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Bipul MRS, Rahman MA, Hossain MF. Study on different brain activation rearrangement during cognitive workload from ERD/ERS and coherence analysis. Cogn Neurodyn 2024; 18:1709-1732. [PMID: 39104686 PMCID: PMC11297888 DOI: 10.1007/s11571-023-10032-6] [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: 09/21/2022] [Revised: 07/11/2023] [Accepted: 11/04/2023] [Indexed: 08/07/2024] Open
Abstract
The functional activities of the brain during any task like imaginary, motor, or cognitive are different in pattern as well as their area of activation in the brain is also different. This variation in pattern is also found in the brain's electrical variations that can be measured from the scalp of the brain using an electroencephalogram (EEG). This work exclusively studied a group of subjects' EEG data (available at: https://archive.physionet.org/physiobank/database/eegmat/) to unravel the activation pattern of the human brain during a mental arithmetic task. Since any cognitive task creates variations in EEG signal pattern, the relative changes in the signal power also occur which is also known as event-related desynchronization/synchronization (ERD/ERS). In this work, ERD/ERS have calculated the band-wise power spectral density (PSD) using Welch's method from the EEG signals. Besides, the coherence analysis was also performed to verify the results of ERD/ERS analysis from several randomly chosen subjects' EEG data. Here, subjects performing mental arithmetic tasks were grouped based on their performances: good (subtraction solved > 10 on average) and bad (subtraction solved ≤ 10 on average) to conduct group-specific ERD/ERS analysis regarding their performance in cognitive tasks. It was found that when the brain is on count condition, the variations found in the band power of theta and beta. The amounts of ERS in the left hemisphere are increased. When the task complexity increases, it contributes to an increase in relative ERD/ERS amounts and durations. The left and right hemispheres were asymmetrically distributed by both the pre-stimulus stages of the corresponding band power and relative ERD/ERS.
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Affiliation(s)
- Md. Rayahan Sarker Bipul
- Department of Biomedical Engineering, Khulna University of Engineering and Technology (KUET), Khulna, 9203 Bangladesh
| | - Md. Asadur Rahman
- Department of Biomedical Engineering, Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka, 1216 Bangladesh
| | - Md. Foisal Hossain
- Department of Electronics and Communication Engineering, Khulna University of Engineering and Technology (KUET), Khulna, 9203 Bangladesh
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Simfukwe C, Han SH, Jeong HT, Youn YC. qEEG as Biomarker for Alzheimer's Disease: Investigating Relative PSD Difference and Coherence Analysis. Neuropsychiatr Dis Treat 2023; 19:2423-2437. [PMID: 37965528 PMCID: PMC10642578 DOI: 10.2147/ndt.s433207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/04/2023] [Indexed: 11/16/2023] Open
Abstract
Purpose Electroencephalography (EEG) is a non-intrusive technique that provides comprehensive insights into the electrical activities of the brain's cerebral cortex. The brain signals obtained from EEGs can be used as a neuropsychological biomarker to detect different stages of Alzheimer's disease (AD) through quantitative EEG (qEEG) analysis. This paper investigates the difference in the abnormalities of resting state EEG (rEEG) signals between eyes-open (EOR) and eyes-closed (ECR) in AD by analyzing 19-scalp electrode EEG signals and making a comparison with healthy controls (HC). Participants and Methods The rEEG data from 534 subjects (ages 40-90) consisting of 269 HC and 265 AD subjects in South Korea were used in this study. The qEEG for EOR and ECR states were performed separately for HC and AD subjects to measure the relative power spectrum density (PSD) and coherence with functional connectivity to evaluate abnormalities. The rEEG data were preprocessed and analyzed using EEGlab and Brainstorm toolboxes in MATLAB R2021a software, and statistical analyses were carried out using ANOVA. Results Based on the Welch method, the relative PSD of the EEG EOR and ECR states difference in the AD group showed a significant increase in the delta frequency band of 19 EEG channels, particularly in the frontal, parietal, and temporal, than the HC groups. The delta power band on the source level was increased for the AD group and decreased for the HC group. In contrast, the source activities of alpha, beta, and gamma frequency bands were significantly reduced in the AD group, with a high decrease in the beta frequency band in all brain areas. Furthermore, the coherence of rEEG among different EEG electrodes was analyzed in the beta frequency band. It showed that pair-wise coherence between different brain areas in the AD group is remarkably increased in the ECR state and decreased after subtracting out the EOR state. Conclusion The findings suggest that examining PSD and functional connectivity through coherence analysis could serve as a promising and comprehensive approach to differentiate individuals with AD from normal, which may benefit our understanding of the disease.
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Affiliation(s)
- Chanda Simfukwe
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Su-Hyun Han
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Ho Tae Jeong
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
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3
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Decreased coherence in the model of the dorsal visual pathway associated with Alzheimer's disease. Sci Rep 2023; 13:3495. [PMID: 36859462 PMCID: PMC9977922 DOI: 10.1038/s41598-023-30535-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 02/24/2023] [Indexed: 03/03/2023] Open
Abstract
Decreased coherence in electroencephalogram (EEG) has been reported in Alzheimer's disease (AD) experimentally, which could be considered as a typical electrophysiological characteristic in AD. This work aimed to investigate the effect of AD on coherence in the dorsal visual pathway by the technique of neurocomputation. Firstly, according to the hierarchical organization of the cerebral cortex and the information flows of the dorsal visual pathway, a more physiologically plausible neural mass model including cortical areas v1, v2, and v5 was established in the dorsal visual pathway. The three interconnected cortical areas were connected by ascending and descending projections. Next, the pathological condition of loss of long synaptic projections in AD was simulated by reducing the parameters of long synaptic projections in the model. Then, the loss of long synaptic projections on coherence among different visual cortex areas was explored by means of power spectral analysis and coherence function. The results demonstrate that the coherence between these interconnected cortical areas showed an obvious decline with the gradual decrease of long synaptic projections, i.e. decrease in descending projections from area v2 to v1 and v5 to v2 and ascending projection from area v2 to v5. Hopefully, the results of this study could provide theoretical guidance for understanding the dynamical mechanism of AD.
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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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Covantes-Osuna C, López JB, Paredes O, Vélez-Pérez H, Romo-Vázquez R. Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold. SENSORS 2021; 21:s21248305. [PMID: 34960399 PMCID: PMC8704651 DOI: 10.3390/s21248305] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/05/2021] [Accepted: 12/06/2021] [Indexed: 12/15/2022]
Abstract
The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.
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Mosbacher JA, Waser M, Garn H, Seiler S, Coronel C, Dal-Bianco P, Benke T, Deistler M, Ransmayr G, Mayer F, Sanin G, Lechner A, Lackner HK, Schwingenschuh P, Grossegger D, Schmidt R. Functional (un-)Coupling: Impairment, Compensation, and Future Progression in Alzheimer's Disease. Clin EEG Neurosci 2021; 54:316-326. [PMID: 34658289 DOI: 10.1177/15500594211052208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: Functional (un-)coupling (task-related change of functional connectivity) between different sites of the brain is a mechanism of general importance for cognitive processes. In Alzheimer's disease (AD), prior research identified diminished cortical connectivity as a hallmark of the disease. However, little is known about the relation between the amount of functional (un-)coupling and cognitive performance and decline in AD. Method: Cognitive performance (based on CERAD-Plus scores) and electroencephalogram (EEG)-based functional (un-)coupling measures (connectivity changes from rest to a Face-Name-Encoding task) were assessed in 135 AD patients (age: M = 73.8 years; SD = 9.0). Of these, 68 patients (M = 73.9 years; SD = 8.9) participated in a follow-up assessment of their cognitive performance 1.5 years later. Results: The amounts of functional (un-)coupling in left anterior-posterior and homotopic interhemispheric connections in beta1-band were related to cognitive performance at baseline (β = .340; p < .001; β = .274; P = .001, respectively). For both markers, a higher amount of functional coupling was associated with better cognitive performance. Both markers also were significant predictors for cognitive decline. However, while patients with greater functional coupling in left anterior-posterior connections declined less in cognitive performance (β = .329; P = .035) those with greater functional coupling in interhemispheric connections declined more (β = -.402; P = .010). Conclusion: These findings suggest an important role of functional coupling mechanisms in left anterior-posterior and interhemispheric connections in AD. Especially the complex relationship with cognitive decline in AD patients might be an interesting aspect for future studies.
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Affiliation(s)
| | - Markus Waser
- Center for Digital Safety and Security, AIT Austrian Institute of Technology, Vienna, Austria
| | - Heinrich Garn
- Center for Digital Safety and Security, AIT Austrian Institute of Technology, Vienna, Austria
| | - Stephan Seiler
- Department of Neurology, 31475Medical University of Graz, Graz, Austria
| | - Carmina Coronel
- Center for Digital Safety and Security, AIT Austrian Institute of Technology, Vienna, Austria
| | - Peter Dal-Bianco
- Department of Neurology, 27271Medical University of Vienna, Vienna, Austria
| | - Thomas Benke
- Department of Neurology, 27280Medical University of Innsbruck, Innsbruck, Austria
| | - Manfred Deistler
- Institute of Statistics and Mathematical Methods in Economics, 27259Vienna University of Technology, Vienna, Austria
| | - Gerhard Ransmayr
- Department of Neurology 2, 31197Kepler University Hospital Linz, Med Campus III, Linz, Austria
| | - Florian Mayer
- Department of Neurology, 27271Medical University of Vienna, Vienna, Austria
| | - Guenter Sanin
- Department of Neurology, 27280Medical University of Innsbruck, Innsbruck, Austria
| | - Anita Lechner
- Department of Neurology, 31475Medical University of Graz, Graz, Austria
| | - Helmut K Lackner
- Division of Physiology, Otto Loewi Research Center, Medical University of Graz, Graz, Austria
| | | | | | - Reinhold Schmidt
- Department of Neurology, 31475Medical University of Graz, Graz, Austria
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Fodor Z, Horváth A, Hidasi Z, Gouw AA, Stam CJ, Csukly G. EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance. Front Aging Neurosci 2021; 13:680200. [PMID: 34690735 PMCID: PMC8529331 DOI: 10.3389/fnagi.2021.680200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 09/20/2021] [Indexed: 12/18/2022] Open
Abstract
Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer's disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints. Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network. Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution. Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the "hub overload and failure" framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.
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Affiliation(s)
- Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - András Horváth
- Department of Neurology, National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Zoltán Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Alida A. Gouw
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
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Yan Y, Zhao A, Ying W, Qiu Y, Ding Y, Wang Y, Xu W, Deng Y. Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer's Disease. Curr Alzheimer Res 2021; 18:513-522. [PMID: 34598666 DOI: 10.2174/1567205018666211001110824] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 06/24/2021] [Accepted: 08/22/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer's Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD. METHODS We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores. RESULTS In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores. CONCLUSION Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.
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Affiliation(s)
- Yi Yan
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Aonan Zhao
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weina Ying
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinghui Qiu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanfei Ding
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Wang
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Xu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yulei Deng
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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9
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Hamzenejad A, Ghoushchi SJ, Baradaran V. Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5516819. [PMID: 34504897 PMCID: PMC8423553 DOI: 10.1155/2021/5516819] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 02/26/2021] [Accepted: 08/16/2021] [Indexed: 11/18/2022]
Abstract
Automated detection of brain tumor location is essential for both medical and analytical uses. In this paper, we clustered brain MRI images to detect tumor location. To obtain perfect results, we presented an unsupervised robust PCA algorithm to clustered images. The proposed method clusters brain MR image pixels to four leverages. The algorithm is implemented for five brain diseases such as glioma, Huntington, meningioma, Pick, and Alzheimer's. We used ten images of each disease to validate the optimal identification rate. According to the results obtained, 2% of the data in the bad leverage part of the image were determined, which acceptably discerned the tumor. Results show that this method has the potential to detect tumor location for brain disease with high sensitivity. Moreover, results show that the method for the Glioma images has approximately better results than others. However, according to the ROC curve for all selected diseases, the present method can find lesion location.
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Affiliation(s)
- Ali Hamzenejad
- Department of Industrial Engineering, Islamic Azad University, Tehran North Branch, Tehran, Iran
| | | | - Vahid Baradaran
- Department of Industrial Engineering, Islamic Azad University, Tehran North Branch, Tehran, Iran
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10
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Meiron O, Ezra Tsur E, Factor H, Jacobsen S, Salomon DY, Kraizler N, Jaul E. Left-prefrontal alpha-dynamics predict executive working-memory functioning in elderly people. Cogn Neurosci 2021; 13:15-25. [PMID: 33886412 DOI: 10.1080/17588928.2021.1911977] [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: 10/21/2022]
Abstract
Recent findings suggest that electroencephalography (EEG) oscillations in the theta and alpha frequency-bands reflect synchronized interregional neuronal activity and are considered to reflect cognitive-control, and executive working memory mechanisms in humans. Above the age of 50 years, hypothesized pronounced alterations in alpha and theta-band power at resting or across different WM-functioning brain states may well be due to pre-dementia cognitive impairments, or increasing severity of age-related neurological disorders. Executive working memory (EWM) functioning was assessed in older-adult participants (54 to 83 years old) by obtaining their WM-related EEG oscillations and WM performance scores. WM performance and WM brain-state EEG were recorded during online-WM periods as well as during specific online WM events within EWM periods, and during resting offline-WM periods that preceded online-WM periods. Left-prefrontal alpha-power was enhanced during offline-WM periods versus online-WM periods and was significantly related to WM accuracy. Left-prefrontal alpha power and left prefrontal-parietal theta power anterior-posterior difference-gradient during online WM activity were related to reaction times (RT's). Importantly, during active-storage events, WM-offset offline-periods, and preparatory pre-retrieval events, excessive left-prefrontal alpha activity was related to poor EWM performance. The potential for developing targeted noninvasive cognition-enhancing interventions and developing clinical-monitoring EEG-based biomarkers of pathological cognitive-decline in elderly people is discussed.
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Affiliation(s)
- Oded Meiron
- Clinical Research Center for Brain Sciences, Herzog Medical Center, Jerusalem, Israel
| | - Elishai Ezra Tsur
- Neuro-Biomorphic Engineering Lab (NBEL), Department of Mathematics and Computer Science, The Open University of Israel, Raanana, Israel
| | - Hagai Factor
- Clinical Research Center for Brain Sciences, Herzog Medical Center, Jerusalem, Israel
| | - Shoham Jacobsen
- Department of Computer Science, Jerusalem College of Technology, Jerusalem, Israel
| | - David Yoel Salomon
- Department of Computer Science, Jerusalem College of Technology, Jerusalem, Israel
| | - Nir Kraizler
- Department of Computer Science, Jerusalem College of Technology, Jerusalem, Israel
| | - Efraim Jaul
- Geriatric Skilled Nursing Department, Herzog Medical Center, Jerusalem, Israel.,School of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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Das S, Puthankattil SD. Complex network analysis of MCI-AD EEG signals under cognitive and resting state. Brain Res 2020; 1735:146743. [PMID: 32114060 DOI: 10.1016/j.brainres.2020.146743] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 01/09/2020] [Accepted: 02/26/2020] [Indexed: 12/17/2022]
Abstract
OBJECTIVE The purpose of this study is to characterize functional connectivity changes in mild cognitive impaired Alzheimer's disease (MCI-AD) under resting and cognitive task conditions. METHOD EEG signals were recorded under resting states (Eyes closed (EC) and Eyes open (EO)) and cognitive states (Mental Arithmetic Eyes closed (MAEC) and Mental Arithmetic Eyes open (MAEO)) conditions. Functional connectivity metrics were calculated using weighted phase lag index (WPLI). Topological features of the functional connectivity network were analyzed through minimum spanning tree (MST) algorithm. Betweenness centrality was estimated in five different regions of the brain to study hub importance. RESULTS An increase in values of eccentricity and diameter were observed in patient group in five frequency bands of delta, theta, alpha1, alpha 2 and beta bands under resting and cognitive states. A reduction in leaf fraction was observed in alpha 1 band of EO condition. The results indicated a reduction in functional integration in the brain network of MCI-AD patients. Analysis of MST parameters revealed a higher disintegrated network for the alpha band under EO protocol. The study of hub status in the network displayed an elevated hub status in the central region for the patient group under cognitive task. The study also observed increased integration in gamma band in MCI - AD subjects than healthy controls under cognitive load. CONCLUSION Disintegration of functional network in alpha band under eyes open protocol and elevated hub strength in central region during cognitive task could be distinguishing features that could aid early detection of AD.
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Affiliation(s)
- Surya Das
- Department of Electrical Engineering, National Institute of Technology, Calicut 673601, Kerala, India.
| | - Subha D Puthankattil
- Department of Electrical Engineering, National Institute of Technology, Calicut 673601, Kerala, India.
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12
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A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics. PLoS One 2020; 15:e0227613. [PMID: 31951604 PMCID: PMC6968862 DOI: 10.1371/journal.pone.0227613] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 12/21/2019] [Indexed: 11/30/2022] Open
Abstract
Recent studies aiming to facilitate mathematical skill development in primary school children have explored the electrophysiological characteristics associated with different levels of arithmetic achievement. The present work introduces an alternative EEG signal characterization using graph metrics and, based on such features, a classification analysis using a decision tree model. This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children. The methods of analysis utilized were signal-processing (EEG artifact removal, Laplacian filtering, and magnitude square coherence measurement) and the characterization (Graph metrics) and classification (Decision Tree) of EEG signals recorded during performance of a numerical comparison task. Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement. Specifically, the most significant results showed an accuracy of 80.00% (α band), 78.33% (δ band), and 76.67% (θ band) in differentiating high-skilled participants from low-skilled ones, averaged-skilled subjects from all others, and averaged-skilled participants from low-skilled ones, respectively. The use of a decision tree tool during the classification stage allows the identification of several brain areas that seem to be more specialized in numerical processing.
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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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Cui D, Qi S, Gu G, Li X, Li Z, Wang L, Yin S. Magnitude Squared Coherence Method based on Weighted Canonical Correlation Analysis for EEG Synchronization Analysis in Amnesic Mild Cognitive Impairment of Diabetes Mellitus. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1908-1917. [DOI: 10.1109/tnsre.2018.2862396] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Ranchet M, Morgan JC, Akinwuntan AE, Devos H. Cognitive workload across the spectrum of cognitive impairments: A systematic review of physiological measures. Neurosci Biobehav Rev 2017; 80:516-537. [PMID: 28711663 DOI: 10.1016/j.neubiorev.2017.07.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 07/06/2017] [Accepted: 07/08/2017] [Indexed: 10/19/2022]
Abstract
Our objective was to identify the physiological measures that are sensitive to assessing cognitive workload across the spectrum of cognitive impairments. Three database searches were conducted: PubMed, PsychINFO, and Web of Science. Studies from the last decade that used physiological measures of cognitive workload in older adults (mean age >65 years-old) were reviewed. The cognitive workload of healthy older individuals was compared with the cognitive workload of younger adults, patients with mild cognitive impairment (MCI), and patients with Alzheimer's diseases (AD). The most common measures of cognitive workload included: electroencephalography, magnetoencephalography, functional magnetic resonance imaging, pupillometry, and heart rate variability. These physiological measures consistently showed greater cognitive workload in healthy older adults compared to younger adults when performing the same task. The same was observed in patients with MCI compared to healthy older adults. Behavioral performance declined when the available cognitive resources became insufficient to cope with the cognitive demands of a task, such as in AD. These findings may have implications for clinical practice and future cognitive interventions.
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Affiliation(s)
- Maud Ranchet
- Univ. Lyon, IFSTTAR, TS2, LESCOT, F-69675 Lyon, France.
| | - John C Morgan
- Parkinson's Foundation Center of Excellence, Movement and Memory Disorder Programs, Department of Neurology, Medical College of Georgia, Augusta University, Augusta, GA, USA
| | - Abiodun E Akinwuntan
- Dean's office, School of Health Professions, The University of Kansas Medical Center, Kansas City, KS, USA
| | - Hannes Devos
- Department of Physical Therapy and Rehabilitation Science, School of Health Professions, The University of Kansas Medical Center, Kansas City, KS, USA
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16
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Han Y, Wang K, Jia J, Wu W. Changes of EEG Spectra and Functional Connectivity during an Object-Location Memory Task in Alzheimer's Disease. Front Behav Neurosci 2017; 11:107. [PMID: 28620287 PMCID: PMC5449767 DOI: 10.3389/fnbeh.2017.00107] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 05/18/2017] [Indexed: 12/19/2022] Open
Abstract
Object-location memory is particularly fragile and specifically impaired in Alzheimer’s disease (AD) patients. Electroencephalogram (EEG) was utilized to objectively measure memory impairment for memory formation correlates of EEG oscillatory activities. We aimed to construct an object-location memory paradigm and explore EEG signs of it. Two groups of 20 probable mild AD patients and 19 healthy older adults were included in a cross-sectional analysis. All subjects took an object-location memory task. EEG recordings performed during object-location memory tasks were compared between the two groups in the two EEG parameters (spectral parameters and phase synchronization). The memory performance of AD patients was worse than that of healthy elderly adults The power of object-location memory of the AD group was significantly higher than the NC group (healthy elderly adults) in the alpha band in the encoding session, and alpha and theta bands in the retrieval session. The channels-pairs the phase lag index value of object-location memory in the AD group was clearly higher than the NC group in the delta, theta, and alpha bands in encoding sessions and delta and theta bands in retrieval sessions. The results provide support for the hypothesis that the AD patients may use compensation mechanisms to remember the items and episode.
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Affiliation(s)
- Yuliang Han
- Department of Neurology, Chinese PLA General HospitalBeijing, China.,Department of Neurology, Chinese PLA 305 HospitalBeijing, China
| | - Kai Wang
- Department of Neurology, Chinese PLA 305 HospitalBeijing, China
| | - Jianjun Jia
- Department of Neurology, Chinese PLA General HospitalBeijing, China
| | - Weiping Wu
- Department of Neurology, Chinese PLA General HospitalBeijing, China
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17
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Waser M, Garn H, Schmidt R, Benke T, Dal-Bianco P, Ransmayr G, Schmidt H, Seiler S, Sanin G, Mayer F, Caravias G, Grossegger D, Frühwirt W, Deistler M. Quantifying synchrony patterns in the EEG of Alzheimer's patients with linear and non-linear connectivity markers. J Neural Transm (Vienna) 2016; 123:297-316. [PMID: 26411482 PMCID: PMC4766239 DOI: 10.1007/s00702-015-1461-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Accepted: 09/11/2015] [Indexed: 11/25/2022]
Abstract
We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.
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Affiliation(s)
- Markus Waser
- AIT Austrian Institute of Technology GmbH, Vienna, Austria.
| | - Heinrich Garn
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | - Reinhold Schmidt
- Department of Neurology, Clinical Section of Neurogeriatrics, Graz Medical University, Graz, Austria
| | - Thomas Benke
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Peter Dal-Bianco
- Department of Neurology, Vienna Medical University, Vienna, Austria
| | - Gerhard Ransmayr
- Department of Neurology and Psychiatry, Linz General Hospital, Linz, Austria
| | - Helena Schmidt
- Institute of Molecular Biology and Biochemistry, Graz Medical University, Graz, Austria
| | - Stephan Seiler
- Department of Neurology, Clinical Section of Neurogeriatrics, Graz Medical University, Graz, Austria
| | - Günter Sanin
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Florian Mayer
- Department of Neurology, Vienna Medical University, Vienna, Austria
| | - Georg Caravias
- Department of Neurology and Psychiatry, Linz General Hospital, Linz, Austria
| | | | | | - Manfred Deistler
- Institute for Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria
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18
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Waser M, Garn H, Deistler M, Benke T, Dal-Bianco P, Ransmayr G, Schmidt H, Sanin G, Santer P, Caravias G, Seiler S, Grossegger D, Fruehwirt W, Schmidt R. Using static and dynamic canonical correlation coefficients as quantitative EEG markers for Alzheimer's disease severity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:2801-4. [PMID: 25570573 DOI: 10.1109/embc.2014.6944205] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We analyzed the relation between Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores and quantitative electroencephalographic (qEEG) markers that were derived from canonical correlation analysis. This allowed an investigation of EEG synchrony between groups of EEG channels. In this study, we applied the data from 79 participants in the multi-centric cohort study PRODEM-Austria with probable AD. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. A quadratic regression model was used to describe the relation between MMSE and the qEEG synchrony markers. This relation was most significant in the δ and θ frequency bands in resting state, and between left-hemispheric central, temporal and parietal channel groups during the cognitive task. Here, the MMSE explained up to 40% of the qEEG marker's variation. QEEG markers showed an ambiguous trend, i.e. an increase of EEG synchrony in the initial stage of AD (MMSE>20) and a decrease in later stages. This effect could be caused by compensatory brain mechanisms. We conclude that the proposed qEEG markers are closely related to AD severity. Despite the ambiguous trend and the resulting diagnostic ambiguity, the qEEG markers could provide aid in the diagnostics of early-stage AD.
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Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information. Clin Neurophysiol 2015; 127:335-348. [PMID: 26142876 DOI: 10.1016/j.clinph.2015.05.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Revised: 04/29/2015] [Accepted: 05/12/2015] [Indexed: 12/12/2022]
Abstract
OBJECTIVE This study was meant to explore whether the coupling strength and direction of resting-state electroencephalogram (rsEEG) could be used as an indicator to distinguish the patients of type 2 diabetes mellitus (T2DM) with or without amnestic mild cognitive impairment (aMCI). METHODS Permutation conditional mutual information (PCMI) was used to calculate the coupling strength and direction of rsEEG signals between different brain areas of 19 aMCI and 20 normal control (NC) with T2DM on 7 frequency bands: Delta, Theta, Alpha1, Alpha2, Beta1, Beta2 and Gamma. The difference in coupling strength or direction of rsEEG between two groups was calculated. The correlation between coupling strength or direction of rsEEG and score of different neuropsychology scales were also calculated. RESULTS We have demonstrated that PCMI can calculate effectively the coupling strength and directionality of EEG signals between different brain regions. The significant difference in coupling strength and directionality of EEG signals was found between the patients of aMCI and NC with T2DM on different brain regions. There also existed significant correlation between sex or age and coupling strength or coupling directionality of EEG signals between a few different brain regions from all subjects. CONCLUSIONS The coupling strength or directionality of EEG signals calculated by PCMI are significantly different between aMCI and NC with T2DM. SIGNIFICANCE These results showed that the coupling strength or directionality of EEG signals calculated by PCMI might be used as a biomarker in distinguishing the aMCI from NC with T2DM.
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20
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Power spectral density and coherence analysis of Alzheimer's EEG. Cogn Neurodyn 2014; 9:291-304. [PMID: 25972978 DOI: 10.1007/s11571-014-9325-x] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Revised: 12/03/2014] [Accepted: 12/10/2014] [Indexed: 10/24/2022] Open
Abstract
In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.
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21
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Wang R, Wang J, Yu H, Wei X, Yang C, Deng B. Decreased coherence and functional connectivity of electroencephalograph in Alzheimer's disease. CHAOS (WOODBURY, N.Y.) 2014; 24:033136. [PMID: 25273216 DOI: 10.1063/1.4896095] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. Coherence is introduced to measure the pair-wise normalized linear synchrony and functional correlations between two EEG signals in different frequency domains, and graph analysis is further used to investigate the influence of AD on the functional connectivity of human brain. Data analysis results show that, compared with the control group, the pair-wise coherence of AD group is significantly decreased, especially for the theta and alpha frequency bands in the frontal and parieto-occipital regions. Furthermore, functional connectivity among different brain regions is reconstructed based on EEG, which exhibit obvious small-world properties. Graph analysis demonstrates that the local functional connections between regions for AD decrease. In addition, it is found that small-world properties of AD networks are largely weakened, by calculating its average path lengths, clustering coefficients, global efficiency, local efficiency, and small-worldness. The obtained results show that both pair-wise coherence and functional network can be taken as effective measures to distinguish AD patients from the normal, which may benefit our understanding of the disease.
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Affiliation(s)
- Ruofan Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Haitao Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Xile Wei
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Chen Yang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
| | - Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
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22
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Garn H, Waser M, Deistler M, Benke T, Dal-Bianco P, Ransmayr G, Schmidt H, Sanin G, Santer P, Caravias G, Seiler S, Grossegger D, Fruehwirt W, Schmidt R. Quantitative EEG markers relate to Alzheimer's disease severity in the Prospective Dementia Registry Austria (PRODEM). Clin Neurophysiol 2014; 126:505-13. [PMID: 25091343 DOI: 10.1016/j.clinph.2014.07.005] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2014] [Revised: 06/26/2014] [Accepted: 07/07/2014] [Indexed: 10/25/2022]
Abstract
OBJECTIVE To investigate which single quantitative electro-encephalographic (QEEG) marker or which combination of markers correlates best with Alzheimer's disease (AD) severity as measured by the Mini-Mental State Examination (MMSE). METHODS We compared quantitative EEG markers for slowing (relative band powers), synchrony (coherence, canonical correlation, Granger causality) and complexity (auto-mutual information, Shannon/Tsallis entropy) in 118 AD patients from the multi-centric study PRODEM Austria. Signal spectra were determined using an indirect spectral estimator. Analyses were adjusted for age, sex, duration of dementia, and level of education. RESULTS For the whole group (39 possible, 79 probable AD cases) MMSE scores explained 33% of the variations in relative theta power during face encoding, and 31% of auto-mutual information in resting state with eyes closed. MMSE scores explained also 25% of the overall QEEG factor. This factor was thus subordinate to individual markers. In probable AD, QEEG coefficients of determination were always higher than in the whole group, where MMSE scores explained 51% of the variations in relative theta power. CONCLUSIONS Selected QEEG markers show strong associations with AD severity. Both cognitive and resting state should be used for QEEG assessments. SIGNIFICANCE Our data indicate theta power measured during face-name encoding to be most closely related to AD severity.
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Affiliation(s)
- Heinrich Garn
- AIT Austrian Institute of Technology GmbH, Vienna, Austria.
| | - Markus Waser
- AIT Austrian Institute of Technology GmbH, Vienna, Austria
| | | | - Thomas Benke
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Peter Dal-Bianco
- Department of Neurology, Vienna Medical University, Vienna, Austria
| | | | - Helena Schmidt
- Department of Neurology, Graz Medical University, Graz, Austria
| | - Guenter Sanin
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Peter Santer
- Department of Neurology, Vienna Medical University, Vienna, Austria
| | - Georg Caravias
- Department of Neurology, Linz General Hospital, Linz, Austria
| | - Stephan Seiler
- Department of Neurology, Graz Medical University, Graz, Austria
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Quantitative EEG in Alzheimer's disease: cognitive state, resting state and association with disease severity. Int J Psychophysiol 2014; 93:390-7. [PMID: 24933410 DOI: 10.1016/j.ijpsycho.2014.06.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2014] [Revised: 06/03/2014] [Accepted: 06/06/2014] [Indexed: 11/20/2022]
Abstract
BACKGROUND Quantitative electroencephalogram (qEEG) recorded during cognitive tasks has been shown to differentiate between patients with Alzheimer's disease (AD) and healthy individuals. However, the association between various qEEG markers recorded during mnestic paradigms and clinical measures of AD has not been studied in detail. OBJECTIVE To evaluate if 'cognitive' qEEG is a useful diagnostic option, particularly if memory paradigms are used as cognitive stimulators. METHODS This study is part of the Prospective Registry on Dementia in Austria (PRODEM), a multicenter dementia research project. A cohort of 79 probable AD patients was included in a cross-sectional analysis. qEEG recordings performed in resting states were compared with recordings during cognitively active states. Cognition was evoked with a face-name paradigm and a paired-associate word list task, respectively. Relative band powers, coherence and auto-mutual information were computed as functions of MMSE scores for the memory paradigms and during rest. Analyses were adjusted for the co-variables age, sex, duration of dementia and educational level. RESULTS MMSE scores explained 36-51% of the variances of qEEG-markers. Face-name encoding with eyes open was superior to resting state with eyes closed in relative theta and beta1 power as well as coherence, whereas relative alpha power and auto-mutual information yielded more significant results during resting state with eyes closed. The face-name task yielded stronger correlations with MMSE scores than the verbal memory task. CONCLUSION qEEG alterations recorded during mnestic activity, particularly face-name encoding showed the highest association with the MMSE and may serve as a clinically valuable marker for disease severity.
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Dauwels J, Vialatte F, Musha T, Cichocki A. A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG. Neuroimage 2010; 49:668-93. [PMID: 19573607 DOI: 10.1016/j.neuroimage.2009.06.056] [Citation(s) in RCA: 222] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2009] [Revised: 06/08/2009] [Accepted: 06/17/2009] [Indexed: 11/18/2022] Open
Affiliation(s)
- J Dauwels
- Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Pachou E, Vourkas M, Simos P, Smit D, Stam CJ, Tsirka V, Micheloyannis S. Working memory in schizophrenia: an EEG study using power spectrum and coherence analysis to estimate cortical activation and network behavior. Brain Topogr 2008; 21:128-37. [PMID: 18726681 DOI: 10.1007/s10548-008-0062-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2008] [Accepted: 08/08/2008] [Indexed: 11/28/2022]
Abstract
This study examined regional cortical activations and cortico-cortical connectivity in a group of 20 high-functioning patients with schizophrenia and 20 healthy controls matched for age and sex during a 0- and a 2-back working memory (WM) task. An earlier study comparing schizophrenia patients with education level-matched healthy controls revealed less "optimally" organized network during the 2-back task, whereas a second study with healthy volunteers had suggested that the degree of cortical organization may be inversely proportional to educational level (less optimal functional connectivity in better educated individuals interpreted as the result of higher efficiency). In the present study, both groups succeeded in the 2-back WM task although healthy individuals had generally attained a higher level of education. First absolute power spectrum of the different frequency bands corresponding to the electrodes of each lobe was calculated. Then the mean values of coherence were calculated as an index of the average synchronization to construct graphs in order to characterize local and large scale topological patterns of cortico-cortical connectivity. The power spectra analyses showed signs of hypofrontality in schizophrenics with an asymmetry. Additionally, differences between the groups with greater changes during WM in healthy individuals were visible in all lobes more on the left side. The graph parameter results indicated decreased small-world architecture i.e. less optimal cortico-cortical functional organization in patients as compared to controls. These findings are consistent with the notion of aberrant neural organization in schizophrenics which is nevertheless sufficient in supporting adequate task performance.
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Affiliation(s)
- Ellie Pachou
- Medical Division, University of Crete, 71409, Iraklion, Crete, Greece.
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26
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Decreased EEG synchronization and its correlation with symptom severity in Alzheimer's disease. Neurosci Res 2008; 62:112-7. [PMID: 18672010 DOI: 10.1016/j.neures.2008.06.009] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2008] [Revised: 05/29/2008] [Accepted: 06/25/2008] [Indexed: 11/24/2022]
Abstract
BACKGROUND Global field synchronization (GFS) has recently been introduced to measure functional synchronization in frequency-domain EEG data. This study explored GFS values and its clinical significance in patients with Alzheimer's disease (AD). METHOD EEGs were recorded from 22 AD patients and 23 age-matched healthy controls. GFS values were computed in the delta, theta, alpha, beta1, beta2, beta3, gamma, and full frequency bands. The Mini-Mental Status Examination (MMSE) and the Clinical Dementia Rating scale (CDR) were used to assess the symptom severity in AD patients. RESULTS GFS values in the beta1, beta2, beta3, and full bands were lower in AD patients than in healthy controls. GFS values in the alpha, beta1, beta2, beta3, and full bands were positively correlated with the MMSE and CDR scores in combined group (AD patients and healthy controls). In AD patients, GFS values were positively correlated with MMSE scores in the beta1, beta 3, and full bands, and with CDR scores in the delta band. CONCLUSION GFS values were significantly lower in AD patients than in healthy controls, and they were positively correlated with MMSE and CDR scores. Our results suggest that GFS values are a useful biological correlate of cognitive decline in AD patients.
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