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Panda R, Vanhaudenhuyse A, Piarulli A, Annen J, Demertzi A, Alnagger N, Chennu S, Laureys S, Faymonville ME, Gosseries O. Altered Brain Connectivity and Network Topological Organization in a Non-ordinary State of Consciousness Induced by Hypnosis. J Cogn Neurosci 2023; 35:1394-1409. [PMID: 37315333 DOI: 10.1162/jocn_a_02019] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Hypnosis has been shown to be of clinical utility; however, its underlying neural mechanisms remain unclear. This study aims to investigate altered brain dynamics during the non-ordinary state of consciousness induced by hypnosis. We studied high-density EEG in 9 healthy participants during eyes-closed wakefulness and during hypnosis, induced by a muscle relaxation and eyes fixation procedure. Using hypotheses based on internal and external awareness brain networks, we assessed region-wise brain connectivity between six ROIs (right and left frontal, right and left parietal, upper and lower midline regions) at the scalp level and compared across conditions. Data-driven, graph-theory analyses were also carried out to characterize brain network topology in terms of brain network segregation and integration. During hypnosis, we observed (1) increased delta connectivity between left and right frontal, as well as between right frontal and parietal regions; (2) decreased connectivity for alpha (between right frontal and parietal and between upper and lower midline regions) and beta-2 bands (between upper midline and right frontal, frontal and parietal, also between upper and lower midline regions); and (3) increased network segregation (short-range connections) in delta and alpha bands, and increased integration (long-range connections) in beta-2 band. This higher network integration and segregation was measured bilaterally in frontal and right parietal electrodes, which were identified as central hub regions during hypnosis. This modified connectivity and increased network integration-segregation properties suggest a modification of the internal and external awareness brain networks that may reflect efficient cognitive-processing and lower incidences of mind-wandering during hypnosis.
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Affiliation(s)
| | | | | | - Jitka Annen
- University of Liège, Belgium
- University Hospital of Liège, Belgium
| | | | - Naji Alnagger
- University of Liège, Belgium
- University Hospital of Liège, Belgium
| | | | - Steven Laureys
- University of Liège, Belgium
- University Hospital of Liège, Belgium
- Laval University, Québec, Canada
| | | | - Olivia Gosseries
- University of Liège, Belgium
- University Hospital of Liège, Belgium
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2
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Berger M, Ryu D, Reese M, McGuigan S, Evered LA, Price CC, Scott DA, Westover MB, Eckenhoff R, Bonanni L, Sweeney A, Babiloni C. A Real-Time Neurophysiologic Stress Test for the Aging Brain: Novel Perioperative and ICU Applications of EEG in Older Surgical Patients. Neurotherapeutics 2023; 20:975-1000. [PMID: 37436580 PMCID: PMC10457272 DOI: 10.1007/s13311-023-01401-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/29/2023] [Indexed: 07/13/2023] Open
Abstract
As of 2022, individuals age 65 and older represent approximately 10% of the global population [1], and older adults make up more than one third of anesthesia and surgical cases in developed countries [2, 3]. With approximately > 234 million major surgical procedures performed annually worldwide [4], this suggests that > 70 million surgeries are performed on older adults across the globe each year. The most common postoperative complications seen in these older surgical patients are perioperative neurocognitive disorders including postoperative delirium, which are associated with an increased risk for mortality [5], greater economic burden [6, 7], and greater risk for developing long-term cognitive decline [8] such as Alzheimer's disease and/or related dementias (ADRD). Thus, anesthesia, surgery, and postoperative hospitalization have been viewed as a biological "stress test" for the aging brain, in which postoperative delirium indicates a failed stress test and consequent risk for later cognitive decline (see Fig. 3). Further, it has been hypothesized that interventions that prevent postoperative delirium might reduce the risk of long-term cognitive decline. Recent advances suggest that rather than waiting for the development of postoperative delirium to indicate whether a patient "passed" or "failed" this stress test, the status of the brain can be monitored in real-time via electroencephalography (EEG) in the perioperative period. Beyond the traditional intraoperative use of EEG monitoring for anesthetic titration, perioperative EEG may be a viable tool for identifying waveforms indicative of reduced brain integrity and potential risk for postoperative delirium and long-term cognitive decline. In principle, research incorporating routine perioperative EEG monitoring may provide insight into neuronal patterns of dysfunction associated with risk of postoperative delirium, long-term cognitive decline, or even specific types of aging-related neurodegenerative disease pathology. This research would accelerate our understanding of which waveforms or neuronal patterns necessitate diagnostic workup and intervention in the perioperative period, which could potentially reduce postoperative delirium and/or dementia risk. Thus, here we present recommendations for the use of perioperative EEG as a "predictor" of delirium and perioperative cognitive decline in older surgical patients.
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Affiliation(s)
- Miles Berger
- Department of Anesthesiology, Duke University Medical Center, Duke South Orange Zone Room 4315B, Box 3094, Durham, NC, 27710, USA.
- Duke Aging Center, Duke University Medical Center, Durham, NC, USA.
- Duke/UNC Alzheimer's Disease Research Center, Duke University Medical Center, Durham, NC, USA.
| | - David Ryu
- School of Medicine, Duke University, Durham, NC, USA
| | - Melody Reese
- Department of Anesthesiology, Duke University Medical Center, Duke South Orange Zone Room 4315B, Box 3094, Durham, NC, 27710, USA
- Duke Aging Center, Duke University Medical Center, Durham, NC, USA
| | - Steven McGuigan
- Department of Anaesthesia and Acute Pain Medicine, St Vincent's Hospital, Melbourne, VIC, Australia
- Department of Critical Care, School of Medicine, University of Melbourne, Melbourne, Australia
| | - Lisbeth A Evered
- Department of Anaesthesia and Acute Pain Medicine, St Vincent's Hospital, Melbourne, VIC, Australia
- Department of Critical Care, School of Medicine, University of Melbourne, Melbourne, Australia
- Weill Cornell Medicine, New York, NY, USA
| | - Catherine C Price
- Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - David A Scott
- Department of Anaesthesia and Acute Pain Medicine, St Vincent's Hospital, Melbourne, VIC, Australia
- Department of Critical Care, School of Medicine, University of Melbourne, Melbourne, Australia
| | - M Brandon Westover
- Department of Neurology, Beth Israel Deaconess Hospital, Boston, MA, USA
| | - Roderic Eckenhoff
- Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Aoife Sweeney
- School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
- San Raffaele of Cassino, Cassino, FR, Italy
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3
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Ulbl J, Rakusa M. The Importance of Subjective Cognitive Decline Recognition and the Potential of Molecular and Neurophysiological Biomarkers-A Systematic Review. Int J Mol Sci 2023; 24:10158. [PMID: 37373304 DOI: 10.3390/ijms241210158] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/01/2023] [Accepted: 06/10/2023] [Indexed: 06/29/2023] Open
Abstract
Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are early stages of Alzheimer's disease (AD). Neurophysiological markers such as electroencephalography (EEG) and event-related potential (ERP) are emerging as alternatives to traditional molecular and imaging markers. This paper aimed to review the literature on EEG and ERP markers in individuals with SCD. We analysed 30 studies that met our criteria, with 17 focusing on resting-state or cognitive task EEG, 11 on ERPs, and two on both EEG and ERP parameters. Typical spectral changes were indicative of EEG rhythm slowing and were associated with faster clinical progression, lower education levels, and abnormal cerebrospinal fluid biomarkers profiles. Some studies found no difference in ERP components between SCD subjects, controls, or MCI, while others reported lower amplitudes in the SCD group compared to controls. Further research is needed to explore the prognostic value of EEG and ERP in relation to molecular markers in individuals with SCD.
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Affiliation(s)
- Janina Ulbl
- Division of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
- Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia
| | - Martin Rakusa
- Division of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
- Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia
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4
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Spinelli G, Bakardjian H, Schwartz D, Potier MC, Habert MO, Levy M, Dubois B, George N. Theta Band-Power Shapes Amyloid-Driven Longitudinal EEG Changes in Elderly Subjective Memory Complainers At-Risk for Alzheimer's Disease. J Alzheimers Dis 2022; 90:69-84. [PMID: 36057818 DOI: 10.3233/jad-220204] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) includes progressive symptoms spread along a continuum of preclinical and clinical stages. Although numerous studies uncovered the neuro-cognitive changes of AD, very little is known on the natural history of brain lesions and modifications of brain networks in elderly cognitively-healthy memory complainers at risk of AD for carrying pathophysiological biomarkers (amyloidopathy and tauopathy). OBJECTIVE We analyzed resting-state electroencephalography (EEG) of 318 cognitively-healthy subjective memory complainers from the INSIGHT-preAD cohort at the time of their first visit (M0) and two-years later (M24). METHODS Using 18F-florbetapir PET-scanner, subjects were stratified between amyloid negative (A-; n = 230) and positive (A+; n = 88) groups. Differences between A+ and A-were estimated at source-level in each band-power of the EEG spectrum. RESULTS At M0, we found an increase of theta power in the mid-frontal cortex in A+ compared to A-. No significant association was found between mid-frontal theta and the individuals' cognitive performance. At M24, theta power increased in A+ relative to A-individuals in the posterior cingulate cortex and the pre-cuneus. Alpha band revealed a peculiar decremental trend in posterior brain regions in the A+ relative to the A-group only at M24. Theta power increase over the mid-frontal and mid-posterior cortices suggests an hypoactivation of the default-mode network in the A+ individuals and a non-linear longitudinal progression at M24. CONCLUSION We provide the first source-level longitudinal evidence on the impact of brain amyloidosis on the EEG dynamics of a large-scale, monocentric cohort of elderly individuals at-risk for AD.
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Affiliation(s)
- Giuseppe Spinelli
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Centre MEG-EEG, CENIR, Paris, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Paris, France
| | - Hovagim Bakardjian
- AP-HP, Hôpital de la Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Paris, France
| | | | - Marie-Claude Potier
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Centre MEG-EEG, CENIR, Paris, France
| | - Marie-Odile Habert
- Sorbonne Université, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, Paris, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Médecine Nucléaire, Paris, France.,Centre d'Acquisition et Traitement des Images (CATI), http://www.cati-neuroimaging.com
| | - Marcel Levy
- AP-HP, Hôpital de la Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Paris, France
| | - Bruno Dubois
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Centre MEG-EEG, CENIR, Paris, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Paris, France
| | - Nathalie George
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Centre MEG-EEG, CENIR, Paris, France
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5
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Devos H, Gustafson K, Liao K, Ahmadnezhad P, Estes B, Martin LE, Mahnken JD, Brooks WM, Burns JM. EEG/ERP evidence of possible hyperexcitability in older adults with elevated beta-amyloid. Transl Neurodegener 2022; 11:8. [PMID: 35139917 PMCID: PMC8827181 DOI: 10.1186/s40035-022-00282-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 01/12/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Although growing evidence links beta-amyloid (Aβ) and neuronal hyperexcitability in preclinical mouse models of Alzheimer's disease (AD), a similar association in humans is yet to be established. The first aim of the study was to determine the association between elevated Aβ (Aβ+) and cognitive processes measured by the P3 event-related potential (ERP) in cognitively normal (CN) older adults. The second aim was to compare the event-related power between CNAβ+ and CNAβ-. METHODS Seventeen CNAβ+ participants (age: 73 ± 5, 11 females, Montreal Cognitive Assessment [MoCA] score 26 ± 2) and 17 CNAβ- participants group-matched for age, sex, and MOCA completed a working memory task (n-back with n = 0, 1, 2) test while wearing a 256-channel electro-encephalography net. P3 peak amplitude and latency of the target, nontarget and task difference effect (nontarget-target), and event-related power in the delta, theta, alpha, and beta bands, extracted from Fz, Cz, and Pz, were compared between groups using linear mixed models. P3 amplitude of the task difference effect at Fz and event-related power in the delta band were considered main outcomes. Correlations of mean Aβ standard uptake value ratios (SUVR) using positron emission tomography with P3 amplitude and latency of the task difference effect were analyzed using Pearson Correlation Coefficient r. RESULTS The P3 peak amplitude of the task difference effect at Fz was lower in the CNAβ+ group (P = 0.048). Similarly, power was lower in the delta band for nontargets at Fz in the CNAβ+ participants (P = 0.04). The CNAβ+ participants also demonstrated higher theta and alpha power in channels at Cz and Pz, but no changes in P3 ERP. Strong correlations were found between the mean Aβ SUVR and the latency of the 1-back (r = - 0.69; P = 0.003) and 2-back (r = - 0.69; P = 0.004) of the task difference effect at channel Fz in the CNAβ+ group. CONCLUSIONS Our data suggest that the elevated amyloid in cognitively normal older adults is associated with neuronal hyperexcitability. The decreased P3 task difference likely reflects early impairments in working memory processes. Further research is warranted to determine the validity of ERP in predicting clinical, neurobiological, and functional manifestations of AD.
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Affiliation(s)
- Hannes Devos
- Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, KS, 66160, USA.
| | - Kathleen Gustafson
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, 66160, USA
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Ke Liao
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Pedram Ahmadnezhad
- Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Bradley Estes
- Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Laura E Martin
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, 66160, USA
- Department of Population Health, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Jonathan D Mahnken
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, 66160, USA
- University of Kansas Alzheimer's Disease Center, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - William M Brooks
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, 66160, USA
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, 66160, USA
- University of Kansas Alzheimer's Disease Center, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Jeffrey M Burns
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, 66160, USA
- University of Kansas Alzheimer's Disease Center, University of Kansas Medical Center, Kansas City, KS, 66160, USA
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6
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Annen J, Panda R, Martial C, Piarulli A, Nery G, Sanz LRD, Valdivia-Valdivia JM, Ledoux D, Gosseries O, Laureys S. Mapping the functional brain state of a world champion freediver in static dry apnea. Brain Struct Funct 2021; 226:2675-2688. [PMID: 34420066 DOI: 10.1007/s00429-021-02361-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 08/04/2021] [Indexed: 11/27/2022]
Abstract
Voluntary apnea showcases extreme human adaptability in trained individuals like professional free divers. We evaluated the psychological and physiological adaptation and the functional cerebral changes using electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) to 6.5 min of dry static apnea performed by a world champion free diver. Compared to resting state at baseline, breath holding was characterized by increased EEG power and functional connectivity in the alpha band, along with decreased delta band connectivity. fMRI connectivity was increased within the default mode network (DMN) and visual areas but decreased in pre- and postcentral cortices. While these changes occurred in regions overlapping with cerebral signatures of several meditation practices, they also display some unique features that suggest an altered somatosensory integration. As suggested by self-reports, these findings could reflect the ability of elite free divers to create a state of sensory dissociation when performing prolonged apnea.
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Affiliation(s)
- Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium.
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium.
| | - Rajanikant Panda
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Andrea Piarulli
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy
| | | | - Leandro R D Sanz
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Juan M Valdivia-Valdivia
- Department of Neurosurgery. St, Joseph's Hospital, Tampa, FL, USA
- International Association for Development of Apnea (AIDA International), Medical and Science Committee, Zurich, Switzerland
| | - Didier Ledoux
- Anesthesia and Intensive Care, GIGA Consciousness, ULiège, Liège, Belgium
- Intensive Care Department, University Hospital of Liège, Liège, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau, University Hospital of Liège, Liège, Belgium
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7
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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: 73] [Impact Index Per Article: 24.3] [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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8
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Chae S, Park J, Byun MS, Yi D, Lee JH, Byeon GH, Suk HW, Choi H, Park JE, Lee DY. Decreased Alpha Reactivity from Eyes-Closed to Eyes-Open in Non-Demented Older Adults with Alzheimer’s Disease: A Combined EEG and [18F]florbetaben PET Study. J Alzheimers Dis 2020; 77:1681-1692. [DOI: 10.3233/jad-200442] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The degree of alpha attenuation from eyes-closed (EC) to eyes-open (EO) has been suggested as a neural marker of cognitive health, and its disruption has been reported in patients with clinically defined Alzheimer’s disease (AD) dementia. Objective: We tested if EC-to-EO alpha reactivity was related to cerebral amyloid-β (Aβ) deposition during the early stage of AD. Methods: Non-demented participants aged ≥55 years who visited the memory clinic between March 2018 and June 2019 (N = 143; 67.8% female; mean age±standard deviation, 74.0±7.6 years) were included in the analyses. Based on the [18F]florbetaben positron emission tomography assessment, the participants were divided into Aβ+ (N = 70) and Aβ- (N = 73) groups. EEG was recorded during the 7 min EC condition followed by a 3 min EO phase, and a Fourier transform spectral analysis was performed. Results: A significant three-way interaction was detected among Aβ positivity, eye condition, and the laterality factor on alpha-band power after adjusting for age, sex, educational years, global cognition, depression, medication use, and white matter hyperintensities on magnetic resonance imaging (F = 5.987, p = 0.016); EC-to-EO alpha reactivity in the left hemisphere was significantly reduced in Aβ+ subjects without dementia compared with the others (F = 3.984, p = 0.048). Conclusion: Among mild cognitive impairment subjects, alpha reactivity additively contributed to predict cerebral Aβ positivity beyond the clinical predictors, including vascular risks, impaired memory function, and apolipoprotein E ɛ4. These findings support that EC-to-EO alpha reactivity acts as an early biomarker of cerebral Aβ deposition and is a useful measurement for screening early-stage AD.
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Affiliation(s)
- Soohyun Chae
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Jinsick Park
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Dahyun Yi
- Medical Research Center, Institute of Human Behavioral Medicine, Seoul National University, Seoul, South Korea
| | - Jun Ho Lee
- Department of Psychiatry, National Center for Mental Health, Seoul, South Korea
| | - Gi Hwan Byeon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Hye Won Suk
- Department of Psychology, Sogang University, Seoul, South Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jee Eun Park
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Medical Research Center, Institute of Human Behavioral Medicine, Seoul National University, Seoul, South Korea
- Interdisiplinary Program in Cognitive science, Seoul National University, Seoul, South Korea
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9
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Wang X, Huang W, Su L, Xing Y, Jessen F, Sun Y, Shu N, Han Y. Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease. Mol Neurodegener 2020; 15:55. [PMID: 32962744 PMCID: PMC7507636 DOI: 10.1186/s13024-020-00395-3] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/07/2020] [Indexed: 12/15/2022] Open
Abstract
Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.
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Affiliation(s)
- Xiaoqi Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Sino-Britain Centre for Cognition and Ageing Research, Southwest University, Chongqing, China
| | - Yue Xing
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Frank Jessen
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, 50937, Cologne, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Yu Sun
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. .,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China. .,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China. .,National Clinical Research Center for Geriatric Disorders, Beijing, China.
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10
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Babiloni C, Lopez S, Del Percio C, Noce G, Pascarelli MT, Lizio R, Teipel SJ, González-Escamilla G, Bakardjian H, George N, Cavedo E, Lista S, Chiesa PA, Vergallo A, Lemercier P, Spinelli G, Grothe MJ, Potier MC, Stocchi F, Ferri R, Habert MO, Fraga FJ, Dubois B, Hampel H. Resting-state posterior alpha rhythms are abnormal in subjective memory complaint seniors with preclinical Alzheimer's neuropathology and high education level: the INSIGHT-preAD study. Neurobiol Aging 2020; 90:43-59. [DOI: 10.1016/j.neurobiolaging.2020.01.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 01/24/2020] [Accepted: 01/26/2020] [Indexed: 01/05/2023]
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11
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Furutani N, Nariya Y, Takahashi T, Noto S, Yang AC, Hirosawa T, Kameya M, Minabe Y, Kikuchi M. Decomposed Temporal Complexity Analysis of Neural Oscillations and Machine Learning Applied to Alzheimer's Disease Diagnosis. Front Psychiatry 2020; 11:531801. [PMID: 33101073 PMCID: PMC7495507 DOI: 10.3389/fpsyt.2020.531801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 08/17/2020] [Indexed: 12/25/2022] Open
Abstract
Despite growing evidence of aberrant neuronal complexity in Alzheimer's disease (AD), it remains unclear how this variation arises. Neural oscillations reportedly comprise different functions depending on their own properties. Therefore, in this study, we investigated details of the complexity of neural oscillations by decomposing the oscillations into frequency, amplitude, and phase for AD patients. We applied resting-state magnetoencephalography (MEG) to 17 AD patients and 21 healthy control subjects. We first decomposed the source time series of the MEG signal into five intrinsic mode functions using ensemble empirical mode decomposition. We then analyzed the temporal complexities of these time series using multiscale entropy. Results demonstrated that AD patients had lower complexity on short time scales and higher complexity on long time scales in the alpha band in temporal regions of the brain. We evaluated the alpha band complexity further by decomposing it into amplitude and phase using Hilbert spectral analysis. Consequently, we found lower amplitude complexity and higher phase complexity in AD patients. Correlation analyses between spectral complexity and decomposed complexities revealed scale-dependency. Specifically, amplitude complexity was positively correlated with spectral complexity on short time scales, whereas phase complexity was positively correlated with spectral complexity on long time scales. Regarding the relevance of cognitive function to the complexity measures, the phase complexity on the long time scale was found to be correlated significantly with the Mini-Mental State Examination score. Additionally, we examined the diagnostic utility of the complexity characteristics using machine learning (ML) methods. We prepared a feature pool using multiple sparse autoencoders (SAEs), chose some discriminating features, and applied them to a support vector machine (SVM). Compared to the simple SVM and the SVM after feature selection (FS + SVM), the SVM with multiple SAEs (SAE + FS + SVM) had improved diagnostic accuracy. Through this study, we 1) advanced the understanding of neuronal complexity in AD patients using decomposed temporal complexity analysis and 2) demonstrated the effectiveness of combining ML methods with information about signal complexity for the diagnosis of AD.
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Affiliation(s)
- Naoki Furutani
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yuta Nariya
- Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Sarah Noto
- Faculty of Nursing, National College of Nursing, Tokyo, Japan
| | - Albert C Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Tetsu Hirosawa
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Masafumi Kameya
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yoshio Minabe
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
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12
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Choi J, Ku B, You YG, Jo M, Kwon M, Choi Y, Jung S, Ryu S, Park E, Go H, Kim G, Cha W, Kim JU. Resting-state prefrontal EEG biomarkers in correlation with MMSE scores in elderly individuals. Sci Rep 2019; 9:10468. [PMID: 31320666 PMCID: PMC6639387 DOI: 10.1038/s41598-019-46789-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 06/28/2019] [Indexed: 11/09/2022] Open
Abstract
We investigated whether cognitive decline could be explained by resting-state electroencephalography (EEG) biomarkers measured in prefrontal regions that reflect the slowing of intrinsic EEG oscillations. In an aged population dwelling in a rural community (total = 496, males = 165, females = 331), we estimated the global cognitive decline using the Mini-Mental State Examination (MMSE) and measured resting-state EEG parameters at the prefrontal regions of Fp1 and Fp2 in an eyes-closed state. Using a tertile split method, the subjects were classified as T3 (MMSE 28-30, N = 162), T2 (MMSE 25-27, N = 179), or T1 (MMSE ≤ 24, N = 155). The EEG slowing biomarkers of the median frequency, peak frequency and alpha-to-theta ratio decreased as the MMSE scores decreased from T2 to T1 for both sexes (-5.19 ≤ t-value ≤ -3.41 for males and -7.24 ≤ t-value ≤ -4.43 for females) after adjusting for age and education level. Using a double cross-validation procedure, we developed a prediction model for the MMSE scores using the EEG slowing biomarkers and demographic covariates of sex, age and education level. The maximum intraclass correlation coefficient between the MMSE scores and model-predicted values was 0.757 with RMSE = 2.685. The resting-state EEG biomarkers showed significant changes in people with early cognitive decline and correlated well with the MMSE scores. Resting-state EEG slowing measured in the prefrontal regions may be useful for the screening and follow-up of global cognitive decline in elderly individuals.
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Affiliation(s)
- Jungmi Choi
- Human Anti-Aging Standards Research Institute, Uiryeong-gun, Gyeongsangnam-do, Republic of Korea
| | - Boncho Ku
- Korea Institute of Oriental Medicine, Yusung-gu, Deajon, Republic of Korea
| | - Young Gooun You
- Uiryeong Community Health Center, Uiryeong-gun, Gyeongsangnam-do, Republic of Korea
| | - Miok Jo
- Uiryeong Community Health Center, Uiryeong-gun, Gyeongsangnam-do, Republic of Korea
| | - Minji Kwon
- Uiryeong Community Health Center, Uiryeong-gun, Gyeongsangnam-do, Republic of Korea
| | - Youyoung Choi
- Uiryeong Community Health Center, Uiryeong-gun, Gyeongsangnam-do, Republic of Korea
| | - Segyeong Jung
- Uiryeong Community Health Center, Uiryeong-gun, Gyeongsangnam-do, Republic of Korea
| | - Soyoung Ryu
- Uiryeong Community Health Center, Uiryeong-gun, Gyeongsangnam-do, Republic of Korea
| | - Eunjeong Park
- Uiryeong Community Health Center, Uiryeong-gun, Gyeongsangnam-do, Republic of Korea
| | - Hoyeon Go
- Semyung University, Jecheon-si, Chungcheongbuk-do, Republic of Korea
| | - Gahye Kim
- Korea Institute of Oriental Medicine, Yusung-gu, Deajon, Republic of Korea
| | - Wonseok Cha
- Human Anti-Aging Standards Research Institute, Uiryeong-gun, Gyeongsangnam-do, Republic of Korea
| | - Jaeuk U Kim
- Korea Institute of Oriental Medicine, Yusung-gu, Deajon, Republic of Korea.
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13
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Gaubert S, Raimondo F, Houot M, Corsi MC, Naccache L, Diego Sitt J, Hermann B, Oudiette D, Gagliardi G, Habert MO, Dubois B, De Vico Fallani F, Bakardjian H, Epelbaum S. EEG evidence of compensatory mechanisms in preclinical Alzheimer’s disease. Brain 2019; 142:2096-2112. [DOI: 10.1093/brain/awz150] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 04/03/2019] [Accepted: 04/07/2019] [Indexed: 11/13/2022] Open
Abstract
Abstract
Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer’s disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer’s disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer’s disease. However, the impact of amyloid-β deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer’s disease in 318 cognitively normal individuals aged 70–85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer’s disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer’s disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer’s disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to ‘suspected non-Alzheimer’s pathophysiology’. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer’s disease.
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Affiliation(s)
- Sinead Gaubert
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
| | - Federico Raimondo
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Laboratorio de Inteligencia Artificial Aplicada, Departamento de computación, FCEyN, Universidad de Buenos Aires, Argentina
- GIGA Consciousness, University of Liège, Liège, Belgium
- Coma Science Group, University Hospital of Liège, Liège, Belgium
| | - Marion Houot
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
- Center for Clinical Investigation (CIC) Neurosciences, Institut du Cerveau et de la Moelle épinière (ICM), Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’Hôpital, Paris, France
| | - Marie-Constance Corsi
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Lionel Naccache
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- AP-HP, Groupe hospitalier Pitié-Salpêtrière, Department of Neurophysiology, Paris, France
| | - Jacobo Diego Sitt
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Bertrand Hermann
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Delphine Oudiette
- AP-HP, Hôpital Pitié-Salpêtrière, Service des Pathologies du Sommeil (Département ‘R3S’), Paris, France
- Sorbonne Université, IHU@ICM, INSERM, CNRS UMR 7225, équipe MOV’IT, Paris, France
| | - Geoffroy Gagliardi
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
| | - Marie-Odile Habert
- Laboratoire d’Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U1146, CNRS UMR 7371, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Department of Nuclear Medicine, Paris, France
- Centre d’Acquisition et de Traitement des Images, CATI neuroimaging platform, France (www.cati-neuroimaging.com)
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
| | - Fabrizio De Vico Fallani
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Hovagim Bakardjian
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Institute of Memory and Alzheimer’s Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Paris, France
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14
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Maestú F, Cuesta P, Hasan O, Fernandéz A, Funke M, Schulz PE. The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease. Front Hum Neurosci 2019; 13:17. [PMID: 30792632 PMCID: PMC6374629 DOI: 10.3389/fnhum.2019.00017] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 01/15/2019] [Indexed: 12/22/2022] Open
Abstract
Current biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease. However, in spite of its added value they are not part of the standard of care in clinical practice in dementia. In this paper we review what these neurophysiological techniques can add to the early diagnosis of AD, whether results in both modalities are related to each other or not, as well as the need of its validation against current biomarkers. We discuss their potential implications for the better understanding of the pathophysiological mechanisms of the disease as well as the need of performing simultaneous M/EEG recordings to better understand discrepancies between these two techniques. Finally, more studies are needed studying M/EEG with amyloid and Tau biomarkers.
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Affiliation(s)
- Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Magnetic Source Imaging Unit, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering & IUNE Universidad de La Laguna, Tenerife, Spain
| | - Omar Hasan
- McGovern Medical School University of Texas Health Science Center, Houston, TX, United States
| | - Alberto Fernandéz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Legal Medicine, Psychiatry, and Pathology, Universidad Complutense de Madrid, Madrid, Spain
| | - Michael Funke
- Magnetic Source Imaging Unit, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States
| | - Paul E. Schulz
- McGovern Medical School University of Texas Health Science Center, Houston, TX, United States
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15
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Timmermans C, Smeets D, Verheyden J, Terzopoulos V, Anania V, Parizel PM, Maas A. Potential of a statistical approach for the standardization of multicenter diffusion tensor data: A phantom study. J Magn Reson Imaging 2019; 49:955-965. [PMID: 30605253 DOI: 10.1002/jmri.26333] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 08/21/2018] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Diffusion tensor imaging (DTI) parameters, such as fractional anisotropy (FA), allow examining the structural integrity of the brain. However, the true value of these parameters may be confounded by variability in MR hardware, acquisition parameters, and image quality. PURPOSE To examine the effects of confounding factors on FA and to evaluate the feasibility of statistical methods to model and reduce multicenter variability. STUDY TYPE Longitudinal multicenter study. PHANTOM DTI single strand phantom (HQ imaging). FIELD STRENGTH/SEQUENCE 3T diffusion tensor imaging. ASSESSMENTS Thirteen European imaging centers participated. DTI scans were acquired every 6 months and whenever maintenance or upgrades to the system were performed. A total of 64 scans were acquired in 2 years, obtained by three scanner vendors, using six individual head coils, and 12 software versions. STATISTICAL TESTS The variability in FA was assessed by the coefficients of variation (CoV). Several linear mixed effects models (LMEM) were developed and compared by means of the Akaike Information Criterion (AIC). RESULTS The CoV was 2.22% for mean FA and 18.40% for standard deviation of FA. The variables "site" (P = 9.26 × 10-5 ), "vendor" (P = 2.18 × 10-5 ), "head coil" (P = 9.00 × 10-4 ), "scanner drift," "bandwidth" (P = 0.033), "TE" (P = 8.20 × 10-6 ), "SNR" (P = 0.029) and "mean residuals" (P = 6.50 × 10-4 ) had a significant effect on the variability in mean FA. The variables "site" (P = 4.00 × 10-4 ), "head coil" (P = 2.00 × 10-4 ), "software" (P = 0.014), and "mean voxel outlier intensity count" (P = 1.10 × 10-4 ) had a significant effect on the variability in standard deviation of FA. The mean FA was best predicted by an LMEM that included "vendor" and the interaction term of "SNR" and "head coil" as model factors (AIC -347.98). In contrast, the standard deviation of FA was best predicted by an LMEM that included "vendor," "bandwidth," "TE," and the interaction term between "SNR" and "head coil" (AIC -399.81). DATA CONCLUSION Our findings suggest that perhaps statistical models seem promising to model the variability in quantitative DTI biomarkers for clinical routine and multicenter studies. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:955-965.
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Affiliation(s)
| | - Dirk Smeets
- icometrix, Research and Development, Leuven, Belgium
| | - Jan Verheyden
- icometrix, Research and Development, Leuven, Belgium
| | | | | | - Paul M Parizel
- Department of Radiology, Neuroradiology Division, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium
| | - Andrew Maas
- Department of Neurosurgery, Antwerp University Hospital and University of Antwerp, Antwerp, Belgium
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