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Zawiślak-Fornagiel K, Ledwoń D, Bugdol M, Grażyńska A, Ślot M, Tabaka-Pradela J, Bieniek I, Siuda J. Quantitative EEG Spectral and Connectivity Analysis for Cognitive Decline in Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2024; 97:1235-1247. [PMID: 38217593 DOI: 10.3233/jad-230485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is considered to be the borderline of cognitive changes associated with aging and very early dementia. Cognitive functions in MCI can improve, remain stable or progress to clinically probable AD. Quantitative electroencephalography (qEEG) can become a useful tool for using the analytical techniques to quantify EEG patterns indicating cognitive impairment. OBJECTIVE The aim of our study was to assess spectral and connectivity analysis of the EEG resting state activity in amnestic MCI (aMCI) patients in comparison with healthy control group (CogN). METHODS 30 aMCI patients and 23 CogN group, matched by age and education, underwent equal neuropsychological assessment and EEG recording, according to the same protocol. RESULTS qEEG spectral analysis revealed decrease of global relative beta band power and increase of global relative theta and delta power in aMCI patients. Whereas, decreased coherence in centroparietal right area considered to be an early qEEG biomarker of functional disconnection of the brain network in aMCI patients. In conclusion, the demonstrated changes in qEEG, especially, the coherence patterns are specific biomarkers of cognitive impairment in aMCI. CONCLUSIONS Therefore, qEEG measurements appears to be a useful tool that complements neuropsychological diagnostics, assessing the risk of progression and provides a basis for possible interventions designed to improve cognitive functions or even inhibit the progression of the disease.
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Affiliation(s)
- Katarzyna Zawiślak-Fornagiel
- Department of Neurology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
| | - Daniel Ledwoń
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
| | - Monika Bugdol
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
| | - Anna Grażyńska
- Department of Imaging Diagnostics and Interventional Radiology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
| | - Maciej Ślot
- Department of Solid State Physics, Faculty of Physics and Applied Computer Science, University of Łódź, Łódź, Poland
| | - Justyna Tabaka-Pradela
- Department of Neurology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
| | - Izabela Bieniek
- Department of Neurology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
| | - Joanna Siuda
- Department of Neurology, Prof. Kornel Gibiński University Clinical Center, Medical University of Silesia, Katowice, Poland
- Department of Neurology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, Katowice, Poland
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Balathay D, Narasimhan U, Belo D, Anandan K. Quantitative assessment of cognitive profile and brain asymmetry in the characterization of autism spectrum in children: A task-based EEG study. Proc Inst Mech Eng H 2023:9544119231170683. [PMID: 37096354 DOI: 10.1177/09544119231170683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children. Assessment of EEG signals acquired during specific cognitive tasks is more appropriate in identifying brain functional and cognitive load variations. The spectral power of EEG sub-band frequency and parameters related to brain asymmetry has the potential to be employed as indices to characterize brain functioning. Thus, the objective of this work is to analyze the cognitive task-based electrophysiological variations in autistic and control groups, using EEG acquired during two well-defined protocols. Theta to Alpha ratio (TAR) and Theta to Beta ratio (TBR) of absolute powers of the respective sub-band frequencies have been estimated to quantify the cognitive load. The variations in interhemispheric cortical power measured by EEG were studied using the brain asymmetry index. For the arithmetic task, the TBR of the LF group was found to be considerably higher than the HF group. The findings reveal that the spectral powers of EEG sub-bands can be a key indicator in the assessment of high and low-functioning ASD to facilitate appropriate training strategies. Instead of depending solely on behavioral tests to diagnose autism, it could be a beneficial approach to use task-based EEG characteristics to differentiate between the LF and HF groups.
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Affiliation(s)
- Divya Balathay
- Centre for Healthcare Technologies, Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India
| | - Udayakumar Narasimhan
- Department of Pediatrics, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu, India
| | - David Belo
- Machine Learning for Time Series at Fraunhofer Portugal AICOS, Seixal, Setubal, Portugal
| | - Kavitha Anandan
- Centre for Healthcare Technologies, Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India
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3
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Fischer MHF, Zibrandtsen IC, Høgh P, Musaeus CS. Systematic Review of EEG Coherence in Alzheimer's Disease. J Alzheimers Dis 2023; 91:1261-1272. [PMID: 36641665 DOI: 10.3233/jad-220508] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Magnitude-squared coherence (MSCOH) is an electroencephalography (EEG) measure of functional connectivity. MSCOH has been widely applied to investigate pathological changes in patients with Alzheimer's disease (AD). However, significant heterogeneity exists between the studies using MSOCH. OBJECTIVE We systematically reviewed the literature on MSCOH changes in AD as compared to healthy controls to investigate the clinical utility of MSCOH as a marker of AD. METHODS We searched PubMed, Embase, and Scopus to identify studies reporting EEG MSCOH used in patients with AD. The identified studies were independently screened by two researchers and the data was extracted, which included cognitive scores, preprocessing steps, and changes in MSCOH across frequency bands. RESULTS A total of 35 studies investigating changes in MSCOH in patients with AD were included in the review. Alpha coherence was significantly decreased in patients with AD in 24 out of 34 studies. Differences in other frequency bands were less consistent. Some studies showed that MSCOH may serve as a diagnostic marker of AD. CONCLUSION Reduced alpha MSCOH is present in patients with AD and MSCOH may serve as a diagnostic marker. However, studies validating MSCOH as a diagnostic marker are needed.
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Affiliation(s)
| | | | - Peter Høgh
- Department of Neurology, University Hospital of Zealand, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Sandøe Musaeus
- Department of Neurology, Danish Dementia Research Centre (DDRC), Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
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4
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Kuang Y, Wu Z, Xia R, Li X, Liu J, Dai Y, Wang D, Chen S. Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes. Brain Sci 2022; 12:brainsci12101399. [PMID: 36291332 PMCID: PMC9599801 DOI: 10.3390/brainsci12101399] [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: 09/10/2022] [Revised: 10/02/2022] [Accepted: 10/07/2022] [Indexed: 11/30/2022] Open
Abstract
Mild cognitive impairment (MCI) is one of the important comorbidities of type 2 diabetes mellitus (T2DM). It is critical to find appropriate methods for early diagnosis and objective assessment of mild cognitive impairment patients with type 2 diabetes (T2DM-MCI). Our study aimed to investigate potential early alterations in phase lag index (PLI) and determine whether it can distinguish between T2DM-MCI and normal controls with T2DM (T2DM-NC). EEG was recorded in 30 T2DM-MCI patients and 30 T2DM-NC patients. The phase lag index was computed and used in a logistic regression model to discriminate between groups. The correlation between the phase lag index and Montreal Cognitive Assessment (MoCA) score was assessed. The α-band phase lag index was significantly decreased in the T2DM-MCI group compared with the T2DM-NC group and showed a moderate degree of classification accuracy. The MoCA score was positively correlated with the α-band phase lag index (r = 0.4812, moderate association, p = 0.015). This work shows that the functional connectivity analysis of EEG may offer an effective way to track the cortical dysfunction linked to the cognitive deterioration of T2DM patients, and the α-band phase lag index may have a role in guiding the diagnosis of T2DM-MCI.
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Affiliation(s)
- Yuxing Kuang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Ziyi Wu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Rui Xia
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Xingjie Li
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Jun Liu
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Yalan Dai
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Dan Wang
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
| | - Shangjie Chen
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
- Department of Rehabilitation, Affiliated Baoan Hospital of Shenzhen, Southern Medical University (The People’s Hospital of Baoan Shenzhen), Shenzhen 518101, China
- Correspondence: ; Tel.: +86-0755-27788311
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Adaikkan C, Wang J, Abdelaal K, Middleton SJ, Bozzelli PL, Wickersham IR, McHugh TJ, Tsai LH. Alterations in a cross-hemispheric circuit associates with novelty discrimination deficits in mouse models of neurodegeneration. Neuron 2022; 110:3091-3105.e9. [PMID: 35987206 PMCID: PMC9547933 DOI: 10.1016/j.neuron.2022.07.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 02/23/2022] [Accepted: 07/22/2022] [Indexed: 10/15/2022]
Abstract
A major pathological hallmark of neurodegenerative diseases, including Alzheimer's, is a significant reduction in the white matter connecting the two cerebral hemispheres, as well as in the correlated activity between anatomically corresponding bilateral brain areas. However, the underlying circuit mechanisms and the cognitive relevance of cross-hemispheric (CH) communication remain poorly understood. Here, we show that novelty discrimination behavior activates CH neurons and enhances homotopic synchronized neural oscillations in the visual cortex. CH neurons provide excitatory drive required for synchronous neural oscillations between hemispheres, and unilateral inhibition of the CH circuit is sufficient to impair synchronous oscillations and novelty discrimination behavior. In the 5XFAD and Tau P301S mouse models, CH communication is altered, and novelty discrimination is impaired. These data reveal a hitherto uncharacterized CH circuit in the visual cortex, establishing a causal link between this circuit and novelty discrimination behavior and highlighting its impairment in mouse models of neurodegeneration.
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Affiliation(s)
- Chinnakkaruppan Adaikkan
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Jun Wang
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Karim Abdelaal
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Steven J Middleton
- Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, Wakoshi, Saitama 351-0198, Japan
| | - P Lorenzo Bozzelli
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ian R Wickersham
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Thomas J McHugh
- Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, Wakoshi, Saitama 351-0198, Japan; Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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6
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EEG Pattern Classification of Picking and Coordination Using Anonymous Random Walks. ALGORITHMS 2022. [DOI: 10.3390/a15040114] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Tacit coordination games are games where players are trying to select the same solution without any communication between them. Various theories have attempted to predict behavior in tacit coordination games. Until now, research combining tacit coordination games with electrophysiological measures was mainly based on spectral analysis. In contrast, EEG coherence enables the examination of functional and morphological connections between brain regions. Hence, we aimed to differentiate between different cognitive conditions using coherence patterns. Specifically, we have designed a method that predicts the class label of coherence graph patterns extracted out of multi-channel EEG epochs taken from three conditions: a no-task condition and two cognitive tasks, picking and coordination. The classification process was based on a coherence graph extracted out of the EEG record. To assign each graph into its appropriate label, we have constructed a hierarchical classifier. First, we have distinguished between the resting-state condition and the other two cognitive tasks by using a bag of node degrees. Next, to distinguish between the two cognitive tasks, we have implemented an anonymous random walk. Our classification model achieved a total accuracy value of 96.55%.
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Holmgren S, Andersson T, Berglund A, Aarsland D, Cummings J, Freund-Levi Y. Neuropsychiatric Symptoms in Dementia: Considering a Clinical Role for Electroencephalography. J Neuropsychiatry Clin Neurosci 2022; 34:214-223. [PMID: 35306829 PMCID: PMC9357098 DOI: 10.1176/appi.neuropsych.21050135] [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] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Degenerative dementia is characterized by progressive cognitive decline and neuropsychiatric symptoms. People with Alzheimer's disease (AD), the most common cause of dementia, show synaptic loss and disruption of functional brain networks along with neuritic plaques and neurofibrillary tangles. Electroencephalography (EEG) directly reflects synaptic activity, and among patients with AD it is associated with slowing of background activity. The purpose of this study was to identify associations between neuropsychiatric symptoms and EEG in patients with dementia and to determine whether EEG parameters could be used for clinical assessment of pharmacological treatment of neuropsychiatric symptoms in dementia (NPSD) with galantamine or risperidone. METHODS Seventy-two patients with EEG recordings and a score ≥10 on the Neuropsychiatric Inventory (NPI) were included. Clinical assessments included administration of the NPI, the Mini-Mental State Examination (MMSE), and the Cohen-Mansfield Agitation Inventory (CMAI). Patients underwent EEG examinations at baseline and after 12 weeks of treatment with galantamine or risperidone. EEG frequency analysis was performed. Correlations between EEG and assessment scale scores were statistically examined, as were EEG changes from baseline to the week 12 visit and the relationship with NPI, CMAI, and MMSE scores. RESULTS Significant correlations were found between NPI agitation and delta EEG frequencies at baseline and week 12. No other consistent and significant relationships were observed between NPSD and EEG at baseline, after NPSD treatment, or in the change in EEG from baseline to follow-up. CONCLUSIONS The limited informative findings in this study suggest that there exists a complex relationship between NPSD and EEG; hence, it is difficult to evaluate and use EEG for clinical assessment of pharmacological NPSD treatment.
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Affiliation(s)
- Simon Holmgren
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi)
| | - Thomas Andersson
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi)
| | - Anders Berglund
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi)
| | - Dag Aarsland
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi)
| | - Jeffrey Cummings
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi)
| | - Yvonne Freund-Levi
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi)
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8
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Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsalikakis D, Tsipouras MG. Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. Int J Neural Syst 2021; 31:2130002. [PMID: 33588710 DOI: 10.1142/s0129065721300023] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GR50100, Greece.,Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Vasileios Christou
- Q Base R&D, Science & Technology Park of Epirus, University of Ioannina Campus, Ioannina GR45110, Greece.,Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Pantelis Angelidis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Dimitrios Tsalikakis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Markos G Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
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9
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Oltu B, Akşahin MF, Kibaroğlu S. A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102223] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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10
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Sedghizadeh MJ, Hojjati H, Ezzatdoost K, Aghajan H, Vahabi Z, Tarighatnia H. Olfactory response as a marker for Alzheimer's disease: Evidence from perceptual and frontal lobe oscillation coherence deficit. PLoS One 2020; 15:e0243535. [PMID: 33320870 PMCID: PMC7737889 DOI: 10.1371/journal.pone.0243535] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 11/24/2020] [Indexed: 11/19/2022] Open
Abstract
High-frequency oscillations of the frontal cortex are involved in functions of the brain that fuse processed data from different sensory modules or bind them with elements stored in the memory. These oscillations also provide inhibitory connections to neural circuits that perform lower-level processes. Deficit in the performance of these oscillations has been examined as a marker for Alzheimer's disease (AD). Additionally, the neurodegenerative processes associated with AD, such as the deposition of amyloid-beta plaques, do not occur in a spatially homogeneous fashion and progress more prominently in the medial temporal lobe in the early stages of the disease. This region of the brain contains neural circuitry involved in olfactory perception. Several studies have suggested that olfactory deficit can be used as a marker for early diagnosis of AD. A quantitative assessment of the performance of the olfactory system can hence serve as a potential biomarker for Alzheimer's disease, offering a relatively convenient and inexpensive diagnosis method. This study examines the decline in the perception of olfactory stimuli and the deficit in the performance of high-frequency frontal oscillations in response to olfactory stimulation as markers for AD. Two measurement modalities are employed for assessing the olfactory performance: 1) An interactive smell identification test is used to sample the response to a sizable variety of odorants, and 2) Electroencephalography data are collected in an olfactory perception task with a pair of selected odorants in order to assess the connectivity of frontal cortex regions. Statistical analysis methods are used to assess the significance of selected features extracted from the recorded modalities as Alzheimer's biomarkers. Olfactory decline regressed to age in both healthy and mild AD groups are evaluated, and single- and multi-modal classifiers are also developed. The novel aspects of this study include: 1) Combining EEG response to olfactory stimulation with behavioral assessment of olfactory perception as a marker of AD, 2) Identification of odorants most significantly affected in mild AD patients, 3) Identification of odorants which are still adequately perceived by mild AD patients, 4) Analysis of the decline in the spatial coherence of different oscillatory bands in response to olfactory stimulation, and 5) Being the first study to quantitatively assess the performance of olfactory decline due to aging and AD in the Iranian population.
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Affiliation(s)
| | - Hadi Hojjati
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Kiana Ezzatdoost
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Hamid Aghajan
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Zahra Vahabi
- Department of Geriatric Medicine, Ziaeian Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Memory and Behavioral Neurology Division, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Heliya Tarighatnia
- Department of Geriatric Medicine, Ziaeian Hospital, Tehran University of Medical Sciences, Tehran, Iran
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Musaeus CS, Nielsen MS, Høgh P. Altered Low-Frequency EEG Connectivity in Mild Cognitive Impairment as a Sign of Clinical Progression. J Alzheimers Dis 2020; 68:947-960. [PMID: 30883355 DOI: 10.3233/jad-181081] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is associated with clinical progression to Alzheimer's disease (AD) but not all patients with MCI convert to AD. However, it is important to have methods that can differentiate between patients with MCI who progress (pMCI) and those who remain stable (sMCI), i.e., for timely administration of disease-modifying drugs. OBJECTIVE In the current study, we wanted to investigate whether quantitative EEG coherence and imaginary part of coherency (iCoh) could be used to differentiate between pMCI and sMCI. METHODS 17 patients with AD, 27 patients with MCI, and 38 older healthy controls were recruited and followed for three years and 2nd year was used to determine progression. EEGs were recorded at baseline and coherence and iCoh were calculated after thorough preprocessing. RESULTS Between pMCI and sMCI, the largest difference in total coherence was found in the theta and delta bands. Here, the significant differences for coherence and iCoh were found in the lower frequency bands involving the temporal-frontal connections for coherence and parietal-frontal connections for iCoh. Furthermore, we found a significant negative correlation between theta coherence and the Addenbrooke's Cognitive Examination (ACE) (p = 0.0378; rho = -0.2388). CONCLUSION These findings suggest that low frequency coherence and iCoh can be used to determine, which patients with MCI will progress to AD and is associated with the ACE score. Low-frequency coherence has previously been associated with increased hippocampal atrophy and degeneration of the cholinergic system and may be an early marker of AD pathology.
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Affiliation(s)
- Christian Sandøe Musaeus
- Department of Neurology, Danish Dementia Research Centre (DDRC), Rigshospitalet, University of Copenhagen, Denmark
| | - Malene Schjønning Nielsen
- Regional Dementia Research Centre, Department of Neurology, Zealand University Hospital, Roskilde, Denmark
| | - Peter Høgh
- Regional Dementia Research Centre, Department of Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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Jamaloo F, Mikaeili M, Noroozian M. Multi metric functional connectivity analysis based on continuous hidden Markov model with application in early diagnosis of Alzheimer’s disease. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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13
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Briels C, Stam C, Scheltens P, Bruins S, Lues I, Gouw A. In pursuit of a sensitive EEG functional connectivity outcome measure for clinical trials in Alzheimer’s disease. Clin Neurophysiol 2020; 131:88-95. [DOI: 10.1016/j.clinph.2019.09.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 07/19/2019] [Accepted: 09/15/2019] [Indexed: 01/01/2023]
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14
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Musaeus CS, Engedal K, Høgh P, Jelic V, Mørup M, Naik M, Oeksengaard AR, Snaedal J, Wahlund LO, Waldemar G, Andersen BB. Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer’s disease. Clin Neurophysiol 2019; 130:1889-1899. [DOI: 10.1016/j.clinph.2019.07.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/26/2019] [Accepted: 07/03/2019] [Indexed: 12/27/2022]
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15
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López-Sanz D, Bruña R, Delgado-Losada ML, López-Higes R, Marcos-Dolado A, Maestú F, Walter S. Electrophysiological brain signatures for the classification of subjective cognitive decline: towards an individual detection in the preclinical stages of dementia. Alzheimers Res Ther 2019; 11:49. [PMID: 31151467 PMCID: PMC6544924 DOI: 10.1186/s13195-019-0502-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 05/05/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) prevalence is rapidly growing as worldwide populations grow older. Available treatments have failed to slow down disease progression, thus increasing research focus towards early or preclinical stages of the disease. Subjective cognitive decline (SCD) is known to increase the risk of developing AD and several other negative outcomes. However, it is still very scarcely characterized and there is no neurophysiological study devoted to its individual classification which could improve targeted sample recruitment for clinical trials. METHODS Two hundred fifty-two older adults (70 healthy controls, 91 SCD, and 91 MCI) underwent a magnetoencephalography scan. Alpha relative power in the source space was employed to train a LASSO classifier and applied to distinguish between healthy controls and SCD. Moreover, MCI participants were used to further validate the previously trained algorithm. RESULTS The classifier was significantly associated to SCD with an AUC of 0.81 in the whole sample. After randomly splitting the sample in 2/3 for discovery and 1/3 for validation, the newly trained classifier was also able to correctly classify SCD individuals with an AUC of 0.75 in the validation sample. The regions selected by the algorithm included medial frontal, temporal, and occipital areas. The algorithm trained to select SCD individuals was also significantly associated to MCI diagnostic. CONCLUSIONS According to our results, magnetoencephalography could be a useful tool for distinguishing individuals with SCD and healthy older adults without cognitive concerns. Furthermore, our classifier showed good external validity, being not only successful for an unseen SCD sample, but also in a different population with MCI cases. This supports its utility in the context of preclinical dementia. These findings highlight the potential applications of electrophysiological techniques to improve sample recruitment at the individual level in the context of clinical trials.
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Affiliation(s)
- David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
- CIBER-BBN: Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, Zaragoza, Spain
| | | | - Ramón López-Higes
- Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | | | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain
- Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
- CIBER-BBN: Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, Zaragoza, Spain
| | - Stefan Walter
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, USA
- Dept. of Preventive Medicine and Public Health, University Rey Juan Carlos, Madrid, Spain
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16
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Musaeus CS, Shafi MM, Santarnecchi E, Herman ST, Press DZ. Levetiracetam Alters Oscillatory Connectivity in Alzheimer's Disease. J Alzheimers Dis 2018; 58:1065-1076. [PMID: 28527204 DOI: 10.3233/jad-160742] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Seizures occur at a higher frequency in people with Alzheimer's disease (AD) but overt, clinically obvious events are infrequent. Evidence from animal models and studies in mild cognitive impairment suggest that subclinical epileptic discharges may play a role in the clinical and pathophysiological manifestations of AD. In this feasibility study, the neurophysiological and cognitive effects of acute administration of levetiracetam (LEV) are measured in patients with mild AD to test whether it could have a therapeutic benefit. AD participants were administered low dose LEV (2.5 mg/kg), higher dose LEV (7.5 mg/kg), or placebo in a double-blind, within-subject repeated measures study with EEG recorded at rest before and after administration. After administration of higher dose of LEV, we found significant decreases in coherence in the delta band (1-3.99 Hz) and increases in the low beta (13-17.99 Hz) and the high beta band (24-29.99 Hz). Furthermore, we found trends toward increased power in the frontal and central regions in the high beta band (24-29.99 Hz). However, there were no significant changes in cognitive performance after this single dose administration. The pattern of decreased coherence in the lower frequency bands and increased coherence in the higher frequency bands suggests a beneficial effect of LEV for patients with AD. Larger longitudinal studies and studies with healthy age-matched controls are needed to determine whether this represents a relative normalization of EEG patterns, whether it is unique to AD as compared to normal aging, and whether longer term administration is associated with a beneficial clinical effect.
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Affiliation(s)
- Christian S Musaeus
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Department of Neurology, Danish Dementia Research Centre (DDRC), Rigshospitalet, University of Copenhagen, Denmark
| | - Mouhsin M Shafi
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, Brain Investigation and Neuromodulation Lab, (Si-BIN Lab), University of Siena, Italy
| | - Susan T Herman
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Daniel Z Press
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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17
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Velikova S, Nordtug B. Self-guided Positive Imagery Training: Effects beyond the Emotions-A Loreta Study. Front Hum Neurosci 2018; 11:644. [PMID: 29375344 PMCID: PMC5767265 DOI: 10.3389/fnhum.2017.00644] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Accepted: 12/18/2017] [Indexed: 11/23/2022] Open
Abstract
Previously we demonstrated that a 12-week lasting self-guided positive imagery training had a positive effect on the psycho-emotional state of healthy subjects and was associated with an increase in functional connectivity in the brain. Here we repeated the previous project, but expanded the study, testing the hypothesis that training can also affect cognitive functions. Twenty subjects (half of them with subthreshold depression according CES-D) participated in the program of positive imagery training for 12 weeks. The schedule began with group training for 2 days, followed by training at home. Evaluations of cognitive functions and electroencephalographic (EEG) activity were conducted during three examinations as follows: E0-baseline (1 month before the training); E1-pre-training and E2-post-training. CNS Vital Signs battery was used to test the following cognitive domains: verbal and visual memory, executive functions, cognitive flexibility, social acuity, non-verbal reasoning. EEGs (19-channel) were recorded at rest with closed eyes and analyzed with Low-resolution electromagnetic tomography software. One-way repeated measures ANOVA, followed by pairwise comparison showed a significant increase after training (E2 vs. E1; E2 vs. E0) in the number of correct hits for positive emotions received during perception of emotions test (POET); after the sample was split according to the initial presence of depressive symptoms, the effect was present only in the subgroup with subthreshold depressive symptomatology. Post-training (E2 vs. E1; E2 vs. E0) the number of correct answers on non-verbal reasoning test increased; this effect was observed only in the subgroup that does have any depressive symptoms. Comparison of EEG post-training vs. pre-training demonstrated a significant reduction in current source density (CSD) after the training in the left hemisphere (insular cortex, frontal and temporal lobes in delta, theta and alpha1 bands). The observed changes were presented only in the subgroup with initial subthreshold depressive symptomatology. A negative correlation was found between POET and CSD in the left insular cortex for theta band. No significant differences were observed when data from EEG and cognitive tests obtained during pre-training were compared with baseline values. Potential use of training for the rehabilitation of various disturbances with cognitive and emotional deficits is discussed.
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Affiliation(s)
- Svetla Velikova
- Central Scientific Research Laboratory, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia
| | - Bente Nordtug
- Faculty of Nursing and Health Science, Nord University Bodø, Bodø, Norway
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18
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Hata M, Tanaka T, Kazui H, Ishii R, Canuet L, Pascual-Marqui RD, Aoki Y, Ikeda S, Sato S, Suzuki Y, Kanemoto H, Yoshiyama K, Iwase M. Cerebrospinal Fluid Biomarkers of Alzheimer's Disease Correlate With Electroencephalography Parameters Assessed by Exact Low-Resolution Electromagnetic Tomography (eLORETA). Clin EEG Neurosci 2017; 48:338-347. [PMID: 27515698 DOI: 10.1177/1550059416662119] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Recently, cerebrospinal fluid (CSF) biomarkers related to Alzheimer's disease (AD) have garnered a lot of clinical attention. To explore neurophysiological traits of AD and parameters for its clinical diagnosis, we examined the association between CSF biomarkers and electroencephalography (EEG) parameters in 14 probable AD patients. Using exact low-resolution electromagnetic tomography (eLORETA), artifact-free 40-sesond EEG data were estimated with current source density (CSD) and lagged phase synchronization (LPS) as the EEG parameters. Correlations between CSF biomarkers and the EEG parameters were assessed. Patients with AD showed significant negative correlation between CSF beta-amyloid (Aβ)-42 concentration and the logarithms of CSD over the right temporal area in the theta band. Total tau concentration was negatively correlated with the LPS between the left frontal eye field and the right auditory area in the alpha-2 band in patients with AD. Our study results suggest that AD biomarkers, in particular CSF Aβ42 and total tau concentrations are associated with the EEG parameters CSD and LPS, respectively. Our results could yield more insights into the complicated pathology of AD.
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Affiliation(s)
- Masahiro Hata
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Toshihisa Tanaka
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hiroaki Kazui
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Ryouhei Ishii
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Leonides Canuet
- 2 UCM-UPM Centre for Biomedical Technology, Department of Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
| | - Roberto D Pascual-Marqui
- 3 The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland.,4 Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Yasunori Aoki
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan.,5 Nissay Hospital, Osaka, Japan
| | - Shunichiro Ikeda
- 4 Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Shunsuke Sato
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yukiko Suzuki
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hideki Kanemoto
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kenji Yoshiyama
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masao Iwase
- 1 Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
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19
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Ratti E, Waninger S, Berka C, Ruffini G, Verma A. Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials. Front Hum Neurosci 2017; 11:398. [PMID: 28824402 PMCID: PMC5540902 DOI: 10.3389/fnhum.2017.00398] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 07/18/2017] [Indexed: 02/03/2023] Open
Abstract
Objectives: To compare quantitative EEG signal and test-retest reliability of medical grade and consumer EEG systems. Methods: Resting state EEG was acquired by two medical grade (B-Alert, Enobio) and two consumer (Muse, Mindwave) EEG systems in five healthy subjects during two study visits. EEG patterns, power spectral densities (PSDs) and test/retest reliability in eyes closed and eyes open conditions were compared across the four systems, focusing on Fp1, the only common electrode. Fp1 PSDs were obtained using Welch's modified periodogram method and averaged for the five subjects for each visit. The test/retest results were calculated as a ratio of Visit 1/Visit 2 Fp1 channel PSD at each 1 s epoch. Results: B-Alert, Enobio, and Mindwave Fp1 power spectra were similar. Muse showed a broadband increase in power spectra and the highest relative variation across test-retest acquisitions. Consumer systems were more prone to artifact due to eye blinks and muscle movement in the frontal region. Conclusions: EEG data can be successfully collected from all four systems tested. Although there was slightly more time required for application, medical systems offer clear advantages in data quality, reliability, and depth of analysis over the consumer systems. Significance: This evaluation provides evidence for informed selection of EEG systemsappropriate for clinical trials.
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Affiliation(s)
- Elena Ratti
- BiogenCambridge, MA, United States,*Correspondence: Elena Ratti
| | - Shani Waninger
- Advanced Brain Monitoring, Inc.Carlsbad, CA, United States
| | - Chris Berka
- Advanced Brain Monitoring, Inc.Carlsbad, CA, United States
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20
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Wang B, Liu Y, Huang L, Chen J, Li JJ, Wang R, Kim E, Justicia C, Sakata K, Chen H, Planas A, Ostrom RS, Li W, Yang G, McDonald MP, Chen R, Heck D, Liao FF, Liao FF. A CNS-permeable Hsp90 inhibitor rescues synaptic dysfunction and memory loss in APP-overexpressing Alzheimer's mouse model via an HSF1-mediated mechanism. Mol Psychiatry 2017; 22:990-1001. [PMID: 27457810 PMCID: PMC5323357 DOI: 10.1038/mp.2016.104] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Revised: 03/27/2016] [Accepted: 04/20/2016] [Indexed: 11/09/2022]
Abstract
Induction of neuroprotective heat-shock proteins via pharmacological Hsp90 inhibitors is currently being investigated as a potential treatment for neurodegenerative diseases. Two major hurdles for therapeutic use of Hsp90 inhibitors are systemic toxicity and limited central nervous system permeability. We demonstrate here that chronic treatment with a proprietary Hsp90 inhibitor compound (OS47720) not only elicits a heat-shock-like response but also offers synaptic protection in symptomatic Tg2576 mice, a model of Alzheimer's disease, without noticeable systemic toxicity. Despite a short half-life of OS47720 in mouse brain, a single intraperitoneal injection induces rapid and long-lasting (>3 days) nuclear activation of the heat-shock factor, HSF1. Mechanistic study indicates that the remedial effects of OS47720 depend upon HSF1 activation and the subsequent HSF1-mediated transcriptional events on synaptic genes. Taken together, this work reveals a novel role of HSF1 in synaptic function and memory, which likely occurs through modulation of the synaptic transcriptome.
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Affiliation(s)
- Bin Wang
- Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Yu Liu
- Department of Anatomy & Neurobiology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Lianyan Huang
- Department of Anesthesiology, New York University School of Medicine, New York, NY 10016
| | - Jianjun Chen
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Jing jing Li
- Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Ruishan Wang
- Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Eunhee Kim
- Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Carles Justicia
- Department of Brain Ischemia and Neurodegeneration, Institute for Biomedical Research (IIBB-CSIC), Rossello 161, planta 6, 08036-Barcelona, Spain
| | - Kazuko Sakata
- Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Hao Chen
- Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Anna Planas
- Department of Brain Ischemia and Neurodegeneration, Institute for Biomedical Research (IIBB-CSIC), Rossello 161, planta 6, 08036-Barcelona, Spain
| | - Rennolds S Ostrom
- Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Wei Li
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Guang Yang
- Department of Anesthesiology, New York University School of Medicine, New York, NY 10016
| | - Michael P. McDonald
- Department of Anatomy & Neurobiology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163,Department of Neurology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Ruihong Chen
- Oncosynergy, Inc; 409 Illinois St., San Francisco, CA, 94158
| | - Detlef Heck
- Department of Anatomy & Neurobiology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163
| | - Francesca-Fang Liao
- Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163,Correspondence should be addressed to Francesca-Fang Liao, Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee 38163.
| | - F-F Liao
- Department of Pharmacology, University of Tennessee Health Science Center, College of Medicine, Memphis, TN, USA
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21
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Marshall AC, Cooper NR. The association between high levels of cumulative life stress and aberrant resting state EEG dynamics in old age. Biol Psychol 2017; 127:64-73. [PMID: 28501607 DOI: 10.1016/j.biopsycho.2017.05.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 04/25/2017] [Accepted: 05/05/2017] [Indexed: 10/19/2022]
Abstract
Cumulative experienced stress produces shortcomings in old adults' cognitive performance. These are reflected in electrophysiological changes tied to task execution. This study explored whether stress-related aberrations in older adults' electroencephalographic (EEG) activity were also apparent in the system at rest. To this effect, the amount of stressful life events experienced by 60 young and 60 elderly participants were assessed in conjunction with resting state power changes in the delta, theta, alpha, and beta frequencies during a resting EEG recording. Findings revealed elevated levels of delta power among elderly individuals reporting high levels of cumulative life stress. These differed significantly from young high and low stress individuals and old adults with low levels of stress. Increases of delta activity have been linked to the emergence of conditions such as Alzheimer's Disease and Mild Cognitive Impairment. Thus, a potential interpretation of our findings associates large amounts of cumulative stress with an increased risk of developing age-related cognitive pathologies in later life.
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Affiliation(s)
- Amanda C Marshall
- Department of General and Experimental Psychology, Ludwig-Maximilian University, 80539 Munich, Germany.
| | - Nicholas R Cooper
- Centre for Brain Science, Department of Psychology, University of Essex, Colchester CO4 3SQ, United Kingdom.
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22
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Chiang HS, Pao SC. An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease. J Med Syst 2016; 40:125. [PMID: 27059738 DOI: 10.1007/s10916-016-0476-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2015] [Accepted: 03/14/2016] [Indexed: 01/19/2023]
Abstract
Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early treatment of Alzheimer's disease can significantly reduce deterioration. Early diagnosis is difficult, and early symptoms are frequently overlooked. While much of the literature focuses on disease detection, the use of electroencephalography (EEG) in Alzheimer's diagnosis has received relatively little attention. This study combines the fuzzy and associative Petri net methodologies to develop a model for the effective and objective detection of Alzheimer's disease. Differences in EEG patterns between normal subjects and Alzheimer patients are used to establish prediction criteria for Alzheimer's disease, potentially providing physicians with a reference for early diagnosis, allowing for early action to delay the disease progression.
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Affiliation(s)
- Hsiu-Sen Chiang
- Department of Information Management, National Taichung University of Science and Technology, No. 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Republic of China.
| | - Shun-Chi Pao
- Department of Information Management, National Taichung University of Science and Technology, No. 129, Section 3, Sanmin Road, Taichung City 404, Taiwan, Republic of China
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