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Burd SG, Lebedeva AV, Rubleva YV, Pantina NV, Efimenko AP, Kovaleva II. [EEG changes in patients with Alzheimer's disease]. Zh Nevrol Psikhiatr Im S S Korsakova 2024; 124:72-76. [PMID: 38696154 DOI: 10.17116/jnevro202412404272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
The prevalence of cognitive impairment is steadily increasing compared to previous years. According to the World Health Organization, the number of people living with dementia will increase reaching 82 million in 2030 and 152 million in 2050. The most common cause is Alzheimer's disease (AD). The pathophysiological process in AD begins several years before the onset of clinical symptoms; so identifying it at an early stage would likely improve the clinical prognosis. The article presents EEG changes in patients with AD, and discusses the possibility of using EEG as a screening method for examining patients with cognitive impairment.
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Affiliation(s)
- S G Burd
- Federal Center of Brain Research and Neurotechnologies, Moscow, Russia
- Pirogov Russian National Research Medical University, Moscow, Russia
- Scientific and Practical Center for Child Psychoneurology, Moscow, Russia
| | - A V Lebedeva
- Federal Center of Brain Research and Neurotechnologies, Moscow, Russia
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - Yu V Rubleva
- Federal Center of Brain Research and Neurotechnologies, Moscow, Russia
| | - N V Pantina
- Federal Center of Brain Research and Neurotechnologies, Moscow, Russia
| | - A P Efimenko
- Federal Center of Brain Research and Neurotechnologies, Moscow, Russia
| | - I I Kovaleva
- Federal Center of Brain Research and Neurotechnologies, Moscow, Russia
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2
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Choi SO, Choi JG, Yun JY. A Study of Brain Function Characteristics of Service Members at High Risk for Accidents in the Military. Brain Sci 2023; 13:1157. [PMID: 37626513 PMCID: PMC10452066 DOI: 10.3390/brainsci13081157] [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: 06/03/2023] [Revised: 07/23/2023] [Accepted: 07/29/2023] [Indexed: 08/27/2023] Open
Abstract
Military accidents are often associated with stress and depressive psychological conditions among soldiers, and they often fail to adapt to military life. Therefore, this study analyzes whether there are differences in EEG and pulse wave indices between general soldiers and three groups of soldiers who have not adapted to military life and are at risk of accidents. Data collection was carried out using a questionnaire and a device that can measure EEG and pulse waves, and data analysis was performed using SPSS. The results showed that the concentration level and brain activity indices were higher in the general soldiers and the soldiers in the first stage of accident risk. The body stress index was higher for each stage of accident risk, and the physical vitality index was higher for general soldiers. Therefore, it can be seen that soldiers who have not adapted to military life and are at risk of accidents have somewhat lower concentration and brain activity than general soldiers, and have symptoms of stress and lethargy. The results of this study will contribute to reducing human accidents through EEG and pulse wave measurements not only in the military but also in occupations with a high risk of accidents such as construction.
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Affiliation(s)
| | | | - Jong-Yong Yun
- Department of Protection and Safety Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
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3
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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: 0] [Impact Index Per Article: 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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4
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Andrade-Talavera Y, Fisahn A, Rodríguez-Moreno A. Timing to be precise? An overview of spike timing-dependent plasticity, brain rhythmicity, and glial cells interplay within neuronal circuits. Mol Psychiatry 2023; 28:2177-2188. [PMID: 36991134 PMCID: PMC10611582 DOI: 10.1038/s41380-023-02027-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/31/2023]
Abstract
In the mammalian brain information processing and storage rely on the complex coding and decoding events performed by neuronal networks. These actions are based on the computational ability of neurons and their functional engagement in neuronal assemblies where precise timing of action potential firing is crucial. Neuronal circuits manage a myriad of spatially and temporally overlapping inputs to compute specific outputs that are proposed to underly memory traces formation, sensory perception, and cognitive behaviors. Spike-timing-dependent plasticity (STDP) and electrical brain rhythms are suggested to underlie such functions while the physiological evidence of assembly structures and mechanisms driving both processes continues to be scarce. Here, we review foundational and current evidence on timing precision and cooperative neuronal electrical activity driving STDP and brain rhythms, their interactions, and the emerging role of glial cells in such processes. We also provide an overview of their cognitive correlates and discuss current limitations and controversies, future perspectives on experimental approaches, and their application in humans.
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Affiliation(s)
- Yuniesky Andrade-Talavera
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013, Seville, Spain.
| | - André Fisahn
- Department of Biosciences and Nutrition and Department of Women's and Children's Health, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Antonio Rodríguez-Moreno
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013, Seville, Spain.
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5
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Arroyo-García LE, Bachiller S, Ruiz R, Boza-Serrano A, Rodríguez-Moreno A, Deierborg T, Andrade-Talavera Y, Fisahn A. Targeting galectin-3 to counteract spike-phase uncoupling of fast-spiking interneurons to gamma oscillations in Alzheimer's disease. Transl Neurodegener 2023; 12:6. [PMID: 36740709 PMCID: PMC9901156 DOI: 10.1186/s40035-023-00338-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 01/19/2023] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive multifaceted neurodegenerative disorder for which no disease-modifying treatment exists. Neuroinflammation is central to the pathology progression, with evidence suggesting that microglia-released galectin-3 (gal3) plays a pivotal role by amplifying neuroinflammation in AD. However, the possible involvement of gal3 in the disruption of neuronal network oscillations typical of AD remains unknown. METHODS Here, we investigated the functional implications of gal3 signaling on experimentally induced gamma oscillations ex vivo (20-80 Hz) by performing electrophysiological recordings in the hippocampal CA3 area of wild-type (WT) mice and of the 5×FAD mouse model of AD. In addition, the recorded slices from WT mice under acute gal3 application were analyzed with RT-qPCR to detect expression of some neuroinflammation-related genes, and amyloid-β (Aβ) plaque load was quantified by immunostaining in the CA3 area of 6-month-old 5×FAD mice with or without Gal3 knockout (KO). RESULTS Gal3 application decreased gamma oscillation power and rhythmicity in an activity-dependent manner, which was accompanied by impairment of cellular dynamics in fast-spiking interneurons (FSNs) and pyramidal cells. We found that the gal3-induced disruption was mediated by the gal3 carbohydrate-recognition domain and prevented by the gal3 inhibitor TD139, which also prevented Aβ42-induced degradation of gamma oscillations. Furthermore, the 5×FAD mice lacking gal3 (5×FAD-Gal3KO) exhibited WT-like gamma network dynamics and decreased Aβ plaque load. CONCLUSIONS We report for the first time that gal3 impairs neuronal network dynamics by spike-phase uncoupling of FSNs, inducing a network performance collapse. Moreover, our findings suggest gal3 inhibition as a potential therapeutic strategy to counteract the neuronal network instability typical of AD and other neurological disorders encompassing neuroinflammation and cognitive decline.
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Affiliation(s)
- Luis Enrique Arroyo-García
- grid.465198.7Neuronal Oscillations Laboratory, Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 17164 Solna, Sweden
| | - Sara Bachiller
- grid.4514.40000 0001 0930 2361Experimental Neuroinflammation Laboratory, Department of Experimental Medical Science, Lund University, BMC B11, 221 84 Lund, Sweden ,grid.9224.d0000 0001 2168 1229Clinical Unit of Infectious Diseases, Microbiology and Parasitology, Institute of Biomedicine of Seville (IBiS), Virgen del Rocío University Hospital, CSIC, University of Seville, Seville, Spain
| | - Rocío Ruiz
- grid.9224.d0000 0001 2168 1229Department of Biochemistry and Molecular Biology, University of Seville, Calle Profesor García González Nº2, 41012 Seville, Spain
| | - Antonio Boza-Serrano
- grid.4514.40000 0001 0930 2361Experimental Neuroinflammation Laboratory, Department of Experimental Medical Science, Lund University, BMC B11, 221 84 Lund, Sweden ,grid.9224.d0000 0001 2168 1229Department of Biochemistry and Molecular Biology, University of Seville, Calle Profesor García González Nº2, 41012 Seville, Spain
| | - Antonio Rodríguez-Moreno
- grid.15449.3d0000 0001 2200 2355Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cellular Biology, Universidad Pablo de Olavide, Carretera de Utrera Km-1, 41013 Seville, Spain
| | - Tomas Deierborg
- grid.4514.40000 0001 0930 2361Experimental Neuroinflammation Laboratory, Department of Experimental Medical Science, Lund University, BMC B11, 221 84 Lund, Sweden
| | - Yuniesky Andrade-Talavera
- Neuronal Oscillations Laboratory, Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 17164, Solna, Sweden. .,Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cellular Biology, Universidad Pablo de Olavide, Carretera de Utrera Km-1, 41013, Seville, Spain.
| | - André Fisahn
- Neuronal Oscillations Laboratory, Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 17164, Solna, Sweden. .,Department of Biosciences and Nutrition, Neo, Karolinska Institutet, 141 83, Huddinge, Sweden.
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Azami H, Moguilner S, Penagos H, Sarkis RA, Arnold SE, Gomperts SN, Lam AD. EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer's Disease. J Alzheimers Dis 2023; 91:1557-1572. [PMID: 36641682 PMCID: PMC10039707 DOI: 10.3233/jad-221152] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales. OBJECTIVE To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD. METHODS We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function. RESULTS SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power. CONCLUSION SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.
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Affiliation(s)
- Hamed Azami
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sebastian Moguilner
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Hector Penagos
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Rani A. Sarkis
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Steven E. Arnold
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Stephen N. Gomperts
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Alice D. Lam
- Massachusetts Alzheimer's Disease Research Center, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA
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7
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Eyjolfsdottir H, Koenig T, Karami A, Almqvist P, Lind G, Linderoth B, Wahlberg L, Seiger Å, Darreh-Shori T, Eriksdotter M, Jelic V. Fast Alpha Activity in EEG of Patients With Alzheimer’s Disease Is Paralleled by Changes in Cognition and Cholinergic Markers During Encapsulated Cell Biodelivery of Nerve Growth Factor. Front Aging Neurosci 2022; 14:756687. [PMID: 35557841 PMCID: PMC9085576 DOI: 10.3389/fnagi.2022.756687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 03/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background Basal forebrain cholinergic neurons are dependent on nerve growth factor (NGF) for growth and survival and these cells are among the first to degenerate in Alzheimer’s disease (AD). Targeted delivery of NGF has been suggested as a potential therapy for AD. This hypothesis was tested in a clinical trial with encapsulated cell biodelivery of NGF (NGF-ECB) in AD patients. Three of six patients showed improved biomarkers for cognition by the end of the study. Here, we report on the effects of targeted delivery of NGF on human resting EEG. Materials and methods NGF-ECB implants were implanted bilaterally in the basal forebrain of six AD patients for 12 months. EEG recordings and quantitative analysis were performed at baseline, 3 and 12 months of NGF delivery, and analyzed for correlation with changes in Mini-mental state examination (MMSE) and levels of the cholinergic marker choline acetyltransferase (ChAT) in cerebrospinal fluid (CSF). Results We found significant correlations between the topographic variance of EEG spectral power at the three study points (baseline, 3 and 12 months) and changes in MMSE and CSF ChAT. This possible effect of NGF was identified in a narrow window of alpha frequency 10–11.5 Hz, where a stabilization in MMSE score during treatment was related to an increase in EEG alpha power. A similar relation was observed between the alpha power and ChAT. More theta power at 6.5 Hz was on the contrary associated with a decrease in CSF ChAT during the trial period. Conclusion In this exploratory study, there was a positive correlative pattern between physiological high-frequency alpha activity and stabilization in MMSE and increase in CSF ChAT in AD patients receiving targeted delivery of NGF to the cholinergic basal forebrain.
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Affiliation(s)
- Helga Eyjolfsdottir
- Department of Neurobiology, Care Science and Society, Karolinska Institutet, Solna, Sweden
- Theme Inflammation and Aging, Karolinska University, Stockholm, Sweden
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Azadeh Karami
- Department of Neurobiology, Care Science and Society, Karolinska Institutet, Solna, Sweden
| | - Per Almqvist
- Department of Clinical Neuroscience, Stockholm, Sweden
- Department of Neurosurgery, Theme Neuro, Karolinska University, Stockholm, Sweden
| | - Göran Lind
- Department of Clinical Neuroscience, Stockholm, Sweden
- Department of Neurosurgery, Theme Neuro, Karolinska University, Stockholm, Sweden
| | - Bengt Linderoth
- Department of Clinical Neuroscience, Stockholm, Sweden
- Department of Neurosurgery, Theme Neuro, Karolinska University, Stockholm, Sweden
| | | | - Åke Seiger
- Stiftelsen Stockholms Sjukhem, Stockholm, Sweden
| | - Taher Darreh-Shori
- Department of Neurobiology, Care Science and Society, Karolinska Institutet, Solna, Sweden
| | - Maria Eriksdotter
- Department of Neurobiology, Care Science and Society, Karolinska Institutet, Solna, Sweden
- Theme Inflammation and Aging, Karolinska University, Stockholm, Sweden
| | - Vesna Jelic
- Department of Neurobiology, Care Science and Society, Karolinska Institutet, Solna, Sweden
- Theme Inflammation and Aging, Karolinska University, Stockholm, Sweden
- *Correspondence: Vesna Jelic,
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Torabinikjeh M, Asayesh V, Dehghani M, Kouchakzadeh A, Marhamati H, Gharibzadeh S. Correlations of frontal resting-state EEG markers with MMSE scores in patients with Alzheimer’s disease. THE EGYPTIAN JOURNAL OF NEUROLOGY, PSYCHIATRY AND NEUROSURGERY 2022. [DOI: 10.1186/s41983-022-00465-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
A previous study suggests that resting-state EEG biomarkers measured at prefrontal region (Fp1, and Fp2) are moderately correlated with Mini-Mental State Examination (MMSE) scores of elderly people with Alzheimer’s disease. In this study, our objective was to investigate whether resting-state EEG biomarkers recorded from frontal region are correlated with each MMSE sub-scores. 20 elderly patients diagnosed as Alzheimer’s disease entered to the study. After completion of MMSE, subjects underwent EEG for 5 min with closed eyes condition. We measured median frequency, theta/alpha power ratio, and relative powers. To examine the relationship between these features and MMSE sub-scores first, Pearson correlation coefficients were computed for each feature and MMSE sub-scores. Then, p values were computed for each correlation. Finally, a Bonferroni correction was done.
Results
Nine correlations have been found for markers recorded from F3, F7, and Fz. Alpha and beta relative powers were the markers which shows correlations. We found that MMSE overall, attention, and calculation scores are significantly correlated with beta relative powers recorded from F3, and Fz, and alpha relative power from F7. Orientation to time scores were correlated with F3, and Fz beta relative powers. The only correlation found for orientation to place was beta relative power of F3.
Conclusions
Our results indicate that there are correlations between frontal EEG markers and MMSE sub-scores of patients with Alzheimer’s disease. The results show that alpha and beta relative powers are markers correlated with MMSE scores. It seems that if we want to develop predicting models for Alzheimer’s disease, using data recorded from other frontal electrodes, especially what we have introduced should be considered.
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Yao S, Zhu J, Li S, Zhang R, Zhao J, Yang X, Wang Y. Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021. Front Psychiatry 2022; 13:830819. [PMID: 35677873 PMCID: PMC9167960 DOI: 10.3389/fpsyt.2022.830819] [Citation(s) in RCA: 1] [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: 12/07/2021] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis. METHODS QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis. RESULTS A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends. CONCLUSION The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.
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Affiliation(s)
- Shun Yao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jieying Zhu
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Shuiyan Li
- Department of Rehabilitation Medicine, School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Ruibin Zhang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiubo Zhao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xueling Yang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - You Wang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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10
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Electroencephalography for Early Detection of Alzheimer’s Disease in Subjective Cognitive Decline. Dement Neurocogn Disord 2022; 21:126-137. [DOI: 10.12779/dnd.2022.21.4.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 11/09/2022] Open
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11
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Mazrooei Rad E, Azarnoosh M, Ghoshuni M, Khalilzadeh MM. Diagnosis of mild Alzheimer's disease by EEG and ERP signals using linear and nonlinear classifiers. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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12
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Kuhn T, Becerra S, Duncan J, Spivak N, Dang BH, Habelhah B, Mahdavi KD, Mamoun M, Whitney M, Pereles FS, Bystritsky A, Jordan SE. Translating state-of-the-art brain magnetic resonance imaging (MRI) techniques into clinical practice: multimodal MRI differentiates dementia subtypes in a traditional clinical setting. Quant Imaging Med Surg 2021; 11:4056-4073. [PMID: 34476189 PMCID: PMC8339641 DOI: 10.21037/qims-20-1355] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 04/25/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND This study sought to validate the clinical utility of multimodal magnetic resonance imaging (MRI) techniques in the assessment of neurodegenerative disorders. We intended to demonstrate that advanced neuroimaging techniques commonly used in research can effectively be employed in clinical practice to accurately differentiate heathy aging and dementia subtypes. METHODS Twenty patients with dementia of the Alzheimer's type (DAT) and 18 patients with Parkinson's disease dementia (PDD) were identified using gold-standard techniques. Twenty-three healthy, age and sex matched control participants were also recruited. All participants underwent multimodal MRI including T1 structural, diffusion tensor imaging (DTI), arterial spin labeling (ASL), and magnetic resonance spectroscopy (MRS). MRI modalities were evaluated by trained neuroimaging readers and were separately assessed using cross-validated, iterative discriminant function analyses with subsequent feature reduction techniques. In this way, each modality was evaluated for its ability to differentiate patients with dementia from healthy controls as well as to differentiate dementia subtypes. RESULTS Following individual and group feature reduction, each of the multimodal MRI metrics except MRS successfully differentiated healthy aging from dementia and also demonstrated distinct dementia subtypes. Using the following ten metrics, excellent separation (95.5% accuracy, 92.3% sensitivity; 100.0% specificity) was achieved between healthy aging and neurodegenerative conditions: volume of the left frontal pole, left occipital pole, right posterior superior temporal gyrus, left posterior cingulate gyrus, right planum temporale; perfusion of the left hippocampus and left occipital lobe; fractional anisotropy (FA) of the forceps major and bilateral anterior thalamic radiation. Using volume of the left frontal pole, right posterior superior temporal gyrus, left posterior cingulate gyrus, perfusion of the left hippocampus and left occipital lobe; FA of the forceps major and bilateral anterior thalamic radiation, neurodegenerative subtypes were accurately differentiated as well (87.8% accuracy, 95.2% sensitivity; 85.0% specificity). CONCLUSIONS Regional volumetrics, DTI metrics, and ASL successfully differentiated dementia patients from controls with sufficient sensitivity to differentiate dementia subtypes. Similarly, feature reduction results suggest that advanced analyses can meaningfully identify brain regions with the most positive predictive value and discriminant validity. Together, these advanced neuroimaging techniques can contribute significantly to diagnosis and treatment planning for individual patients.
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Affiliation(s)
- Taylor Kuhn
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, USA
| | - Sergio Becerra
- Neurology Management Associates, Los Angeles, California, USA
| | - John Duncan
- Neurology Management Associates, Los Angeles, California, USA
| | - Norman Spivak
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, USA
| | - Bianca Huan Dang
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, USA
| | | | | | | | | | | | - Alexander Bystritsky
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, USA
| | - Sheldon E. Jordan
- Neurology Management Associates, Los Angeles, California, USA
- Department of Neurology, University of California, Los Angeles, Los Angeles, California, USA
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13
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Fröhlich S, Kutz DF, Müller K, Voelcker-Rehage C. Characteristics of Resting State EEG Power in 80+-Year-Olds of Different Cognitive Status. Front Aging Neurosci 2021; 13:675689. [PMID: 34456708 PMCID: PMC8387136 DOI: 10.3389/fnagi.2021.675689] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 07/07/2021] [Indexed: 11/28/2022] Open
Abstract
Compared with healthy older adults, patients with Alzheimer's disease show decreased alpha and beta power as well as increased delta and theta power during resting state electroencephalography (rsEEG). Findings for mild cognitive impairment (MCI), a stage of increased risk of conversion to dementia, are less conclusive. Cognitive status of 213 non-demented high-agers (mean age, 82.5 years) was classified according to a neuropsychological screening and a cognitive test battery. RsEEG was measured with eyes closed and open, and absolute power in delta, theta, alpha, and beta bands were calculated for nine regions. Results indicate no rsEEG power differences between healthy individuals and those with MCI. There were also no differences present between groups in EEG reactivity, the change in power from eyes closed to eyes open, or the topographical pattern of each frequency band. Overall, EEG reactivity was preserved in 80+-year-olds without dementia, and topographical patterns were described for each frequency band. The application of rsEEG power as a marker for the early detection of dementia might be less conclusive for high-agers.
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Affiliation(s)
- Stephanie Fröhlich
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Dieter F Kutz
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Katrin Müller
- Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany.,Department of Social Science of Physical Activity and Health, Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Claudia Voelcker-Rehage
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
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14
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Babiloni C, Arakaki X, Bonanni L, Bujan A, Carrillo MC, Del Percio C, Edelmayer RM, Egan G, Elahh FM, Evans A, Ferri R, Frisoni GB, Güntekin B, Hainsworth A, Hampel H, Jelic V, Jeong J, Kim DK, Kramberger M, Kumar S, Lizio R, Nobili F, Noce G, Puce A, Ritter P, Smit DJA, Soricelli A, Teipel S, Tucci F, Sachdev P, Valdes-Sosa M, Valdes-Sosa P, Vergallo A, Yener G. EEG measures for clinical research in major vascular cognitive impairment: recommendations by an expert panel. Neurobiol Aging 2021; 103:78-97. [PMID: 33845399 DOI: 10.1016/j.neurobiolaging.2021.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 02/17/2021] [Accepted: 03/05/2021] [Indexed: 11/22/2022]
Abstract
Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy; San Raffaele Cassino, Cassino, FR, Italy.
| | | | - 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, Portugal
| | | | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Gary Egan
- Foundation Director of the Monash Biomedical Imaging (MBI) research facilities, Monash University, Clayton, Australia
| | - Fanny M Elahh
- Memory and Aging Center, University of California, San Francisco
| | - Alan Evans
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | | | - Giovanni B Frisoni
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland; Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey; REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab, Istanbul Medipol University, Istanbul, Turkey
| | - Atticus Hainsworth
- University of London St George's Molecular and Clinical Sciences Research Institute, London, UK
| | - Harald Hampel
- Sorbonne University, GRC No. 21, Alzheimer Precision Medicine, Pitié-Salpêtrière Hospital, 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
| | - Doh Kwan Kim
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Milica Kramberger
- Center for cognitive and movement disorders, Department of neurology, University Medical Center Ljubljana, Slovenia
| | - Sanjeev Kumar
- Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | | | - Flavio Nobili
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy; Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI)
| | | | - Aina Puce
- Department of Psychological and Brain Sciences at Indiana University in Bloomington, Indiana, USA
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Dirk J A Smit
- Department of Psychiatry Academisch Medisch Centrum Universiteit van Amsterdam, Amsterdam, the Netherlands
| | - Andrea Soricelli
- IRCCS SDN, Naples, Italy; Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - 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
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales; Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney, Australia
| | | | - Pedro Valdes-Sosa
- Cuban Neuroscience Center, Havana, Cuba; Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Andrea Vergallo
- Sorbonne University, GRC No. 21, Alzheimer Precision Medicine, Pitié-Salpêtrière Hospital, Paris, France
| | - Görsev Yener
- Izmir Biomedicine and Genome Center. Dokuz Eylul University Health Campus, Izmir, Turkey
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15
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Grässler B, Herold F, Dordevic M, Gujar TA, Darius S, Böckelmann I, Müller NG, Hökelmann A. Multimodal measurement approach to identify individuals with mild cognitive impairment: study protocol for a cross-sectional trial. BMJ Open 2021; 11:e046879. [PMID: 34035103 PMCID: PMC8154928 DOI: 10.1136/bmjopen-2020-046879] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 05/11/2021] [Indexed: 12/26/2022] Open
Abstract
INTRODUCTION The diagnosis of mild cognitive impairment (MCI), that is, the transitory phase between normal age-related cognitive decline and dementia, remains a challenging task. It was observed that a multimodal approach (simultaneous analysis of several complementary modalities) can improve the classification accuracy. We will combine three noninvasive measurement modalities: functional near-infrared spectroscopy (fNIRS), electroencephalography and heart rate variability via ECG. Our aim is to explore neurophysiological correlates of cognitive performance and whether our multimodal approach can aid in early identification of individuals with MCI. METHODS AND ANALYSIS This study will be a cross-sectional with patients with MCI and healthy controls (HC). The neurophysiological signals will be measured during rest and while performing cognitive tasks: (1) Stroop, (2) N-back and (3) verbal fluency test (VFT). Main aims of statistical analysis are to (1) determine the differences in neurophysiological responses of HC and MCI, (2) investigate relationships between measures of cognitive performance and neurophysiological responses and (3) investigate whether the classification accuracy can be improved by using our multimodal approach. To meet these targets, statistical analysis will include machine learning approaches.This is, to the best of our knowledge, the first study that applies simultaneously these three modalities in MCI and HC. We hypothesise that the multimodal approach improves the classification accuracy between HC and MCI as compared with a unimodal approach. If our hypothesis is verified, this study paves the way for additional research on multimodal approaches for dementia research and fosters the exploration of new biomarkers for an early detection of nonphysiological age-related cognitive decline. ETHICS AND DISSEMINATION Ethics approval was obtained from the local Ethics Committee (reference: 83/19). Data will be shared with the scientific community no more than 1 year following completion of study and data assembly. TRIAL REGISTRATION NUMBER ClinicalTrials.gov, NCT04427436, registered on 10 June 2020, https://clinicaltrials.gov/ct2/show/study/NCT04427436.
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Affiliation(s)
- Bernhard Grässler
- Institute of Sport Science, Faculty of Humanities, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Fabian Herold
- Department of Neuroprotection, German Centre for Neurodegenerative Diseases Site Magdeburg, Magdeburg, Germany
| | - Milos Dordevic
- Department of Neuroprotection, German Centre for Neurodegenerative Diseases Site Magdeburg, Magdeburg, Germany
| | - Tariq Ali Gujar
- Institute of Sport Science, Faculty of Humanities, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Sabine Darius
- Occupational Medicine, Otto von Guericke University Medical Faculty, Magdeburg, Germany
| | - Irina Böckelmann
- Occupational Medicine, Otto von Guericke University Medical Faculty, Magdeburg, Germany
| | - Notger G Müller
- Department of Neuroprotection, German Centre for Neurodegenerative Diseases Site Magdeburg, Magdeburg, Germany
- Department of Neurology, Otto von Guericke University Medical Faculty, Magdeburg, Germany
| | - Anita Hökelmann
- Institute of Sport Science, Faculty of Humanities, Otto von Guericke University Magdeburg, Magdeburg, Germany
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16
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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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17
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Doan DNT, Ku B, Choi J, Oh M, Kim K, Cha W, Kim JU. Predicting Dementia With Prefrontal Electroencephalography and Event-Related Potential. Front Aging Neurosci 2021; 13:659817. [PMID: 33927610 PMCID: PMC8077968 DOI: 10.3389/fnagi.2021.659817] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 03/19/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia. Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms. Results: Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy. Conclusion: Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores. Significance: Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE.
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Affiliation(s)
- Dieu Ni Thi Doan
- Korea Institute of Oriental Medicine, Daejeon, South Korea.,Korean Convergence Medicine, University of Science and Technology, Daejeon, South Korea
| | - Boncho Ku
- Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Jungmi Choi
- Human Anti-Aging Standards Research Institute, Uiryeong-gun, South Korea
| | - Miae Oh
- Korea Institute for Health and Social Affairs, Sejong, South Korea
| | - Kahye Kim
- Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Wonseok Cha
- Human Anti-Aging Standards Research Institute, Uiryeong-gun, South Korea
| | - Jaeuk U Kim
- Korea Institute of Oriental Medicine, Daejeon, South Korea.,Korean Convergence Medicine, University of Science and Technology, Daejeon, South Korea
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Psycho-Electrophysiological Benefits of Forest Therapies Focused on Qigong and Walking with Elderly Individuals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18063004. [PMID: 33804164 PMCID: PMC7999348 DOI: 10.3390/ijerph18063004] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/28/2021] [Accepted: 03/09/2021] [Indexed: 11/16/2022]
Abstract
We developed two distinct forest therapy programs (FTPs) and compared their effects on dementia prevention and related health problems for older adults. One was focused on Qigong practice in the forest (QP) and the other involved active walking in the forest (WP). Both FTPs consisted of twelve 2-h sessions over six weeks and were conducted in an urban forest. We obtained data from 25, 18, and 26 participants aged 65 years or above for the QP, WP, and control groups, respectively. Neuropsychological scores via cognition (MoCA), geriatric depression (GDS) and quality of life (EQ-5D), and electrophysiological variables (electroencephalography, bioimpedance, and heart rate variability) were measured. We analyzed the intervention effects with a generalized linear model. Compared to the control group, the WP group showed benefits in terms of neurocognition (increases in the MoCA score, and alpha and beta band power values in the electroencephalogram), sympathetic nervous activity, and bioimpedance in the lower body. On the other hand, the QP group showed alleviated depression and an increased bioimpedance phase angle in the upper body. In conclusion, both active walking and Qigong in the forest were shown to have distinctive neuropsychological and electrophysiological benefits, and both had beneficial effects in terms of preventing dementia and relieving related health problems for elderly individuals.
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Bogolepova A, Vasenina E, Gomzyakova N, Gusev E, Dudchenko N, Emelin A, Zalutskaya N, Isaev R, Kotovskaya Y, Levin O, Litvinenko I, Lobzin V, Martynov M, Mkhitaryan E, Nikolay G, Palchikova E, Tkacheva O, Cherdak M, Chimagomedova A, Yakhno N. Clinical Guidelines for Cognitive Disorders in Elderly and Older Patients. Zh Nevrol Psikhiatr Im S S Korsakova 2021. [DOI: 10.17116/jnevro20211211036] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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20
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Koenig T, Smailovic U, Jelic V. Past, present and future EEG in the clinical workup of dementias. Psychiatry Res Neuroimaging 2020; 306:111182. [PMID: 32921526 DOI: 10.1016/j.pscychresns.2020.111182] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/02/2020] [Accepted: 09/03/2020] [Indexed: 01/25/2023]
Abstract
Electroencephalography (EEG), as non-invasive, global measure of neuronal activity, is a prime candidate functional marker of synapse dysfunction and loss in dementias. Nevertheless, EEG currently has no established role in the clinical workup of individual patients. This opinion paper presents our critical view on why EEG has so far failed to keep its promise, and where we believe EEG will be clinically useful for patients threatened with cognitive decline in the future. Individual EEGs are an integral outcome of many causally intermixing upstream factors contributing to dementia. Therefore, EEG cannot become a clinically useful "simple" stand-alone biomarker of some pathognomic accumulations of specific brain proteins, but rather offer unique opportunities for more comprehensive and richly faceted insights into the functional status of brain systems. EEG may thus remain an essential window into the brain when it comes to the at-risk and presymptomatic phases of dementias, where it can be uniquely informative about concepts such as burdens of plasticity and repair, cognitive reserve, and sleep. Jointly with rapid gains in usability, portability, machine learning, closed loop systems, and understanding of the role of EEG-based sleep stages for memory and brain repair, EEG may come to keep its initial promise after all.
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Affiliation(s)
- Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland.
| | - Una Smailovic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden.
| | - Vesna Jelic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden; Karolinska University Hospital-Huddinge, Clinic for Cognitive Disorders, Stockholm, Sweden.
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21
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Smailovic U, Koenig T, Savitcheva I, Chiotis K, Nordberg A, Blennow K, Winblad B, Jelic V. Regional Disconnection in Alzheimer Dementia and Amyloid-Positive Mild Cognitive Impairment: Association Between EEG Functional Connectivity and Brain Glucose Metabolism. Brain Connect 2020; 10:555-565. [PMID: 33073602 PMCID: PMC7757561 DOI: 10.1089/brain.2020.0785] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Introduction: The disconnection hypothesis of Alzheimer's disease (AD) is supported by growing neuroimaging and neurophysiological evidence of altered brain functional connectivity in cognitively impaired individuals. Brain functional modalities such as [18F]fluorodeoxyglucose positron-emission tomography ([18F]FDG-PET) and electroencephalography (EEG) measure different aspects of synaptic functioning, and can contribute to understanding brain connectivity disruptions in AD. Aim: This study investigated the relationship between cortical glucose metabolism and topographical EEG measures of brain functional connectivity in subjects along AD continuum. Methods: Patients diagnosed with mild cognitive impairment (MCI) and AD (n = 67), and stratified into amyloid-positive (n = 32) and negative (n = 10) groups according to cerebrospinal fluid Aβ42/40 ratio, were assessed with [18F]FDG-PET and resting-state EEG recordings. EEG-based neuroimaging analysis involved standardized low-resolution electromagnetic tomography (sLORETA), which estimates functional connectivity from cortical sources of electrical activity in a 3D head model. Results: Glucose hypometabolism in temporoparietal lobes was significantly associated with altered EEG functional connectivity of the same regions of interest in clinically diagnosed MCI and AD patients and in patients with biomarker-verified AD pathology. The correlative pattern of disrupted connectivity in temporoparietal lobes, as detected by EEG sLORETA analysis, included decreased instantaneous linear connectivity in fast frequencies and increased lagged linear connectivity in slow frequencies in relation to the activity of remaining cortex. Conclusions: Topographical EEG measures of functional connectivity detect regional dysfunction of AD-vulnerable brain areas as evidenced by association and spatial overlap with the cortical glucose hypometabolism in MCI and AD patients.
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Affiliation(s)
- Una Smailovic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Konstantinos Chiotis
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Agneta Nordberg
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Clinic for Cognitive Disorders, Karolinska University Hospital-Huddinge, Huddinge, Sweden
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry and Sahlgrenska University Hospital, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Bengt Winblad
- Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Geriatrics, Karolinska University Hospital, Huddinge, Sweden
| | - Vesna Jelic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Clinic for Cognitive Disorders, Karolinska University Hospital-Huddinge, Huddinge, Sweden
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22
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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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23
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Choi J, Lim E, Park MG, Cha W. Assessing the Retest Reliability of Prefrontal EEG Markers of Brain Rhythm Slowing in the Eyes-Closed Resting State. Clin EEG Neurosci 2020; 51:348-356. [PMID: 32253926 DOI: 10.1177/1550059420914832] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Objective. We examined whether prefrontal lobe EEG markers of slower brain rhythms, which are correlated with functional brain aging, can reliably reflect those of other brain lobes, as measured by a multichannel device. Methods. EEG measurements were taken of 112 healthy individuals aged 20 to 69 years in the eyes-closed resting state. A 5-minute measurement was taken at 8 regions (Fp1, Fp2, F3, F4, T3, T4, O1, O2). Indices (median frequency [MDF], peak frequency [PF]) that quantitatively reflect the characteristics of EEG slowing, and traditional commonly used spectral indices (absolute powers as delta, theta, alpha, beta, and relative power as alpha-to-theta ratio [ATR]), were extracted from the EEG signals. For these indices, the differences between the prefrontal lobe and other areas were analyzed and the test-retest reproducibility was investigated. Results. The EEG slowing indicators showed high conformity over all brain lobes and stable reproducibility. On the other hand, the typical EEG spectral indicators delta, theta, alpha, beta, and ATR differed between brain regions. Conclusion. It was found that EEG slowing markers, which were used for assessing the aging or degeneration of brain functions, could be reliably extracted from a prefrontal EEG alone. Significance. These findings suggest that EEG prefrontal markers may reflect markers of other brain regions when a multi-channel device is used. Thus, this method may constitute a low-cost, wearable, wireless, easily accessible, and noninvasive tool for neurological assessment that could be used in the early detection of cognitive decline and in the prevention of dementia.
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Affiliation(s)
- Jungmi Choi
- Human Anti-Aging Standards Research Institute, Gyeongsangnam-do, Uiryeong-gun, Republic of Korea
| | - Eunjo Lim
- Human Anti-Aging Standards Research Institute, Gyeongsangnam-do, Uiryeong-gun, Republic of Korea
| | - Min-Goo Park
- Animal and Plant Quarantine Agency, Kimcheon-si, Gyeongsangbuk-do, Republic of Korea.,Kyeongsang national university, Jinju-si, Gyeongsangnam-do, Republic of Korea
| | - Wonseok Cha
- Human Anti-Aging Standards Research Institute, Gyeongsangnam-do, Uiryeong-gun, Republic of Korea
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24
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Negative effect of methyl bromide fumigation work on the central nervous system. PLoS One 2020; 15:e0236694. [PMID: 32745138 PMCID: PMC7398500 DOI: 10.1371/journal.pone.0236694] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 07/07/2020] [Indexed: 01/05/2023] Open
Abstract
Methyl bromide (MB) is a fumigant that has been widely used for killing pests on plants in trade, soils, and structures worldwide due to its excellent permeability and insecticidal effect; however, MB should be replaced because it is an ozone-depleting substance. It is well-known that MB is highly toxic and hazardous to workers, but the effects of exposure in asymptomatic workers have not been explored. The purpose of this study is to investigate the impact of MB fumigation on the health of fumigators at a sensitive level. The electroencephalogram (EEG) and urinary bromide ion levels of 44 fumigators (the study group) and 20 inspectors (the control) were measured before and after fumigation work from February to August 2019 in Busan, Korea. The mean post-work concentration of bromide ion (18.311 μg/mg CRE) in the fumigators was significantly increased from the pre-work level (7.390 μg/mg CRE) (P<0.001). The fumigator post-work median frequencies (MDF) and alpha-to-theta ratios (ATR) of EEG index were significantly decreased compared to the pre-work values (P<0.05 for all indices). In contrast, there were no significant differences in inspector EEG indices and urinary bromide ion. The urinary bromide ion levels in all the subjects were negatively correlated with MDF (P = 0.032). In conclusion, fumigators’ EEG indices and urinary bromide ion suggested that occupational exposure to MB negatively affected the health of workers, although the workers were asymptomatic.
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25
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Andrade-Talavera Y, Arroyo-García LE, Chen G, Johansson J, Fisahn A. Modulation of Kv3.1/Kv3.2 promotes gamma oscillations by rescuing Aβ-induced desynchronization of fast-spiking interneuron firing in an AD mouse model in vitro. J Physiol 2020; 598:3711-3725. [PMID: 32638407 DOI: 10.1113/jp279718] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 05/26/2020] [Indexed: 11/08/2022] Open
Abstract
KEY POINTS Gamma oscillations (30-80 Hz) are important for cognitive functions and depend on the synchronized activity of fast-spiking interneurons (FSN), which is crucial for network stability. Gamma oscillations are degraded in Alzheimer's disease (AD) patients exhibiting cognitive impairment, with the degree of cognitive decline correlating with the severity of gamma disruption in response to neurotoxic amyloid-beta peptide (Aβ). Small molecule compounds EX15 and RE01 modulate Kv3.1/Kv3.2 potassium channels on FSN, resulting in faster activation kinetics and increased firing frequency, suggesting direct consequences for cognition-relevant gamma oscillations, particularly in a situation where network activity is pathologically compromised in the presence of neurotoxic Aβ. Using electrophysiological techniques in an in vitro AD model, we found a significant effect of EX15 and RE01 with respect to counteracting toxic Aβ effects on neuronal dynamics, advocating for targeting FSN activity to rescue cognitive performance from impairment caused by neurodegenerative triggers. ABSTRACT Rhythmic electrical activity in neuronal networks such as gamma oscillations (30-80 Hz) underlies cognitive functions such as sensory perception, attention and memory. Gamma oscillations are disrupted in Alzheimer's disease (AD) patients and animal AD models, with the severity of cognitive decline correlating with the degree of rhythm disruption. Misfolded amyloid-β peptide (Aβ) is assumed to be a key trigger of AD pathology and has been show to de-synchronize action potential firing in fast-spiking interneurons (FSN), which is crucial for entraining neuronal network activity into the gamma rhythm. The synchronizing activity of FSN therefore has become one of the most suitable targets to counteract disease-driven degradation of gamma oscillations and consequent cognitive decline. EX15 and RE01 are small-molecule compounds that modulate Kv3.1/Kv3.2 potassium channels, resulting in faster activation kinetics and increased FSN firing frequency. In the present study, we investigated the potential pro-cognitive effects of EX15 and RE01 by testing their ability to modulate FSN activity during ongoing gamma oscillations in normal and Aβ-disrupted network states in mouse hippocampus in vitro. In the compromised, but not the uncompromised, network state with gamma oscillations partially disrupted by Aβ, both compounds improve gamma oscillation regularity by promoting re-synchronization of FSN action potential firing. Our data suggest a therapeutic potential for compounds such as EX15 and RE01, which can rescue normal action potential firing parameters in FSN, in the search for disease-modifying drug candidates counteracting the progressive dysfunction of neuronal network dynamics that underlies the cognitive impairment typical of AD and other cognitive brain disorders.
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Affiliation(s)
| | | | - Gefei Chen
- Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Jan Johansson
- Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
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26
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Pedroni A, Bahreini A, Langer N. Automagic: Standardized preprocessing of big EEG data. Neuroimage 2019; 200:460-473. [DOI: 10.1016/j.neuroimage.2019.06.046] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 05/25/2019] [Accepted: 06/19/2019] [Indexed: 01/08/2023] Open
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27
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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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28
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Rupp G, Berka C, Meghdadi AH, Karić MS, Casillas M, Smith S, Rosenthal T, McShea K, Sones E, Marcotte TD. EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers. Front Hum Neurosci 2019; 12:532. [PMID: 30697156 PMCID: PMC6341028 DOI: 10.3389/fnhum.2018.00532] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 12/17/2018] [Indexed: 01/12/2023] Open
Abstract
The number of older drivers is steadily increasing, and advancing age is associated with a high rate of automobile crashes and fatalities. This can be attributed to a combination of factors including decline in sensory, motor, and cognitive functions due to natural aging or neurodegenerative diseases such as HIV-Associated Neurocognitive Disorder (HAND). Current clinical assessment methods only modestly predict impaired driving. Thus, there is a need for inexpensive and scalable tools to predict on-road driving performance. In this study EEG was acquired from 39 HIV+ patients and 63 healthy participants (HP) during: 3-Choice-Vigilance Task (3CVT), a 30-min driving simulator session, and a 12-mile on-road driving evaluation. Based on driving performance, a designation of Good/Poor (simulator) and Safe/Unsafe (on-road drive) was assigned to each participant. Event-related potentials (ERPs) obtained during 3CVT showed increased amplitude of the P200 component was associated with bad driving performance both during the on-road and simulated drive. This P200 effect was consistent across the HP and HIV+ groups, particularly over the left frontal-central region. Decreased amplitude of the late positive potential (LPP) during 3CVT, particularly over the left frontal regions, was associated with bad driving performance in the simulator. These EEG ERP metrics were shown to be associated with driving performance across participants independent of HIV status. During the on-road evaluation, Unsafe drivers exhibited higher EEG alpha power compared to Safe drivers. The results of this study are 2-fold. First, they demonstrate that high-quality EEG can be inexpensively and easily acquired during simulated and on-road driving assessments. Secondly, EEG metrics acquired during a sustained attention task (3CVT) are associated with driving performance, and these metrics could potentially be used to assess whether an individual has the cognitive skills necessary for safe driving.
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Affiliation(s)
- Greg Rupp
- Advanced Brain Monitoring Inc., Carlsbad, CA, United States
| | - Chris Berka
- Advanced Brain Monitoring Inc., Carlsbad, CA, United States
| | | | | | - Marc Casillas
- Advanced Brain Monitoring Inc., Carlsbad, CA, United States
| | | | | | - Kevin McShea
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Emily Sones
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Thomas D. Marcotte
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
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29
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Waser M, Benke T, Dal‐Bianco P, Garn H, Mosbacher JA, Ransmayr G, Schmidt R, Seiler S, Sorensen HBD, Jennum PJ. Neuroimaging markers of global cognition in early Alzheimer's disease: A magnetic resonance imaging-electroencephalography study. Brain Behav 2019; 9:e01197. [PMID: 30592179 PMCID: PMC6346656 DOI: 10.1002/brb3.1197] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 11/26/2018] [Accepted: 12/05/2018] [Indexed: 12/16/2022] Open
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) and electroencephalography (EEG) are a promising means to an objectified assessment of cognitive impairment in Alzheimer's disease (AD). Individually, however, these modalities tend to lack precision in both AD diagnosis and AD staging. A joint MRI-EEG approach that combines structural with functional information has the potential to overcome these limitations. MATERIALS AND METHODS This cross-sectional study systematically investigated the link between MRI and EEG markers and the global cognitive status in early AD. We hypothesized that the joint modalities would identify cognitive deficits with higher accuracy than the individual modalities. In a cohort of 111 AD patients, we combined MRI measures of cortical thickness and regional brain volume with EEG measures of rhythmic activity, information processing and functional coupling in a generalized multiple regression model. Machine learning classification was used to evaluate the markers' utility in accurately separating the subjects according to their cognitive score. RESULTS We found that joint measures of temporal volume, cortical thickness, and EEG slowing were well associated with the cognitive status and explained 38.2% of ifs variation. The inclusion of the covariates age, sex, and education considerably improved the model. The joint markers separated the subjects with an accuracy of 84.7%, which was considerably higher than by using individual modalities. CONCLUSIONS These results suggest that including joint MRI-EEG markers may be beneficial in the diagnostic workup, thus allowing for adequate treatment. Further studies in larger populations, with a longitudinal design and validated against functional-metabolic imaging are warranted to confirm the results.
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Affiliation(s)
- Markus Waser
- Biomedical EngineeringDepartment of Electrical EngineeringTechnical University of DenmarkLyngbyDenmark
- Danish Center for Sleep MedicineDepartment of Clinical NeurophysiologyRigshospitalet GlostrupGlostrupDenmark
- AIT Austrian Institute of Technology GmbHCenter for Digital Safety & SecuritySensing and Vision SolutionsViennaAustria
| | - Thomas Benke
- Department of NeurologyMedical University of InnsbruckInnsbruckAustria
| | - Peter Dal‐Bianco
- Department of NeurologyMedical University of ViennaViennaAustria
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbHCenter for Digital Safety & SecuritySensing and Vision SolutionsViennaAustria
| | | | - Gerhard Ransmayr
- Clinic for Neurology IIKepler University HospitalMed Campus IIILinzAustria
| | | | - Stephan Seiler
- Department of NeurologyMedical University of GrazGrazAustria
| | - Helge B. D. Sorensen
- Biomedical EngineeringDepartment of Electrical EngineeringTechnical University of DenmarkLyngbyDenmark
| | - Poul J. Jennum
- Danish Center for Sleep MedicineDepartment of Clinical NeurophysiologyRigshospitalet GlostrupGlostrupDenmark
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Schjønning Nielsen M, Simonsen AH, Siersma V, Engedal K, Jelic V, Andersen BB, Naik M, Hasselbalch SG, Høgh P. Quantitative Electroencephalography Analyzed by Statistical Pattern Recognition as a Diagnostic and Prognostic Tool in Mild Cognitive Impairment: Results from a Nordic Multicenter Cohort Study. Dement Geriatr Cogn Dis Extra 2018; 8:426-438. [PMID: 30631335 PMCID: PMC6323395 DOI: 10.1159/000490788] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 06/09/2018] [Indexed: 11/26/2022] Open
Abstract
Aim To examine diagnostic and prognostic potential of quantitative electroencephalography (qEEG) analyzed by the statistical pattern recognition (SPR) method in patients with cognitive impairment. We compared the differential diagnostic ability of SPR to visual EEG analysis. Correlation between SPR findings and cerebrospinal fluid (CSF) Alzheimer disease (AD) biomarkers were evaluated. Methods It is a multicenter cohort study involving 129 patients, (mild cognitive impairment [MCI], AD, and healthy controls). Standardized EEG was performed at baseline. Patients were continuously clinically evaluated. Results Receiver Operating Characteristic curves showed a low discriminative ability of SPR and no ability to predict clinical progression in patients with MCI. Moderate correlation between SPR analysis and CSF AD biomarkers was found. Conclusion The diagnostic and prognostic abilities of qEEG were low. The SPR method was superior to the visual EEG analysis. The qEEG method correlates well to CSF AD biomarkers, suggesting association with pathology in AD.
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Affiliation(s)
- Malene Schjønning Nielsen
- Regional Dementia Research Centre, Department of Neurology, Zealand University Hospital, Roskilde, Denmark
| | - Anja Hviid Simonsen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Volkert Siersma
- The Research Unit for General Practice and Section of General Practice, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Knut Engedal
- Norwegian Advisory Unit for Ageing and Health, Vestfold Health Trust, Toensberg, Norway.,Department of Geriatric Medicine, Deaconess Hospital, Bergen, Norway
| | - Vesna Jelic
- Section for Clinical Geriatrics, Department of NVS, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Birgitte Bo Andersen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Mala Naik
- Department of Geriatric Medicine, Deaconess Hospital, Bergen, Norway
| | - Steen Gregers Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Peter Høgh
- Regional Dementia Research Centre, Department of Neurology, Zealand University Hospital, Roskilde, Denmark
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Hübner L, Godde B, Voelcker-Rehage C. Older adults reveal enhanced task-related beta power decreases during a force modulation task. Behav Brain Res 2018; 345:104-113. [DOI: 10.1016/j.bbr.2018.02.028] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 02/16/2018] [Accepted: 02/20/2018] [Indexed: 10/18/2022]
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Koenig AM, Nobuhara CK, Williams VJ, Arnold SE. Biomarkers in Alzheimer's, Frontotemporal, Lewy Body, and Vascular Dementias. FOCUS: JOURNAL OF LIFE LONG LEARNING IN PSYCHIATRY 2018; 16:164-172. [PMID: 31975911 DOI: 10.1176/appi.focus.20170048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
This article reviews the current evidence base for biomarkers of the most common causes of dementia in later life: Alzheimer's disease (AD), frontotemporal lobar degenerations, Lewy body dementias, and vascular cognitive impairment and dementia. Biomarkers are objectively measurable indicators of normal physiology, pathological processes, or response to an intervention. Ideally, they are sensitive, specific, easy to obtain, and closely reflect the underlying biological processes of interest. While such markers are well established and in broad clinical use for common disorders in general medicine (e.g., thallium stress tests for coronary artery disease or serum blood urea nitrogen and creatinine for renal failure), analogous, validated markers for AD or other common dementias are limited, although biomarkers in research settings and specialty dementia clinics are progressing toward clinical use. By way of introducing current and future biomarkers for dementias of later life, this article will benefit the practicing clinician by increasing awareness of the availability and utility of current and emerging biomarkers in dementia diagnosis and prognosis and for monitoring new disease-modifying therapeutics that arrive in the clinic over the coming decade.
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Affiliation(s)
- Aaron M Koenig
- All authors are with the MassGeneral Institute for Neurodegenerative Disease (MIND), Department of Neurology, Massachusetts General Hospital, Boston
| | - Chloe K Nobuhara
- All authors are with the MassGeneral Institute for Neurodegenerative Disease (MIND), Department of Neurology, Massachusetts General Hospital, Boston
| | - Victoria J Williams
- All authors are with the MassGeneral Institute for Neurodegenerative Disease (MIND), Department of Neurology, Massachusetts General Hospital, Boston
| | - Steven E Arnold
- All authors are with the MassGeneral Institute for Neurodegenerative Disease (MIND), Department of Neurology, Massachusetts General Hospital, Boston
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33
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Quantitative EEG power and synchronization correlate with Alzheimer's disease CSF biomarkers. Neurobiol Aging 2018; 63:88-95. [DOI: 10.1016/j.neurobiolaging.2017.11.005] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 11/08/2017] [Accepted: 11/09/2017] [Indexed: 01/25/2023]
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Ferreira D, Jelic V, Cavallin L, Oeksengaard AR, Snaedal J, Høgh P, Andersen BB, Naik M, Engedal K, Westman E, Wahlund LO. Electroencephalography Is a Good Complement to Currently Established Dementia Biomarkers. Dement Geriatr Cogn Disord 2018; 42:80-92. [PMID: 27595479 DOI: 10.1159/000448394] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/13/2016] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND/AIMS Dementia biomarkers that are accessible and easily applicable in nonspecialized clinical settings are urgently needed. Quantitative electroencephalography (qEEG) is a good candidate, and the statistical pattern recognition (SPR) method has recently provided promising results. We tested the diagnostic value of qEEG-SPR in comparison to cognition, structural imaging, and cerebrospinal fluid (CSF) biomarkers. METHODS A total of 511 individuals were recruited from the multicenter NORD EEG study [141 healthy controls, 64 subjective cognitive decline, 124 mild cognitive impairment, 135 Alzheimer's disease (AD), 15 dementia with Lewy bodies/Parkinson's disease with dementia (DLB/PDD), 32 other dementias]. The EEG data were recorded in a standardized way. Structural imaging data were visually rated using scales of atrophy in the medial temporal, frontal, and posterior cortex. RESULTS qEEG-SPR outperformed structural imaging, cognition, and CSF biomarkers in DLB/PDD diagnosis, outperformed structural imaging in AD diagnosis, and improved the differential diagnosis of AD. In addition, qEEG-SPR allowed differentiation of two clinically different AD subtypes. CONCLUSION Adding qEEG to the diagnostic workup substantially increases the detection of AD pathology even in pre-dementia stages and improves differential diagnosis. EEG could serve as a good complement to currently established dementia biomarkers since it is cheap, noninvasive, and extensively applied outside academic centers.
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Affiliation(s)
- Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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Badrakalimuthu VR, Swamiraju R, de Waal H. EEG in psychiatric practice: to do or not to do? ACTA ACUST UNITED AC 2018. [DOI: 10.1192/apt.bp.109.006916] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
SummaryElectroencephalography (EEG) is a non-invasive investigation that can aid the diagnosis of psychiatric and neuropsychiatric disorders. A good predictor of an abnormal EEG recording is the presence of an organic factor identified during the clinical assessment. The non-invasiveness and low cost of the procedure and its ability to measure spontaneous brain activity appear to attract clinicians to utilise this investigative tool. However, studies have reported that EEGs arising from psychiatric referrals have the lowest abnormality detection rate. The focus of this article is to improve this by highlighting the current pitfalls and providing recommendations for appropriate utilisation of EEG. We describe specific EEG changes associated with major psychiatric disorders. We conclude by offering pragmatic considerations when referring a patient for EEG, emphasising the fact that the information provided to the neurophysiologist plays a crucial role in interpreting the EEG recording in a diagnostically meaningful way.
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36
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Subclinical white matter lesions and medial temporal lobe atrophy are associated with EEG slowing in a memory clinic cohort. Clin Neurophysiol 2017; 128:1575-1582. [PMID: 28709123 DOI: 10.1016/j.clinph.2017.05.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 04/30/2017] [Accepted: 05/29/2017] [Indexed: 01/16/2023]
Abstract
OBJECTIVE The aim of the study was to describe the relationship between electroencephalographic (EEG) findings obtained by standardized visual analysis, subclinical white matter lesions (WML) and brain atrophy in a large memory clinic population. METHODS Patients with Alzheimer's disease (AD, n=58), mild cognitive impairment (MCI, n=141), subjective cognitive impairment (SCI, n=194) had clinical, MRI based WML severity and regional atrophy assessments, and routine resting EEG recording. Background activity (BA) and episodic and continuous abnormalities were assessed visually in EEG. RESULTS WML (p=0.006) and atrophy in medial temporal regions (MTA) (p=<0.001) were associated with slower BA in all diagnoses. WML were associated in SCI with total episodic EEG abnormalities (p=0.03). CONCLUSIONS EEG is associated with subclinical WML burden and cortical brain atrophy in a memory clinic population. SIGNIFICANCE Even the standard visually assessed EEG can complement a memory clinic diagnostic workup.
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Castro P, Zaman S, Holland A. Alzheimer's disease in people with Down's syndrome: the prospects for and the challenges of developing preventative treatments. J Neurol 2017; 264:804-813. [PMID: 27778163 PMCID: PMC5374178 DOI: 10.1007/s00415-016-8308-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 10/06/2016] [Accepted: 10/07/2016] [Indexed: 12/31/2022]
Abstract
People with Down's syndrome (DS) are at high risk for developing Alzheimer's disease (AD) at a relatively young age. This increased risk is not observed in people with intellectual disabilities for reasons other than DS and for this reason it is unlikely to be due to non-specific effects of having a neurodevelopmental disorder but, instead, a direct consequence of the genetics of DS (trisomy 21). Given the location of the amyloid precursor protein (APP) gene on chromosome 21, the amyloid cascade hypothesis is the dominant theory accounting for this risk, with other genetic and environmental factors modifying the age of onset and the course of the disease. Several potential therapies targeting the amyloid pathway and aiming to modify the course of AD are currently being investigated, which may also be useful for treating AD in DS. However, given that the neuropathology associated with AD starts many years before dementia manifests, any preventative treatment must start well before the onset of symptoms. To enable trials of such interventions, plasma, CSF, brain, and retinal biomarkers are being studied as proxy early diagnostic and outcome measures for AD. In this systematic review, we consider the prospects for the development of potential preventative treatments of AD in the DS population and their evaluation.
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Affiliation(s)
- Paula Castro
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, 18b Trumpington Road, Cambridge, CB2 8AH, UK
| | - Shahid Zaman
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, 18b Trumpington Road, Cambridge, CB2 8AH, UK
| | - Anthony Holland
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, 18b Trumpington Road, Cambridge, CB2 8AH, UK.
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Horváth A, Szűcs A, Barcs G, Kamondi A. Sleep EEG Detects Epileptiform Activity in Alzheimer’s Disease with High Sensitivity. J Alzheimers Dis 2017; 56:1175-1183. [DOI: 10.3233/jad-160994] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Affiliation(s)
- András Horváth
- National Institute of Clinical Neurosciences, Budapest, Hungary
- Semmelweis University School of PhD Studies, János Szentágothai Doctoral School of Neurosciences, Budapest, Hungary
| | - Anna Szűcs
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Gábor Barcs
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Anita Kamondi
- National Institute of Clinical Neurosciences, Budapest, Hungary
- Department of Neurology, Semmelweis University, Budapest, Hungary
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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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Engedal K, Snaedal J, Hoegh P, Jelic V, Bo Andersen B, Naik M, Wahlund LO, Oeksengaard AR. Quantitative EEG Applying the Statistical Recognition Pattern Method: A Useful Tool in Dementia Diagnostic Workup. Dement Geriatr Cogn Disord 2016; 40:1-12. [PMID: 25895831 DOI: 10.1159/000381016] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/16/2015] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND/AIM The aim of this study was to examine the discriminatory power of quantitative EEG (qEEG) applying the statistical pattern recognition (SPR) method to separate Alzheimer's disease (AD) patients from elderly individuals without dementia and from other dementia patients. METHODS The participants were recruited from 6 Nordic memory clinics: 372 unselected patients [mean age 71.7 years (SD 8.6), 54% women] and 146 healthy elderly individuals [mean age 66.5 years (SD 7.7), 60% women]. After a standardized and comprehensive assessment, clinical diagnoses were made according to internationally accepted criteria by at least 2 clinicians. EEGs were recorded in a standardized way and analyzed independently of the clinical diagnoses, using the SPR method. RESULTS In receiver operating characteristic curve analyses, the qEEGs separated AD patients from healthy elderly individuals with an area under the curve (AUC) of 0.90, representing a sensitivity of 84% and a specificity of 81%. The qEEGs further separated patients with Lewy body dementia or Parkinson's disease dementia from AD patients with an AUC of 0.9, a sensitivity of 85% and a specificity of 87%. CONCLUSION qEEG using the SPR method could be a useful tool in dementia diagnostic workup.
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Affiliation(s)
- Knut Engedal
- Norwegian Advisory Unit for Ageing and Health, Vestfold Health Trust, Toensberg, Norway
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41
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Holston EC. The Electrophysiological Phenomenon of Alzheimer's Disease: A Psychopathology Theory. Issues Ment Health Nurs 2015; 36:603-13. [PMID: 26379134 DOI: 10.3109/01612840.2015.1015696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The current understanding of Alzheimer's disease (AD) is based on the Aβ and tau pathology and the resulting neuropathological changes, which are associated with manifested clinical symptoms. However, electrophysiological brain changes may provide a more expansive understanding of AD. Hence, the objective of this systematic review is to propose a theory about the electrophysiological phenomenon of Alzheimer's disease (EPAD). The review of literature resulted from an extensive search of PubMed and MEDLINE databases. One-hundred articles were purposively selected. They provided an understanding of the concepts establishing the theory of EPAD (neuropathological changes, neurochemical changes, metabolic changes, and electrophysiological brain changes). Changes in the electrophysiology of the brain are foundational to the association or interaction of the concepts. Building on Berger's Psychophysical Model, it is evident that electrophysiological brain changes occur and affect cortical areas to generate or manifest symptoms from onset and across the stages of AD, which may be prior to pathological changes. Therefore, the interaction of the concepts demonstrates how the psychopathology results from affected electrophysiology of the brain. The theory of the EPAD provides a theoretical foundation for appropriate measurements of AD without dependence on neuropathological changes. Future research is warranted to further test this theory. Ultimately, this theory contributes to existing knowledge because it shows how electrophysiological changes are useful in understanding the risk and progression of AD across the stages.
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Affiliation(s)
- Ezra C Holston
- a University of Tennessee-Knoxville , College of Nursing , Knoxville , Tennessee , USA
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McBride J, Zhao X, Munro N, Jicha G, Smith C, Jiang Y. Discrimination of mild cognitive impairment and Alzheimer's disease using transfer entropy measures of scalp EEG. JOURNAL OF HEALTHCARE ENGINEERING 2015; 6:55-70. [PMID: 25708377 DOI: 10.1260/2040-2295.6.1.55] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer's disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.
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Affiliation(s)
- Joseph McBride
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA
| | - Nancy Munro
- Oak Ridge Nation Laboratory, Oak Ridge, TN, USA
| | - Gregory Jicha
- Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA Department of Neurology, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Charles Smith
- Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA Department of Neurology, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Yang Jiang
- Sanders-Brown Center on Aging, University of Kentucky College of Medicine, Lexington, KY, USA Department of Behavioral Science, University of Kentucky College of Medicine, Lexington, KY, USA
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Rosenberg S, Périn B, Michel V, Debs R, Navarro V, Convers P. EEG in adults in the laboratory or at the patient's bedside. Neurophysiol Clin 2015; 45:19-37. [DOI: 10.1016/j.neucli.2014.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2014] [Accepted: 11/03/2014] [Indexed: 12/25/2022] Open
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Amyloid-β-induced action potential desynchronization and degradation of hippocampal gamma oscillations is prevented by interference with peptide conformation change and aggregation. J Neurosci 2014; 34:11416-25. [PMID: 25143621 DOI: 10.1523/jneurosci.1195-14.2014] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The amyloid-β hypothesis of Alzheimer's Disease (AD) focuses on accumulation of amyloid-β peptide (Aβ) as the main culprit for the myriad physiological changes seen during development and progression of AD including desynchronization of neuronal action potentials, consequent development of aberrant brain rhythms relevant for cognition, and final emergence of cognitive deficits. The aim of this study was to elucidate the cellular and synaptic mechanisms underlying the Aβ-induced degradation of gamma oscillations in AD, to identify aggregation state(s) of Aβ that mediate the peptides neurotoxicity, and to test ways to prevent the neurotoxic Aβ effect. We show that Aβ(1-42) in physiological concentrations acutely degrades mouse hippocampal gamma oscillations in a concentration- and time-dependent manner. The underlying cause is an Aβ-induced desynchronization of action potential generation in pyramidal cells and a shift of the excitatory/inhibitory equilibrium in the hippocampal network. Using purified preparations containing different aggregation states of Aβ, as well as a designed ligand and a BRICHOS chaperone domain, we provide evidence that the severity of Aβ neurotoxicity increases with increasing concentration of fibrillar over monomeric Aβ forms, and that Aβ-induced degradation of gamma oscillations and excitatory/inhibitory equilibrium is prevented by compounds that interfere with Aβ aggregation. Our study provides correlative evidence for a link between Aβ-induced effects on synaptic currents and AD-relevant neuronal network oscillations, identifies the responsible aggregation state of Aβ and proofs that strategies preventing peptide aggregation are able to prevent the deleterious action of Aβ on the excitatory/inhibitory equilibrium and on the gamma rhythm.
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45
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[French guidelines on electroencephalogram]. Neurophysiol Clin 2014; 44:515-612. [PMID: 25435392 DOI: 10.1016/j.neucli.2014.10.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2014] [Accepted: 10/07/2014] [Indexed: 12/11/2022] Open
Abstract
Electroencephalography allows the functional analysis of electrical brain cortical activity and is the gold standard for analyzing electrophysiological processes involved in epilepsy but also in several other dysfunctions of the central nervous system. Morphological imaging yields complementary data, yet it cannot replace the essential functional analysis tool that is EEG. Furthermore, EEG has the great advantage of being non-invasive, easy to perform and allows control tests when follow-up is necessary, even at the patient's bedside. Faced with the advances in knowledge, techniques and indications, the Société de Neurophysiologie Clinique de Langue Française (SNCLF) and the Ligue Française Contre l'Épilepsie (LFCE) found it necessary to provide an update on EEG recommendations. This article will review the methodology applied to this work, refine the various topics detailed in the following chapters. It will go over the summary of recommendations for each of these chapters and underline proposals for writing an EEG report. Some questions could not be answered by the review of the literature; in those cases, an expert advice was given by the working and reading groups in addition to the guidelines.
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46
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Kramberger MG, Kåreholt I, Andersson T, Winblad B, Eriksdotter M, Jelic V. Association between EEG abnormalities and CSF biomarkers in a memory clinic cohort. Dement Geriatr Cogn Disord 2014; 36:319-28. [PMID: 24022277 DOI: 10.1159/000351677] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/23/2013] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The aim of the study was to describe distinct electroencephalogram (EEG) phenotypes defined after routine visual EEG analysis in a large memory clinic cohort and to investigate their relationship to cerebrospinal fluid (CSF) biomarkers. METHODS Patients with Alzheimer's disease (n = 131), mild cognitive impairment (n = 285), subjective cognitive impairment (n = 310), and mixed dementia (n = 29) were assessed clinically with neuroimaging, EEG and CSF investigations. EEG phenotypes were based on frequency of background activity (BA) and presence and degree of episodic abnormalities (EA). RESULTS BA and EA differed significantly (p < 0.001) between diagnostic groups. A lower CSF amyloid β42/phospho-tau ratio and higher total tau were associated with slower BA (p < 0.01) and a higher degree of EA (p < 0.04). CONCLUSIONS Slowing of BA in combination with EA seems to be related to biological markers of neurodegeneration.
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Affiliation(s)
- Milica Gregoric Kramberger
- Department of Neurobiology, Care Sciences and Society, University Medical Centre Ljubljana, Ljubljana, Slovenia
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48
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McBride JC, Zhao X, Munro NB, Smith CD, Jicha GA, Hively L, Broster LS, Schmitt FA, Kryscio RJ, Jiang Y. Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 114:153-63. [PMID: 24598317 PMCID: PMC4021716 DOI: 10.1016/j.cmpb.2014.01.019] [Citation(s) in RCA: 92] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2013] [Revised: 01/17/2014] [Accepted: 01/28/2014] [Indexed: 06/03/2023]
Abstract
Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.
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Affiliation(s)
- Joseph C McBride
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN 37996, United States
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN 37996, United States; National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Knoxville, TN 37996, United States.
| | - Nancy B Munro
- Oak Ridge National Laboratory, Oak Ridge, TN 37831-6418, United States
| | - Charles D Smith
- Department of Neurology, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States
| | - Gregory A Jicha
- Department of Neurology, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States
| | - Lee Hively
- Oak Ridge National Laboratory, Oak Ridge, TN 37831-6418, United States
| | - Lucas S Broster
- Department of Behavioral Science, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States
| | - Frederick A Schmitt
- Department of Neurology, University of Kentucky, Lexington, KY 40356, United States; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States
| | - Richard J Kryscio
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States; Department of Statistics, University of Kentucky, Lexington, KY 40356, United States
| | - Yang Jiang
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40356, United States; Department of Behavioral Science, University of Kentucky, Lexington, KY 40356, United States
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49
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Sorbi S, Hort J, Erkinjuntti T, Fladby T, Gainotti G, Gurvit H, Nacmias B, Pasquier F, Popescu BO, Rektorova I, Religa D, Rusina R, Rossor M, Schmidt R, Stefanova E, Warren JD, Scheltens P. EFNS-ENS Guidelines on the diagnosis and management of disorders associated with dementia. Eur J Neurol 2013; 19:1159-79. [PMID: 22891773 DOI: 10.1111/j.1468-1331.2012.03784.x] [Citation(s) in RCA: 191] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
BACKGROUND AND OBJECTIVES The last version of the EFNS dementia guidelines is from 2007. In 2010, the revised guidelines for Alzheimer's disease (AD) were published. The current guidelines involve the revision of the dementia syndromes outside of AD, notably vascular cognitive impairment, frontotemporal lobar degeneration, dementia with Lewy bodies, corticobasal syndrome, progressive supranuclear palsy, Parkinson's disease dementia, Huntington's disease, prion diseases, normal-pressure hydrocephalus, limbic encephalitis and other toxic and metabolic disorders. The aim is to present a peer-reviewed evidence-based statement for the guidance of practice for clinical neurologists, geriatricians, psychiatrists and other specialist physicians responsible for the care of patients with dementing disorders. It represents a statement of minimum desirable standards for practice guidance. METHODS The task force working group reviewed evidence from original research articles, meta-analyses and systematic reviews, published by June 2011. The evidence was classified (I, II, III, IV) and consensus recommendations graded (A, B, or C) according to the EFNS guidance. Where there was a lack of evidence, but clear consensus, good practice points were provided. RESULTS AND CONCLUSIONS New recommendations and good practice points are made for clinical diagnosis, blood tests, neuropsychology, neuroimaging, electroencephalography, cerebrospinal fluid (CSF) analysis, genetic testing, disclosure of diagnosis, treatment of behavioural and psychological symptoms in dementia, legal issues, counselling and support for caregivers. All recommendations were revised as compared with the previous EFNS guidelines. The specialist neurologist together with primary care physicians play an important role in the assessment, interpretation and treatment of symptoms, disability and needs of dementia patients.
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Affiliation(s)
- S Sorbi
- Department of Neurological and Psychiatric Sciences, University of Florence, Florence, Italy.
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Fonseca LC, Tedrus GMAS, Carvas PN, Machado ECFA. Comparison of quantitative EEG between patients with Alzheimer's disease and those with Parkinson's disease dementia. Clin Neurophysiol 2013; 124:1970-4. [PMID: 23746496 DOI: 10.1016/j.clinph.2013.05.001] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2013] [Revised: 05/01/2013] [Accepted: 05/03/2013] [Indexed: 10/26/2022]
Abstract
OBJECTIVE Dementia frequently occurs in Parkinson's disease (PD) but its pathophysiological basis is little known. Comparative EEG studies of Alzheimer's disease (AD) and Parkinson's disease dementia (PDD) are still rare, but could provide knowledge on the different pathophysiological mechanisms involved. The objective of the present study was to comparatively evaluate the absolute power and coherence on the EEG for patients with AD and PDD. METHODS This study assessed 38 adults with AD, 12 with PDD, 31 with Parkinson's disease without dementia, and 37 controls (CG) by a neurological evaluation, CERAD neuropsychological battery, executive functions tests and qEEG, calculating global absolute powers for the delta, theta, alpha and beta bands and inter- and intra-hemispheric coherences. RESULTS The delta and theta powers were highest in PDD and lowest in CG (p<0.05). The beta frontal-occipital inter-hemispheric coherence was highest in PDD (p<0.05). Whereas, alpha and beta frontal inter-hemispheric coherence was highest in PDD and lowest in AD (p<0.05). CONCLUSION These results suggest that qEEG power and coherence measures are different in AD and PDD. SIGNIFICANCE These qEEG differences must be related to the distinct mechanisms of cortical neural connections in AD and PDD.
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Affiliation(s)
- Lineu C Fonseca
- Department of Neurology, Faculty of Medicine, Pontifícia Universidade Católica de Campinas (PUC-Campinas), Campinas, SP, Brazil.
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