A. A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer's disease. J R Soc Interface 2023; 20:20220607. [PMID: 36596460 PMCID: PMC9810432 DOI: 10.1098/rsif.2022.0607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Alzheimer's disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-β and tau affect neurons at the cellular scale. On a larger scale, Alzheimer's patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein, we model the spreading of both amyloid-β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-β and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-β and tau protein.
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Affiliation(s)
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Christian Bick
- Mathematical Institute, University of Oxford, Oxford, UK,Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands,Amsterdam Neuroscience—Systems and Network Neuroscience, Amsterdam, The Netherlands
| | - Alain Goriely
- Mathematical Institute, University of Oxford, Oxford, UK
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Babiloni C, Noce G, Di Bonaventura C, Lizio R, Eldellaa A, Tucci F, Salamone EM, Ferri R, Soricelli A, Nobili F, Famà F, Arnaldi D, Palma E, Cifelli P, Marizzoni M, Stocchi F, Bruno G, Di Gennaro G, Frisoni GB, Del Percio C. Alzheimer's Disease with Epileptiform EEG Activity: Abnormal Cortical Sources of Resting State Delta Rhythms in Patients with Amnesic Mild Cognitive Impairment. J Alzheimers Dis 2022; 88:903-931. [PMID: 35694930 DOI: 10.3233/jad-220442] [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: 12/11/2022]
Abstract
BACKGROUND Patients with amnesic mild cognitive impairment due to Alzheimer's disease (ADMCI) typically show a "slowing" of cortical resting-state eyes-closed electroencephalographic (rsEEG) rhythms. Some of them also show subclinical, non-convulsive, and epileptiform EEG activity (EEA) with an unclear relationship with that "slowing." OBJECTIVE Here we tested the hypothesis that the "slowing" of rsEEG rhythms is related to EEA in ADMCI patients. METHODS Clinical and instrumental datasets in 62 ADMCI patients and 38 normal elderly (Nold) subjects were available in a national archive. No participant had received a clinical diagnosis of epilepsy. The eLORETA freeware estimated rsEEG cortical sources. The area under the receiver operating characteristic curve (AUROCC) indexed the accuracy of eLORETA solutions in the classification between ADMCI-EEA and ADMCI-noEEA individuals. RESULTS EEA was observed in 15% (N = 8) of the ADMCI patients. The ADMCI-EEA group showed: 1) more abnormal Aβ 42 levels in the cerebrospinal fluid as compared to the ADMCI-noEEA group and 2) higher temporal and occipital delta (<4 Hz) rsEEG source activities as compared to the ADMCI-noEEA and Nold groups. Those source activities showed moderate accuracy (AUROCC = 0.70-0.75) in the discrimination between ADMCI-noEEA versus ADMCI-EEA individuals. CONCLUSION It can be speculated that in ADMCI-EEA patients, AD-related amyloid neuropathology may be related to an over-excitation in neurophysiological low-frequency (delta) oscillatory mechanisms underpinning cortical arousal and quiet vigilance.
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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
| | | | - Carlo Di Bonaventura
- Epilepsy Unit, Department of Neurosciences/Mental Health, Sapienza University of Rome, Rome, Italy
| | | | - Ali Eldellaa
- 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
| | - Enrico M Salamone
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy.,Epilepsy Unit, Department of Neurosciences/Mental Health, Sapienza University of Rome, Rome, Italy
| | | | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Flavio Nobili
- Clinical Neurology, IRCCS Hospital Policlinico San Martino, Genoa, Italy.,Department of Neuroscience (DiNOGMI), University of Genoa, Genoa, Italy
| | - Francesco Famà
- Clinical Neurology, IRCCS Hospital Policlinico San Martino, Genoa, Italy
| | - Dario Arnaldi
- Clinical Neurology, IRCCS Hospital Policlinico San Martino, Genoa, Italy
| | - Eleonora Palma
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy.,Pasteur Institute-Cenci Bolognetti Foundation, Rome, Italy
| | - Pierangelo Cifelli
- IRCCS Neuromed, Pozzilli, (IS), Italy.,Department of Applied and Biotechnological Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Giuseppe Bruno
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | | | - Giovanni B Frisoni
- Department of Applied and Biotechnological Clinical Sciences, University of L'Aquila, L'Aquila, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
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9
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REACTIVITY OF POSTERIOR CORTICAL ELECTROENCEPHALOGRAPHIC ALPHA RHYTHMS DURING EYES OPENING IN COGNITIVELY INTACT OLDER ADULTS AND PATIENTS WITH DEMENTIA DUE TO ALZHEIMER'S AND LEWY BODY DISEASES. Neurobiol Aging 2022; 115:88-108. [DOI: 10.1016/j.neurobiolaging.2022.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 03/17/2022] [Accepted: 04/02/2022] [Indexed: 12/19/2022]
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10
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Fang X, Klawohn J, De Sabatino A, Kundnani H, Ryan J, Yu W, Hajcak G. Accurate classification of depression through optimized machine learning models on high-dimensional noisy data. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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11
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Monllor P, Cervera-Ferri A, Lloret MA, Esteve D, Lopez B, Leon JL, Lloret A. Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules? Int J Mol Sci 2021; 22:10889. [PMID: 34639229 PMCID: PMC8509134 DOI: 10.3390/ijms221910889] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/26/2021] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.
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Affiliation(s)
- Paloma Monllor
- CIBERFES, Department of Physiology, Institute INCLIVA, Faculty of Medicine, Health Research University of Valencia, Avda. Blasco Ibanez 17, 46010 Valencia, Spain; (P.M.); (D.E.)
| | - Ana Cervera-Ferri
- Department of Human Anatomy and Embryology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain;
| | - Maria-Angeles Lloret
- Department of Clinical Neurophysiology, University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain;
| | - Daniel Esteve
- CIBERFES, Department of Physiology, Institute INCLIVA, Faculty of Medicine, Health Research University of Valencia, Avda. Blasco Ibanez 17, 46010 Valencia, Spain; (P.M.); (D.E.)
| | - Begoña Lopez
- Department of Neurology, University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain;
| | - Jose-Luis Leon
- Ascires Biomedical Group, Department of Neuroradiology, Hospital Clinico Universitario, 46010 Valencia, Spain;
| | - Ana Lloret
- CIBERFES, Department of Physiology, Institute INCLIVA, Faculty of Medicine, Health Research University of Valencia, Avda. Blasco Ibanez 17, 46010 Valencia, Spain; (P.M.); (D.E.)
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12
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Su R, Li X, Li Z, Han Y, Cui W, Xie P, Liu Y. Constructing biomarker for early diagnosis of aMCI based on combination of multiscale fuzzy entropy and functional brain connectivity. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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13
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Gurja JP, Muthukrishnan SP, Tripathi M, Sharma R. Reduced Resting-State Cortical Alpha Connectivity Reflects Distinct Functional Brain Dysconnectivity in Alzheimer's Disease and Mild Cognitive Impairment. Brain Connect 2021; 12:134-145. [PMID: 34030487 DOI: 10.1089/brain.2020.0926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background: Emerging evidence suggests distinct abnormal activity patterns during resting state in intrinsic functional brain networks in patients with neurodegenerative diseases, including Alzheimer's disease (AD) and mild cognitive impairment (MCI). This study aimed to identify the changes in the resting-state intracortical lagged phase synchronization derived from dense array electroencephalography (EEG) in AD and MCI. Methods: Resting-state current source density (CSD) and lagged phase synchronization between 84 regions of interest defined by Brodmann areas (BAs) for seven EEG frequency bands were investigated between the study groups (AD, MCI, and age-matched controls) using 128-channel EEG. Results: Reduced CSD and connectivity (large effect size, Cohen's d > 0.8) were found in AD and MCI compared with controls at alpha frequency. However, a positive correlation (r = 0.433; p = 0.044) of mini-mental state examination scores was found with BA 32-33 connectivity values in AD only. Conclusion: Reduced resting-state alpha 1 source connectivity in patient groups and correlation between attenuation of resting-state alpha 1 connectivity with cognitive decline in AD could indicate the disruption of inhibitory function of alpha rhythm leading to tonic unselective cortical excitation that affects attention and controlled access to stored information.
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Affiliation(s)
- John Preetham Gurja
- Stress and Cognitive Electroimaging Laboratory, Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Suriya Prakash Muthukrishnan
- Stress and Cognitive Electroimaging Laboratory, Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Ratna Sharma
- Stress and Cognitive Electroimaging Laboratory, Department of Physiology, All India Institute of Medical Sciences, New Delhi, India
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14
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Babiloni C, Arakaki X, Azami H, Bennys K, Blinowska K, Bonanni L, Bujan A, Carrillo MC, Cichocki A, de Frutos-Lucas J, Del Percio C, Dubois B, Edelmayer R, Egan G, Epelbaum S, Escudero J, Evans A, Farina F, Fargo K, Fernández A, Ferri R, Frisoni G, Hampel H, Harrington MG, Jelic V, Jeong J, Jiang Y, Kaminski M, Kavcic V, Kilborn K, Kumar S, Lam A, Lim L, Lizio R, Lopez D, Lopez S, Lucey B, Maestú F, McGeown WJ, McKeith I, Moretti DV, Nobili F, Noce G, Olichney J, Onofrj M, Osorio R, Parra-Rodriguez M, Rajji T, Ritter P, Soricelli A, Stocchi F, Tarnanas I, Taylor JP, Teipel S, Tucci F, Valdes-Sosa M, Valdes-Sosa P, Weiergräber M, Yener G, Guntekin B. Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel. Alzheimers Dement 2021; 17:1528-1553. [PMID: 33860614 DOI: 10.1002/alz.12311] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 12/28/2020] [Accepted: 01/01/2021] [Indexed: 12/25/2022]
Abstract
The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy.,San Raffaele of Cassino, Cassino (FR), Italy
| | | | - Hamed Azami
- Department of Neurology and Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Karim Bennys
- Centre Mémoire de Ressources et de Recherche (CMRR), Centre Hospitalier, Universitaire de Montpellier, Montpellier, France
| | - Katarzyna Blinowska
- Institute of Biocybernetics, Warsaw, Poland.,Faculty of Physics University of Warsaw and Nalecz, Warsaw, Poland
| | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University "G. D'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Ana Bujan
- Psychological Neuroscience Lab, School of Psychology, University of Minho, Minho, Portugal
| | - Maria C Carrillo
- Division of Medical & Scientific Relations, Alzheimer's Association, Chicago, Illinois, USA
| | - Andrzej Cichocki
- Skolkowo Institute of Science and Technology (SKOLTECH), Moscow, Russia.,Systems Research Institute PAS, Warsaw, Poland.,Nicolaus Copernicus University (UMK), Torun, Poland
| | - Jaisalmer de Frutos-Lucas
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Bruno Dubois
- Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Institute of Memory and Alzheimer's Disease (IM2A), Paris, France.,ICM, INSERM U1127, CNRS UMR 7225, Sorbonne Université, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Rebecca Edelmayer
- Division of Medical & Scientific Relations, Alzheimer's Association, Chicago, Illinois, USA
| | - Gary Egan
- Foundation Director of the Monash Biomedical Imaging (MBI) Research Facilities, Monash University, Clayton, Australia
| | - Stephane Epelbaum
- Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Institute of Memory and Alzheimer's Disease (IM2A), Paris, France.,ICM, INSERM U1127, CNRS UMR 7225, Sorbonne Université, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Javier Escudero
- School of Engineering, Institute for Digital Communications, The University of Edinburgh, Edinburgh, UK
| | - Alan Evans
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Francesca Farina
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Keith Fargo
- Division of Medical & Scientific Relations, Alzheimer's Association, Chicago, Illinois, USA
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Giovanni Frisoni
- IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Harald Hampel
- GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Sorbonne University, Paris, France
| | | | - Vesna Jelic
- Division of Clinical Geriatrics, NVS Department, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Jaeseung Jeong
- Department of Bio and Brain Engineering/Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Yang Jiang
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Maciej Kaminski
- Faculty of Physics University of Warsaw and Nalecz, Warsaw, Poland
| | - Voyko Kavcic
- Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, UK
| | - Sanjeev Kumar
- Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Alice Lam
- MGH Epilepsy Service, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lew Lim
- Vielight Inc., Toronto, Ontario, Canada
| | | | - David Lopez
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Brendan Lucey
- Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - William J McGeown
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, UK
| | - Ian McKeith
- Newcastle upon Tyne, Translational and Clinical Research Institute, Newcastle University, UK
| | | | - Flavio Nobili
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy.,Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - John Olichney
- UC Davis Department of Neurology and Center for Mind and Brain, Davis, California, USA
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University "G. D'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Ricardo Osorio
- Center for Brain Health, Department of Psychiatry, NYU Langone Medical Center, New York, New York, USA
| | | | - Tarek Rajji
- Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Andrea Soricelli
- IRCCS SDN, Napoli, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Ioannis Tarnanas
- Global Brain Health Institute, University of California San Francisco, San Francisco, USA.,Global Brain Health Institute, Trinity College Dublin, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - John Paul Taylor
- Newcastle upon Tyne, Translational and Clinical Research Institute, Newcastle University, UK
| | - Stefan Teipel
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany.,German Center for Neurodegenerative Diseases (DZNE) - Rostock/Greifswald, Rostock, Germany
| | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Pedro Valdes-Sosa
- Cuban Neuroscience Center, Havana, Cuba.,Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Marco Weiergräber
- Experimental Neuropsychopharmacology, BfArM), Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, Bonn, Germany
| | - Gorsev Yener
- Departments of Neurosciences and Department of Neurology, Dokuz Eylül University Medical School, Izmir, Turkey
| | - Bahar Guntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey.,REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab, Istanbul Medipol University, Istanbul, Turkey
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15
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San-Martin R, Johns E, Quispe Mamani G, Tavares G, Phillips NA, Fraga FJ. A method for diagnosis support of mild cognitive impairment through EEG rhythms source location during working memory tasks. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102499] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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16
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Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsalikakis D, Tsipouras MG. Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. Int J Neural Syst 2021; 31:2130002. [PMID: 33588710 DOI: 10.1142/s0129065721300023] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GR50100, Greece.,Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Vasileios Christou
- Q Base R&D, Science & Technology Park of Epirus, University of Ioannina Campus, Ioannina GR45110, Greece.,Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Pantelis Angelidis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Dimitrios Tsalikakis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Markos G Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
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17
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Chávez-Fumagalli MA, Shrivastava P, Aguilar-Pineda JA, Nieto-Montesinos R, Del-Carpio GD, Peralta-Mestas A, Caracela-Zeballos C, Valdez-Lazo G, Fernandez-Macedo V, Pino-Figueroa A, Vera-Lopez KJ, Lino Cardenas CL. Diagnosis of Alzheimer's Disease in Developed and Developing Countries: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy. J Alzheimers Dis Rep 2021; 5:15-30. [PMID: 33681713 PMCID: PMC7902992 DOI: 10.3233/adr-200263] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The present systematic review and meta-analysis of diagnostic test accuracy summarizes the last three decades in advances on diagnosis of Alzheimer's disease (AD) in developed and developing countries. OBJECTIVE To determine the accuracy of biomarkers in diagnostic tools in AD, for example, cerebrospinal fluid, positron emission tomography (PET), and magnetic resonance imaging (MRI), etc. METHODS The authors searched PubMed for published studies from 1990 to April 2020 on AD diagnostic biomarkers. 84 published studies were pooled and analyzed in this meta-analysis and diagnostic accuracy was compared by summary receiver operating characteristic statistics. RESULTS Overall, 84 studies met the criteria and were included in a meta-analysis. For EEG, the sensitivity ranged from 67 to 98%, with a median of 80%, 95% CI [75, 91], tau-PET diagnosis sensitivity ranged from 76 to 97%, with a median of 94%, 95% CI [76, 97]; and MRI sensitivity ranged from 41 to 99%, with a median of 84%, 95% CI [81, 87]. Our results showed that tau-PET diagnosis had higher performance as compared to other diagnostic methods in this meta-analysis. CONCLUSION Our findings showed an important discrepancy in diagnostic data for AD between developed and developing countries, which can impact global prevalence estimation and management of AD. Also, our analysis found a better performance for the tau-PET diagnostic over other methods to diagnose AD patients, but the expense of tau-PET scan seems to be the limiting factor in the diagnosis of AD in developing countries such as those found in Asia, Africa, and Latin America.
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Affiliation(s)
- Miguel A. Chávez-Fumagalli
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Pallavi Shrivastava
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Jorge A. Aguilar-Pineda
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Rita Nieto-Montesinos
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Gonzalo Davila Del-Carpio
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Antero Peralta-Mestas
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Claudia Caracela-Zeballos
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Guillermo Valdez-Lazo
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Victor Fernandez-Macedo
- Division of Neurology, Psychiatry and Radiology of the National Hospital ESSALUD-HNCASE, Arequipa, Peru
| | - Alejandro Pino-Figueroa
- Department of Pharmaceutical Sciences, Massachusetts College of Pharmacy and Health Sciences, Boston, MA, USA
| | - Karin J. Vera-Lopez
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
| | - Christian L. Lino Cardenas
- Laboratory of Genomics and Neurovascular Diseases, Vicerrectorado de investigación, Universidad Católica de Santa Maria, Arequipa, Peru
- Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
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18
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Resting-state electroencephalographic delta rhythms may reflect global cortical arousal in healthy old seniors and patients with Alzheimer's disease dementia. Int J Psychophysiol 2020; 158:259-270. [DOI: 10.1016/j.ijpsycho.2020.08.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 08/25/2020] [Accepted: 08/27/2020] [Indexed: 12/23/2022]
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19
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Ferri R, Babiloni C, Karami V, Triggiani AI, Carducci F, Noce G, Lizio R, Pascarelli MT, Soricelli A, Amenta F, Bozzao A, Romano A, Giubilei F, Del Percio C, Stocchi F, Frisoni GB, Nobili F, Patanè L, Arena P. Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements. Clin Neurophysiol 2020; 132:232-245. [PMID: 33433332 DOI: 10.1016/j.clinph.2020.09.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 08/12/2020] [Accepted: 09/11/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both. METHODS For the classification exercise, the ANNs had two architectures that included stacked (autoencoding) hidden layers recreating input data in the output. The classification was based on LORETA source estimates from rsEEG activity recorded with 10-20 montage system (19 electrodes) and standard sMRI variables in 89 ADD and 45 healthy control participants taken from a national database. RESULTS The ANN with stacked autoencoders and a deep leaning model representing both ADD and control participants showed classification accuracies in discriminating them of 80%, 85%, and 89% using rsEEG, sMRI, and rsEEG + sMRI features, respectively. The two ANNs with stacked autoencoders and a deep leaning model specialized for either ADD or control participants showed classification accuracies of 77%, 83%, and 86% using the same input features. CONCLUSIONS The two architectures of ANNs using stacked (autoencoding) hidden layers consistently reached moderate to high accuracy in the discrimination between ADD and healthy control participants as a function of the rsEEG and sMRI features employed. SIGNIFICANCE The present results encourage future multi-centric, prospective and longitudinal cross-validation studies using high resolution EEG techniques and harmonized clinical procedures towards clinical applications of the present ANNs.
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Affiliation(s)
- Raffaele Ferri
- Department of Neurology I.C., Oasi Research Institute - IRCCS, Troina, Italy.
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino (FR), Italy
| | - Vania Karami
- Department of Pharmaceutical Sciences and Health Products, University of Camerino, Camerino, Italy
| | | | - Filippo Carducci
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy
| | | | | | - Maria T Pascarelli
- Department of Neurology I.C., Oasi Research Institute - IRCCS, Troina, Italy
| | - Andrea Soricelli
- IRCCS SDN, Napoli, Italy; Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Francesco Amenta
- Department of Pharmaceutical Sciences and Health Products, University of Camerino, Camerino, Italy
| | - Alessandro Bozzao
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Andrea Romano
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Giovanni B Frisoni
- LENITEM (Laboratory of Epidemiology, Neuroimaging and Telemedicine), IRCCS Centro "S. Giovanni di Dio-F.B.F.", Brescia, Italy; Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Flavio Nobili
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy; Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Italy
| | - Luca Patanè
- Dipartimento di Ingegneria, Università degli Studi di Messina, Messina, Italy
| | - Paolo Arena
- Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Catania, Italy
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20
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Farina FR, Emek-Savaş DD, Rueda-Delgado L, Boyle R, Kiiski H, Yener G, Whelan R. A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment. Neuroimage 2020; 215:116795. [PMID: 32278090 DOI: 10.1016/j.neuroimage.2020.116795] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 03/27/2020] [Accepted: 03/28/2020] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs = 1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs = 0.67-0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI.
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Affiliation(s)
- F R Farina
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
| | - D D Emek-Savaş
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, Izmir, 35160, Turkey; Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland
| | - L Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - R Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - H Kiiski
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Department of Neurology, Dokuz Eylul University Medical School, Izmir, 35340, Turkey
| | - R Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland.
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21
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Abstract
Currently established and employed biomarkers of Alzheimer's disease (AD) predominantly mirror AD-associated molecular and structural brain changes. While they are necessary for identifying disease-specific neuropathology, they lack a clear and robust relationship with the clinical presentation of dementia; they can be altered in healthy individuals, while they often inadequately mirror the degree of cognitive and functional deficits in affected subjects. There is growing evidence that synaptic loss and dysfunction are early events during the trajectory of AD pathogenesis that best correlate with the clinical symptoms, suggesting measures of brain functional deficits as candidate early markers of AD. Resting-state electroencephalography (EEG) is a widely available and noninvasive diagnostic method that provides direct insight into brain synaptic activity in real time. Quantitative EEG (qEEG) analysis additionally provides information on physiologically meaningful frequency components, dynamic alterations and topography of EEG signal generators, i.e. neuronal signaling. Numerous studies have shown that qEEG measures can detect disruptions in activity, topographical distribution and synchronization of neuronal (synaptic) activity such as generalized EEG slowing, reduced global synchronization and anteriorization of neuronal generators of fast-frequency resting-state EEG activity in patients along the AD continuum. Moreover, qEEG measures appear to correlate well with surrogate markers of AD neuropathology and discriminate between different types of dementia, making them promising low-cost and noninvasive markers of AD. Future large-scale longitudinal clinical studies are needed to elucidate the diagnostic and prognostic potential of qEEG measures as early functional markers of AD on an individual subject level.
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Affiliation(s)
- Una Smailovic
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden.
| | - Vesna Jelic
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital, Huddinge, Sweden
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22
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Cassani R, Falk TH. Alzheimer's Disease Diagnosis and Severity Level Detection Based on Electroencephalography Modulation Spectral "Patch" Features. IEEE J Biomed Health Inform 2019; 24:1982-1993. [PMID: 31725401 DOI: 10.1109/jbhi.2019.2953475] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Over the last two decades, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). Typically, resting-state EEG (rsEEG) signals have been used, and traditional frequency bands (delta, theta, alpha, beta and gamma) have been explored. Recent studies, however, have suggested that non-conventional bands may lead to improved diagnostic performance. In this work, we propose a new type of features derived from the 2-dimensional modulation spectral domain representation of the rsEEG signal in order to characterize the neuromodulatory deficit emergent with AD. The proposed features are computed as the power in specific "patches" or regions of interest in the power modulation spectrogram, which are shown to be highly discriminant of AD severity levels. The proposed features were compared with traditional features used in the rsEEG AD monitoring literature. Results showed the proposed features not only achieving improved performance at discriminating between healthy normal elderly controls (Nold) and AD patients with varying severity levels, but also at monitoring severity levels (i.e., mild AD versus moderate AD). Moreover, the proposed features were shown to outperform traditional rsEEG features. Finally, we validated the biological origin of the proposed features by using source localization and comparing the obtained results with ones reported in the AD literature.
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23
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Babiloni C, Del Percio C, Lizio R, Noce G, Lopez S, Soricelli A, Ferri R, Pascarelli MT, Catania V, Nobili F, Arnaldi D, Famà F, Aarsland D, Orzi F, Buttinelli C, Giubilei F, Onofrj M, Stocchi F, Vacca L, Stirpe P, Fuhr P, Gschwandtner U, Ransmayr G, Garn H, Fraioli L, Pievani M, Frisoni GB, D'Antonio F, De Lena C, Güntekin B, Hanoğlu L, Başar E, Yener G, Emek-Savaş DD, Triggiani AI, Franciotti R, Taylor JP, De Pandis MF, Bonanni L. Abnormalities of Resting State Cortical EEG Rhythms in Subjects with Mild Cognitive Impairment Due to Alzheimer's and Lewy Body Diseases. J Alzheimers Dis 2019; 62:247-268. [PMID: 29439335 DOI: 10.3233/jad-170703] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The present study tested the hypothesis that cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms reveal different abnormalities in cortical neural synchronization in groups of patients with mild cognitive impairment due to Alzheimer's disease (ADMCI) and dementia with Lewy bodies (DLBMCI) as compared to cognitively normal elderly (Nold) subjects. Clinical and rsEEG data in 30 ADMCI, 23 DLBMCI, and 30 Nold subjects were available in an international archive. Age, gender, and education were carefully matched in the three groups. The Mini-Mental State Evaluation (MMSE) score was matched between the ADMCI and DLBMCI groups. Individual alpha frequency peak (IAF) was used to determine the delta, theta, alpha1, alpha2, and alpha3 frequency band ranges. Fixed beta1, beta2, and gamma bands were also considered. eLORETA estimated the rsEEG cortical sources. Receiver operating characteristic curve (ROCC) classified these sources across individuals. Compared to Nold, IAF showed marked slowing in DLBMCI and moderate in ADMCI. Furthermore, the posterior alpha 2 and alpha 3 source activities were more abnormal in the ADMCI than the DLBMCI group, while widespread delta source activities were more abnormal in the DLBMCI than the ADMCI group. The posterior delta and alpha sources correlated with the MMSE score and correctly classified the Nold and MCI individuals (area under the ROCC >0.85). In conclusion, the ADMCI and DLBMCI patients showed different features of cortical neural synchronization at delta and alpha frequencies underpinning brain arousal and vigilance in the quiet wakefulness. Future prospective cross-validation studies will have to test the clinical validity of these rsEEG markers.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy.,Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | | | - Roberta Lizio
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy.,Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Giuseppe Noce
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Andrea Soricelli
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Raffaele Ferri
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Enna, Italy
| | - Maria Teresa Pascarelli
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Enna, Italy
| | - Valentina Catania
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Enna, Italy
| | - Flavio Nobili
- Department of Neuroscience (DiNOGMI), Clinical Neurology, University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dario Arnaldi
- Department of Neuroscience (DiNOGMI), Clinical Neurology, University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Francesco Famà
- Department of Neuroscience (DiNOGMI), Clinical Neurology, University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dag Aarsland
- Department of Old Age Psychiatry, King's College University, London, UK
| | - Francesco Orzi
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Paola Stirpe
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Peter Fuhr
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Ute Gschwandtner
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Gerhard Ransmayr
- Department of Neurology 2, Med Campus III, Kepler University Hospital, Medical Faculty of the Johannes Kepler University, Linz, Austria
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | | | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - 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
| | - Fabrizia D'Antonio
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Italy
| | - Carlo De Lena
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, University of Istanbul-Medipol, Istanbul, Turkey
| | - Erol Başar
- IBG, Departments of Neurology and Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Görsev Yener
- IBG, Departments of Neurology and Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Derya Durusu Emek-Savaş
- Department of Psychology and Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | | | - Raffaella Franciotti
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | | | | | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
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24
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Abnormalities of functional cortical source connectivity of resting-state electroencephalographic alpha rhythms are similar in patients with mild cognitive impairment due to Alzheimer's and Lewy body diseases. Neurobiol Aging 2019; 77:112-127. [PMID: 30797169 DOI: 10.1016/j.neurobiolaging.2019.01.013] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Revised: 01/15/2019] [Accepted: 01/16/2019] [Indexed: 02/01/2023]
Abstract
Previous evidence has shown different resting-state eyes-closed electroencephalographic delta (<4 Hz) and alpha (8-10.5 Hz) source connectivity in subjects with dementia due to Alzheimer's (ADD) and Lewy body (DLB) diseases. The present study tested if the same differences may be observed in the prodromal stages of mild cognitive impairment (MCI). Here, clinical and resting-state eyes-closed electroencephalographic data in age-, gender-, and education-matched 30 ADMCI, 23 DLBMCI, and 30 healthy elderly (Nold) subjects were available in our international archive. Mini-Mental State Evaluation (MMSE) score was matched in the ADMCI and DLBMCI groups. The eLORETA freeware estimated delta and alpha source connectivity by the tool called lagged linear connectivity (LLC). Area under receiver operating characteristic curve (AUROCC) indexed the classification accuracy among individuals. Results showed that widespread interhemispheric and intrahemispheric LLC solutions in alpha sources were abnormally lower in both MCI groups compared with the Nold group, but with no differences were found between the 2 MCI groups. AUROCCs of LLC solutions in alpha sources exhibited significant accuracies (0.72-0.75) in the discrimination of Nold versus ADMCI-DLBMCI individuals, but not between the 2 MCI groups. These findings disclose similar abnormalities in ADMCI and DLBMCI patients as revealed by alpha source connectivity. It can be speculated that source connectivity mostly reflects common cholinergic impairment in prodromal state of both AD and DLB, before a substantial dopaminergic derangement in the dementia stage of DLB.
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25
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Song Z, Deng B, Wang J, Wang R. Biomarkers for Alzheimer's Disease Defined by a Novel Brain Functional Network Measure. IEEE Trans Biomed Eng 2019; 66:41-49. [DOI: 10.1109/tbme.2018.2834546] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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26
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Cassani R, Estarellas M, San-Martin R, Fraga FJ, Falk TH. Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment. DISEASE MARKERS 2018; 2018:5174815. [PMID: 30405860 PMCID: PMC6200063 DOI: 10.1155/2018/5174815] [Citation(s) in RCA: 147] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/12/2018] [Accepted: 07/29/2018] [Indexed: 12/17/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.
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Affiliation(s)
- Raymundo Cassani
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
| | - Mar Estarellas
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
- Department of Bioengineering, Imperial College London, London, UK
| | - Rodrigo San-Martin
- Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Francisco J. Fraga
- Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Tiago H. Falk
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
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27
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Ishii R, Canuet L, Aoki Y, Hata M, Iwase M, Ikeda S, Nishida K, Ikeda M. Healthy and Pathological Brain Aging: From the Perspective of Oscillations, Functional Connectivity, and Signal Complexity. Neuropsychobiology 2018; 75:151-161. [PMID: 29466802 DOI: 10.1159/000486870] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Accepted: 01/14/2018] [Indexed: 12/24/2022]
Abstract
Healthy aging is associated with impairment in cognitive information processing. Several neuroimaging methods such as functional magnetic resonance imaging, positron emission tomography and near-infrared spectroscopy have been used to explore healthy and pathological aging by relying on hemodynamic or metabolic changes that occur in response to brain activity. Since electroencephalography (EEG) and magnetoencephalography (MEG) are able to measure neural activity directly with a high temporal resolution of milliseconds, these neurophysiological techniques are particularly important to investigate the dynamics of brain activity underlying neurocognitive aging. It is well known that age is a major risk factor for Alzheimer's disease (AD), and that synaptic dysfunction represents an early sign of this disease associated with hallmark neuropathological findings. However, the neurophysiological mechanisms underlying AD are not fully elucidated. This review addresses healthy and pathological brain aging from a neurophysiological perspective, focusing on oscillatory activity changes during the resting state, event-related potentials and stimulus-induced oscillatory responses during cognitive or motor tasks, functional connectivity between brain regions, and changes in signal complexity. We also highlight the accumulating evidence on age-related EEG/MEG changes and biological markers of brain neurodegeneration, including genetic factors, structural abnormalities on magnetic resonance images, and the biochemical changes associated with Aβ deposition and tau pathology.
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Affiliation(s)
- Ryouhei Ishii
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan.,Department of Palliative Care, Ashiya Municipal Hospital, Ashiya, Japan
| | - Leonides Canuet
- Department of Cognitive, Social and Organizational Psychology, La Laguna University, Tenerife, Spain
| | - Yasunori Aoki
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan.,Department of Psychiatry, Nissay Hospital, Osaka, Japan
| | - Masahiro Hata
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan
| | - Masao Iwase
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan
| | - Shunichiro Ikeda
- Department of Psychiatry, Kansai Medical University, Moriguchi, Japan
| | - Keiichiro Nishida
- Department of Psychiatry, Kansai Medical University, Moriguchi, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan
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28
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Babiloni C, Del Percio C, Lizio R, Noce G, Cordone S, Lopez S, Soricelli A, Ferri R, Pascarelli MT, Nobili F, Arnaldi D, Famà F, Aarsland D, Orzi F, Buttinelli C, Giubilei F, Onofrj M, Stocchi F, Stirpe P, Fuhr P, Gschwandtner U, Ransmayr G, Caravias G, Garn H, Sorpresi F, Pievani M, D'Antonio F, De Lena C, Güntekin B, Hanoğlu L, Başar E, Yener G, Emek-Savaş DD, Triggiani AI, Franciotti R, Frisoni GB, Bonanni L, De Pandis MF. Abnormalities of Cortical Neural Synchronization Mechanisms in Subjects with Mild Cognitive Impairment due to Alzheimer's and Parkinson's Diseases: An EEG Study. J Alzheimers Dis 2018. [PMID: 28621693 DOI: 10.3233/jad-160883] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The aim of this retrospective and exploratory study was that the cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms might reveal different abnormalities in cortical neural synchronization in groups of patients with mild cognitive impairment due to Alzheimer's disease (ADMCI) and Parkinson's disease (PDMCI) as compared to healthy subjects. Clinical and rsEEG data of 75 ADMCI, 75 PDMCI, and 75 cognitively normal elderly (Nold) subjects were available in an international archive. Age, gender, and education were carefully matched in the three groups. The Mini-Mental State Evaluation (MMSE) was matched between the ADMCI and PDMCI groups. Individual alpha frequency peak (IAF) was used to determine the delta, theta, alpha1, alpha2, and alpha3 frequency band ranges. Fixed beta1, beta2, and gamma bands were also considered. eLORETA estimated the rsEEG cortical sources. Receiver operating characteristic curve (ROC) classified these sources across individuals. Results showed that compared to the Nold group, the posterior alpha2 and alpha3 source activities were more abnormal in the ADMCI than the PDMCI group, while the parietal delta source activities were more abnormal in the PDMCI than the ADMCI group. The parietal delta and alpha sources correlated with MMSE score and correctly classified the Nold and diseased individuals (area under the ROC = 0.77-0.79). In conclusion, the PDMCI and ADMCI patients showed different features of cortical neural synchronization at delta and alpha frequencies underpinning brain arousal and vigilance in the quiet wakefulness. Future prospective cross-validation studies will have to test these rsEEG markers for clinical applications and drug discovery.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy.,Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | | | - Roberta Lizio
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy.,Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Giuseppe Noce
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy
| | - Susanna Cordone
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Andrea Soricelli
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Raffaele Ferri
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Enna, Italy
| | - Maria Teresa Pascarelli
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Enna, Italy
| | - Flavio Nobili
- Clinical Neurology, dept of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dario Arnaldi
- Clinical Neurology, dept of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Francesco Famà
- Clinical Neurology, dept of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dag Aarsland
- Department of Old Age Psychiatry, King's College University, London, UK
| | - Francesco Orzi
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Paola Stirpe
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Peter Fuhr
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Ute Gschwandtner
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Gerhard Ransmayr
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Georg Caravias
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | | | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizia D'Antonio
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Italy
| | - Carlo De Lena
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, University of Istanbul-Medipol, Istanbul, Turkey
| | - Erol Başar
- Department of Neurosciences and Department of Neurology, Dokuz Eylül University Medical School, Izmir, Turkey
| | - Görsev Yener
- Department of Psychology and Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Derya Durusu Emek-Savaş
- Department of Psychology and Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | | | - Raffaella Franciotti
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - 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
| | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
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Functional cortical source connectivity of resting state electroencephalographic alpha rhythms shows similar abnormalities in patients with mild cognitive impairment due to Alzheimer's and Parkinson's diseases. Clin Neurophysiol 2018; 129:766-782. [PMID: 29448151 DOI: 10.1016/j.clinph.2018.01.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 11/30/2017] [Accepted: 01/10/2018] [Indexed: 11/22/2022]
Abstract
OBJECTIVE This study tested the hypothesis that markers of functional cortical source connectivity of resting state eyes-closed electroencephalographic (rsEEG) rhythms may be abnormal in subjects with mild cognitive impairment due to Alzheimer's (ADMCI) and Parkinson's (PDMCI) diseases compared to healthy elderly subjects (Nold). METHODS rsEEG data had been collected in ADMCI, PDMCI, and Nold subjects (N = 75 for any group). eLORETA freeware estimated functional lagged linear connectivity (LLC) from rsEEG cortical sources. Area under receiver operating characteristic (AUROC) curve indexed the accuracy in the classification of Nold and MCI individuals. RESULTS Posterior interhemispheric and widespread intrahemispheric alpha LLC solutions were abnormally lower in both MCI groups compared to the Nold group. At the individual level, AUROC curves of LLC solutions in posterior alpha sources exhibited moderate accuracies (0.70-0.72) in the discrimination of Nold vs. ADMCI-PDMCI individuals. No differences in the LLC solutions were found between the two MCI groups. CONCLUSIONS These findings unveil similar abnormalities in functional cortical connectivity estimated in widespread alpha sources in ADMCI and PDMCI. This was true at both group and individual levels. SIGNIFICANCE The similar abnormality of alpha source connectivity in ADMCI and PDMCI subjects might reflect common cholinergic impairment.
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Babiloni C, Del Percio C, Lizio R, Noce G, Lopez S, Soricelli A, Ferri R, Nobili F, Arnaldi D, Famà F, Aarsland D, Orzi F, Buttinelli C, Giubilei F, Onofrj M, Stocchi F, Stirpe P, Fuhr P, Gschwandtner U, Ransmayr G, Garn H, Fraioli L, Pievani M, Frisoni GB, D'Antonio F, De Lena C, Güntekin B, Hanoğlu L, Başar E, Yener G, Emek-Savaş DD, Triggiani AI, Franciotti R, Taylor JP, Vacca L, De Pandis MF, Bonanni L. Abnormalities of resting-state functional cortical connectivity in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study. Neurobiol Aging 2017; 65:18-40. [PMID: 29407464 DOI: 10.1016/j.neurobiolaging.2017.12.023] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 12/21/2017] [Accepted: 12/21/2017] [Indexed: 11/30/2022]
Abstract
Previous evidence showed abnormal posterior sources of resting-state delta (<4 Hz) and alpha (8-12 Hz) rhythms in patients with Alzheimer's disease with dementia (ADD), Parkinson's disease with dementia (PDD), and Lewy body dementia (DLB), as cortical neural synchronization markers in quiet wakefulness. Here, we tested the hypothesis of additional abnormalities in functional cortical connectivity computed in those sources, in ADD, considered as a "disconnection cortical syndrome", in comparison with PDD and DLB. Resting-state eyes-closed electroencephalographic (rsEEG) rhythms had been collected in 42 ADD, 42 PDD, 34 DLB, and 40 normal healthy older (Nold) participants. Exact low-resolution brain electromagnetic tomography (eLORETA) freeware estimated the functional lagged linear connectivity (LLC) from rsEEG cortical sources in delta, theta, alpha, beta, and gamma bands. The area under receiver operating characteristic (AUROC) curve indexed the classification accuracy between Nold and diseased individuals (only values >0.7 were considered). Interhemispheric and intrahemispheric LLCs in widespread delta sources were abnormally higher in the ADD group and, unexpectedly, normal in DLB and PDD groups. Intrahemispheric LLC was reduced in widespread alpha sources dramatically in ADD, markedly in DLB, and moderately in PDD group. Furthermore, the interhemispheric LLC in widespread alpha sources showed lower values in ADD and DLB than PDD groups. At the individual level, AUROC curves of LLC in alpha sources exhibited better classification accuracies for the discrimination of ADD versus Nold individuals (0.84) than for DLB versus Nold participants (0.78) and PDD versus Nold participants (0.75). Functional cortical connectivity markers in delta and alpha sources suggest a more compromised neurophysiological reserve in ADD than DLB, at both group and individual levels.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy; Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy.
| | | | - Roberta Lizio
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy; Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Giuseppe Noce
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Andrea Soricelli
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy; Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Raffaele Ferri
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Enna, Italy
| | - Flavio Nobili
- Clinical Neurology, Department of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dario Arnaldi
- Clinical Neurology, Department of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Francesco Famà
- Clinical Neurology, Department of Neuroscience (DiNOGMI), University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dag Aarsland
- Department of Old Age Psychiatry, King's College University, London, UK
| | - Francesco Orzi
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Paola Stirpe
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Peter Fuhr
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Ute Gschwandtner
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Gerhard Ransmayr
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | | | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - 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
| | - Fabrizia D'Antonio
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Rome, Italy
| | - Carlo De Lena
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, University of Istanbul-Medipol, Istanbul, Turkey
| | - Erol Başar
- IBG, Departments of Neurology and Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Görsev Yener
- IBG, Departments of Neurology and Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Derya Durusu Emek-Savaş
- Department of Psychology and Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | | | - Raffaella Franciotti
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | | | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy; Casa di Cura Privata del Policlinico (CCPP) Milano SpA, Milan, Italy
| | | | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
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Babiloni C, Del Percio C, Lizio R, Noce G, Cordone S, Lopez S, Soricelli A, Ferri R, Pascarelli MT, Nobili F, Arnaldi D, Aarsland D, Orzi F, Buttinelli C, Giubilei F, Onofrj M, Stocchi F, Stirpe P, Fuhr P, Gschwandtner U, Ransmayr G, Caravias G, Garn H, Sorpresi F, Pievani M, Frisoni GB, D'Antonio F, De Lena C, Güntekin B, Hanoğlu L, Başar E, Yener G, Emek-Savaş DD, Triggiani AI, Franciotti R, De Pandis MF, Bonanni L. Abnormalities of cortical neural synchronization mechanisms in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study. Neurobiol Aging 2017; 55:143-158. [PMID: 28454845 DOI: 10.1016/j.neurobiolaging.2017.03.030] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 03/24/2017] [Accepted: 03/26/2017] [Indexed: 12/15/2022]
Abstract
The aim of this retrospective exploratory study was that resting state eyes-closed electroencephalographic (rsEEG) rhythms might reflect brain arousal in patients with dementia due to Alzheimer's disease dementia (ADD), Parkinson's disease dementia (PDD), and dementia with Lewy body (DLB). Clinical and rsEEG data of 42 ADD, 42 PDD, 34 DLB, and 40 healthy elderly (Nold) subjects were available in an international archive. Demography, education, and Mini-Mental State Evaluation score were not different between the patient groups. Individual alpha frequency peak (IAF) determined the delta, theta, alpha 1, alpha 2, and alpha 3 frequency bands. Fixed beta 1, beta 2, and gamma bands were also considered. rsEEG cortical sources were estimated by means of the exact low-resolution brain electromagnetic source tomography and were then classified across individuals, on the basis of the receiver operating characteristic curves. Compared to Nold, IAF showed marked slowing in PDD and DLB and moderate slowing in ADD. Furthermore, all patient groups showed lower posterior alpha 2 source activities. This effect was dramatic in ADD, marked in DLB, and moderate in PDD. These groups also showed higher occipital delta source activities, but this effect was dramatic in PDD, marked in DLB, and moderate in ADD. The posterior delta and alpha sources allowed good classification accuracy (approximately 0.85-0.90) between the Nold subjects and patients, and between ADD and PDD patients. In quiet wakefulness, delta and alpha sources unveiled different spatial and frequency features of the cortical neural synchronization underpinning brain arousal in ADD, PDD, and DLB patients. Future prospective cross-validation studies should test these rsEEG markers for clinical applications and drug discovery.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy; Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy.
| | | | - Roberta Lizio
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy; Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Giuseppe Noce
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy
| | - Susanna Cordone
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza", Rome, Italy
| | - Andrea Soricelli
- Department of Integrated Imaging, IRCCS SDN, Naples, Italy; Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Raffaele Ferri
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Italy
| | - Maria Teresa Pascarelli
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Italy
| | - Flavio Nobili
- Department of Neuroscience (DiNOGMI), Clinical Neurology, University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dario Arnaldi
- Department of Neuroscience (DiNOGMI), Clinical Neurology, University of Genoa and IRCCS AOU S Martino-IST, Genoa, Italy
| | - Dag Aarsland
- Department of Old Age Psychiatry, King's College University, London, UK
| | - Francesco Orzi
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza", Rome, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Paola Stirpe
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Peter Fuhr
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Ute Gschwandtner
- Universitätsspital Basel, Abteilung Neurophysiologie, Basel, Switzerland
| | - Gerhard Ransmayr
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Georg Caravias
- Department of Neurology and Psychiatry and Faculty of Medicine, Johannes Kepler University Linz, General Hospital of the City of Linz, Linz, Austria
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | | | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - 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
| | - Fabrizia D'Antonio
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Rome, Italy
| | - Carlo De Lena
- Department of Neurology and Psychiatry, Sapienza, University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, University of Istanbul-Medipol, Istanbul, Turkey
| | - Erol Başar
- Department of Neurosciences, Dokuz Eylül University Medical School, Izmir, Turkey; Department of Neurology, Dokuz Eylül University Medical School, Izmir, Turkey
| | - Görsev Yener
- Department of Psychology, Dokuz Eylül University, Izmir, Turkey; Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Derya Durusu Emek-Savaş
- Department of Psychology, Dokuz Eylül University, Izmir, Turkey; Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | | | - Raffaella Franciotti
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | | | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
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Triggiani AI, Bevilacqua V, Brunetti A, Lizio R, Tattoli G, Cassano F, Soricelli A, Ferri R, Nobili F, Gesualdo L, Barulli MR, Tortelli R, Cardinali V, Giannini A, Spagnolo P, Armenise S, Stocchi F, Buenza G, Scianatico G, Logroscino G, Lacidogna G, Orzi F, Buttinelli C, Giubilei F, Del Percio C, Frisoni GB, Babiloni C. Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks. Front Neurosci 2017; 10:604. [PMID: 28184183 PMCID: PMC5266711 DOI: 10.3389/fnins.2016.00604] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 12/19/2016] [Indexed: 11/13/2022] Open
Abstract
Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2–4 Hz), theta (4–8 Hz Hz), alpha1 (8–10.5 Hz), and alpha2 (10.5–13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification.
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Affiliation(s)
- Antonio I Triggiani
- Department of Clinical and Experimental Medicine, University of Foggia Foggia, Italy
| | | | - Antonio Brunetti
- Department of Electrical and Information Engineering, Polytechnic of Bari Bari, Italy
| | - Roberta Lizio
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza"Rome, Italy; Department of Neuroscience, IRCCS San Raffaele PisanaRome, Italy
| | - Giacomo Tattoli
- Department of Electrical and Information Engineering, Polytechnic of Bari Bari, Italy
| | - Fabio Cassano
- Department of Electrical and Information Engineering, Polytechnic of Bari Bari, Italy
| | - Andrea Soricelli
- Department of Integrated Imaging, IRCCS Istituto di Ricerca Diagnostica e NucleareNapoli, Italy; Department of Motor Sciences and Healthiness, University of Naples ParthenopeNaples, Italy
| | - Raffaele Ferri
- Department of Neurology, IRCCS Oasi Institute for Research on Mental Retardation and Brain Aging Enna, Italy
| | - Flavio Nobili
- Clinical Neurology Unit, Department of Neuroscience, University of Genoa and IRCCS Azienda Ospedaliera Universitaria San Martino-IST Genoa, Italy
| | - Loreto Gesualdo
- Dipartimento Emergenza e Trapianti d'Organi, University of Bari Bari, Italy
| | - Maria R Barulli
- Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. Panico Lecce, Italy
| | - Rosanna Tortelli
- Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. Panico Lecce, Italy
| | - Valentina Cardinali
- Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. PanicoLecce, Italy; Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari "Aldo Moro"Bari, Italy
| | - Antonio Giannini
- Department of Imaging-Division of Radiology, Hospital "Di Venere" Bari, Italy
| | | | - Silvia Armenise
- Division of Neuroradiology, "F. Ferrari" Hospital Lecce, Italy
| | - Fabrizio Stocchi
- Department of Neuroscience, IRCCS San Raffaele Pisana Rome, Italy
| | - Grazia Buenza
- Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. Panico Lecce, Italy
| | - Gaetano Scianatico
- Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. Panico Lecce, Italy
| | - Giancarlo Logroscino
- Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari "Aldo Moro", Pia Fondazione Cardinale G. PanicoLecce, Italy; Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari "Aldo Moro"Bari, Italy
| | - Giordano Lacidogna
- Center for Neuropsychological Research, Institute of Neurology of the Policlinico Gemelli/Catholic University of Rome Italy
| | - Francesco Orzi
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza" Rome, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza" Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, University of Rome "La Sapienza" Rome, Italy
| | - Claudio Del Percio
- Department of Integrated Imaging, IRCCS Istituto di Ricerca Diagnostica e Nucleare Napoli, Italy
| | - Giovanni B Frisoni
- Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS Centro "S. Giovanni di Dio-F.B.F."Brescia, Italy; Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of GenevaGeneva, Switzerland
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", University of Rome "La Sapienza"Rome, Italy; Department of Neuroscience, IRCCS San Raffaele PisanaRome, Italy
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