1
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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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2
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Hyperconnectivity matters in early-onset Alzheimer's disease: a resting-state EEG connectivity study. Neurophysiol Clin 2022; 52:459-471. [DOI: 10.1016/j.neucli.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 10/17/2022] [Accepted: 10/21/2022] [Indexed: 11/11/2022] Open
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3
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Fide E, Yerlikaya D, Öz D, Öztura İ, Yener G. Normalized Theta but Increased Gamma Activity after Acetylcholinesterase Inhibitor Treatment in Alzheimer's Disease: Preliminary qEEG Study. Clin EEG Neurosci 2022; 54:305-315. [PMID: 35957592 DOI: 10.1177/15500594221120723] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Acetylcholinesterase inhibitors (AChE-I) are the core treatment of mild to severe Alzheimer's disease (AD). However, the efficacy of AChE-I treatment on electroencephalography (EEG) and cognition remains unclear. We aimed to investigate the EEG power and coherence changes, in addition to neuropsychological performance, following a one-year treatment. Nine de-novo AD patients and demographically-matched healthy controls (HC) were included. After baseline assessments, all AD participants started cholinergic therapy. We found that baseline and follow-up gamma power analyzes were similar between groups. Yet, within the AD group after AChE-I intake, individuals with AD displayed higher gamma power compared to their baselines (P < .039). Also, baseline gamma coherence analysis showed lower values in the AD than in HC (P < .048), while these differences disappeared with increased gamma values of AD patients at the follow-up. Within the AD group after AChE-I intake, individuals with AD displayed higher theta and alpha coherence compared to their baselines (all, P < .039). These increased results within the AD group may result from a subclinical epileptiform activity. Even though AChE-I is associated with lower mortality, our results showed a significant effect on EEG power yet can increase the subclinical epileptiform activity. It is essential to be conscious of the seizure risk that treatment may cause.
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Affiliation(s)
- Ezgi Fide
- Department of Neurosciences, Institute of Health Sciences, 37508Dokuz Eylül University, Izmir, Turkey
| | - Deniz Yerlikaya
- Department of Neurosciences, Institute of Health Sciences, 37508Dokuz Eylül University, Izmir, Turkey
| | - Didem Öz
- Department of Neurosciences, Institute of Health Sciences, 37508Dokuz Eylül University, Izmir, Turkey.,Department of Neurology, 37508Dokuz Eylül University Medical School, Izmir, Turkey.,Global Brain Health Institute, 8785University of California San Francisco, San Francisco, CA, USA.,Brain Dynamics Multidisciplinary Research Center, 37508Dokuz Eylül University, Izmir, Turkey
| | - İbrahim Öztura
- Department of Neurosciences, Institute of Health Sciences, 37508Dokuz Eylül University, Izmir, Turkey.,Department of Neurology, 37508Dokuz Eylül University Medical School, Izmir, Turkey.,Brain Dynamics Multidisciplinary Research Center, 37508Dokuz Eylül University, Izmir, Turkey
| | - Görsev Yener
- Brain Dynamics Multidisciplinary Research Center, 37508Dokuz Eylül University, Izmir, Turkey.,Faculty of Medicine, 605730Izmir University of Economics, Izmir, Turkey.,Izmir Biomedicine and Genome Center, Izmir, Turkey
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4
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Mitsukura Y, Sumali B, Watanabe H, Ikaga T, Nishimura T. Frontotemporal EEG as potential biomarker for early MCI: a case-control study. BMC Psychiatry 2022; 22:289. [PMID: 35459119 PMCID: PMC9027034 DOI: 10.1186/s12888-022-03932-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 04/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Previous studies using EEG (electroencephalography) as biomarker for dementia have attempted to research, but results have been inconsistent. Most of the studies have extremely small number of samples (average N = 15) and studies with large number of data do not have control group. We identified EEG features that may be biomarkers for dementia with 120 subjects (dementia 10, MCI 33, against control 77). METHODS We recorded EEG from 120 patients with dementia as they stayed in relaxed state using a single-channel EEG device while conducting real-time noise reduction and compared them to healthy subjects. Differences in EEG between patients and controls, as well as differences in patients' severity, were examined using the ratio of power spectrum at each frequency. RESULTS In comparing healthy controls and dementia patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. CONCLUSIONS A study with a larger sample size should be conducted to gauge reproducibility, but the results implied the effectiveness of EEG in clinical practice as a biomarker of MCI (mild cognitive impairment) and/or dementia.
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Affiliation(s)
- Yasue Mitsukura
- Department of System Design Engineering, School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa, Japan.
| | - Brian Sumali
- grid.26091.3c0000 0004 1936 9959Keio Global Institute(KGRI), Keio University, Tokyo, Japan
| | - Hideto Watanabe
- grid.26091.3c0000 0004 1936 9959Department of System Design Engineering, School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa Japan
| | - Toshiharu Ikaga
- grid.26091.3c0000 0004 1936 9959Department of System Design Engineering, School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Kanagawa Japan
| | - Toshihiko Nishimura
- grid.168010.e0000000419368956Department of Anesthesia, School of Medicine, Stanford University, Stanford, CA USA
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5
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Chen YS, Shu K, Kang HC. Deep Brain Stimulation in Alzheimer's Disease: Targeting the Nucleus Basalis of Meynert. J Alzheimers Dis 2021; 80:53-70. [PMID: 33492288 DOI: 10.3233/jad-201141] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Alzheimer's disease (AD) is becoming a prevalent disease in the elderly population. Past decades have witnessed the development of drug therapies with varying targets. However, all drugs with a single molecular target fail to reverse or ameliorate AD progression, which ultimately results in cortical and subcortical network dysregulation. Deep brain stimulation (DBS) has been proven effective for the treatment of Parkinson's disease, essential tremor, and other neurological diseases. As such, DBS has also been gradually acknowledged as a potential therapy for AD. The current review focuses on DBS of the nucleus basalis of Meynert (NBM). As a critical component of the cerebral cholinergic system and the Papez circuit in the basal ganglia, the NBM plays an indispensable role in the subcortical regulation of memory, attention, and arousal state, which makes the NBM a promising target for modulation of neural network dysfunction and AD treatment. We summarized the intricate projection relations and functionality of the NBM, current approaches for stereotactic localization and evaluation of the NBM, and the therapeutic effects of NBM-DBS both in patients and animal models. Furthermore, the current shortcomings of NBM-DBS, such as variations in cortical blood flow, increased temperature in the target area, and stimulation-related neural damage, were presented.
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Affiliation(s)
- Yu-Si Chen
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Kai Shu
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hui-Cong Kang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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6
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Han SH, Pyun JM, Yeo S, Kang DW, Jeong HT, Kang SW, Kim S, Youn YC. Differences between memory encoding and retrieval failure in mild cognitive impairment: results from quantitative electroencephalography and magnetic resonance volumetry. Alzheimers Res Ther 2021; 13:3. [PMID: 33397486 PMCID: PMC7784298 DOI: 10.1186/s13195-020-00739-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 12/04/2020] [Indexed: 02/17/2023]
Abstract
BACKGROUND The memory impairments in mild cognitive impairment (MCI) can be classified into encoding (EF) and retrieval (RF) failure, which can be affected by underlying pathomechanism. We explored the differences structurally and functionally. METHODS We compared quantitative electroencephalography (qEEG) power spectra and connectivity between 87 MCI patients with EF and 78 MCI with RF using iSyncBrain® (iMediSync Inc., Republic of Korea) ( https://isyncbrain.com/ ). Voxel-based morphometric analysis of the gray matter (GM) in the MCI groups and 71 cognitive normal controls was also done using the Computational Anatomy Toolbox 12 ( http://www.neuro.uni-jena.de/cat/ ). RESULTS qEEG showed higher frontal theta and lower beta2 band power, and higher theta connectivity in the EF. There was no statistically significant difference in GM volume between the EF and RF. However, when compared to normal control, GM volume reductions due to EF in the left thalamus and bilateral hippocampi and reductions due to RF in the left thalamus, right superior frontal lobe, right superior temporal lobe, and right middle cingulum were observed (p < 0.05, family-wise error correction). CONCLUSIONS MCI differs functionally and structurally according to their specific memory impairments. The EF findings are structurally and functionally more consistent with the prodromal Alzheimer's disease stage than the RF findings. Since this study is a cross-sectional study, prospective follow-up studies are needed to investigate whether different types of memory impairments can predict the underlying pathology of amnestic MCI. Additionally, insufficient sample size may lead to ambiguous statistical findings in direct comparisons, and a larger patient cohort could more robustly identify differences in GM volume reductions between the EF and the RF group.
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Affiliation(s)
- Su-Hyun Han
- Department of Neurology, Chung-Ang University College of Medicine, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea
| | - Jung-Min Pyun
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Soeun Yeo
- Department of Neurology, Chung-Ang University College of Medicine, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea
| | | | - Ho Tae Jeong
- Department of Neurology, Chung-Ang University College of Medicine, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea
| | - Seung Wan Kang
- iMediSync Inc., Seoul, Republic of Korea.
- Data Center for Korean EEG, College of Nursing, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - SangYun Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea.
- Department of Medical Informatics, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
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7
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Jeong HT, Youn YC, Sung HH, Kim SY. Power Spectral Changes of Quantitative EEG in the Subjective Cognitive Decline: Comparison of Community Normal Control Groups. Neuropsychiatr Dis Treat 2021; 17:2783-2790. [PMID: 34465994 PMCID: PMC8403030 DOI: 10.2147/ndt.s320130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/06/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE The purpose of this study is to compare and analyze the power spectral changes between subjective cognitive decline (SCD) subjects and normal controls (NC) while checking the preclinical stage of AD in the SCD subjects and to use the derived data for biomarker research that can diagnose early-stage AD in the future. METHODS We recruited 23 SCD patients and 23 normal control subjects and QEEG analysis including power spectral density (PSD) and source-level analysis were performed. An automated preprocessing procedure and statistical analysis were performed by iSync Brain® (iMediSync Inc., Republic of Korea) (https://isyncbrain.com/) using the international standard 10-20 system (19 electrodes). RESULTS Absolute PSD, there was no statistically significant difference in all of the EEG power measurements of the 19 channels. In the relative PSD analysis, the average delta band power of the SCD group was significantly higher in Fp2, F4, and F8 than NC. Alpha1 band power of the O1 channel was 22.56±16.05 for the SCD group and 33.19±19.05 for the NC (p-value <0.05). Source-level analysis did not show a statistically significant difference. CONCLUSION SCD subjects showed a partial increase of delta waves in the frontal lobe region and a partial decrease in alpha1, a fast wave in the occipital region, compared to the NC. SCD is considered one of the earliest clinical symptoms of AD and it is predicted to be related to minor nerve damage. We were able to observe the power spectral changes in SCD subjects in this cross-sectional study, a large number of subjects and longitudinal studies are needed to evaluate their predictability for future deterioration such as conversion to MCI.
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Affiliation(s)
- Ho Tae Jeong
- Department of Neurology, Chung-Ang University of College of Medicine, Seoul, Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University of College of Medicine, Seoul, Korea
| | - Hyun-Ho Sung
- Department of Clinical Laboratory Science, Dongnam Health University, Suwon, Korea
| | - Sang Yun Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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8
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Abstract
Currently established and employed biomarkers of Alzheimer's disease (AD) predominantly mirror AD-associated molecular and structural brain changes. While they are necessary for identifying disease-specific neuropathology, they lack a clear and robust relationship with the clinical presentation of dementia; they can be altered in healthy individuals, while they often inadequately mirror the degree of cognitive and functional deficits in affected subjects. There is growing evidence that synaptic loss and dysfunction are early events during the trajectory of AD pathogenesis that best correlate with the clinical symptoms, suggesting measures of brain functional deficits as candidate early markers of AD. Resting-state electroencephalography (EEG) is a widely available and noninvasive diagnostic method that provides direct insight into brain synaptic activity in real time. Quantitative EEG (qEEG) analysis additionally provides information on physiologically meaningful frequency components, dynamic alterations and topography of EEG signal generators, i.e. neuronal signaling. Numerous studies have shown that qEEG measures can detect disruptions in activity, topographical distribution and synchronization of neuronal (synaptic) activity such as generalized EEG slowing, reduced global synchronization and anteriorization of neuronal generators of fast-frequency resting-state EEG activity in patients along the AD continuum. Moreover, qEEG measures appear to correlate well with surrogate markers of AD neuropathology and discriminate between different types of dementia, making them promising low-cost and noninvasive markers of AD. Future large-scale longitudinal clinical studies are needed to elucidate the diagnostic and prognostic potential of qEEG measures as early functional markers of AD on an individual subject level.
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Affiliation(s)
- Una Smailovic
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden.
| | - Vesna Jelic
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital, Huddinge, Sweden
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9
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Menardi A, Pascual-Leone A, Fried PJ, Santarnecchi E. The Role of Cognitive Reserve in Alzheimer's Disease and Aging: A Multi-Modal Imaging Review. J Alzheimers Dis 2019; 66:1341-1362. [PMID: 30507572 DOI: 10.3233/jad-180549] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Comforts in modern society have generally been associated with longer survival rates, enabling individuals to reach advanced age as never before in history. With the increase in longevity, however, the incidence of neurodegenerative diseases, especially Alzheimer's disease, has also doubled. Nevertheless, most of the observed variance, in terms of time of clinical diagnosis and progression, often remains striking. Only recently, differences in the social, educational and occupational background of the individual, as proxies of cognitive reserve (CR), have been hypothesized to play a role in accounting for such discrepancies. CR is a well-established concept in literature; lots of studies have been conducted in trying to better understand its underlying neural substrates and associated biomarkers, resulting in an incredible amount of data being produced. Here, we aimed to summarize recent relevant published work addressing the issue, gathering evidence for the existence of a common path across research efforts that might ease future investigations by providing a general perspective on the actual state of the arts. An innovative model is hereby proposed, addressing the role of CR across structural and functional evidences, as well as the potential implementation of non-invasive brain stimulation techniques in the causal validation of such theoretical frame.
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Affiliation(s)
- Arianna Menardi
- Brain Investigation and Neuromodulation Lab, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Italy.,Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Peter J Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Emiliano Santarnecchi
- Brain Investigation and Neuromodulation Lab, Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Italy.,Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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10
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Cassani R, Falk TH. Alzheimer's Disease Diagnosis and Severity Level Detection Based on Electroencephalography Modulation Spectral "Patch" Features. IEEE J Biomed Health Inform 2019; 24:1982-1993. [PMID: 31725401 DOI: 10.1109/jbhi.2019.2953475] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Over the last two decades, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). Typically, resting-state EEG (rsEEG) signals have been used, and traditional frequency bands (delta, theta, alpha, beta and gamma) have been explored. Recent studies, however, have suggested that non-conventional bands may lead to improved diagnostic performance. In this work, we propose a new type of features derived from the 2-dimensional modulation spectral domain representation of the rsEEG signal in order to characterize the neuromodulatory deficit emergent with AD. The proposed features are computed as the power in specific "patches" or regions of interest in the power modulation spectrogram, which are shown to be highly discriminant of AD severity levels. The proposed features were compared with traditional features used in the rsEEG AD monitoring literature. Results showed the proposed features not only achieving improved performance at discriminating between healthy normal elderly controls (Nold) and AD patients with varying severity levels, but also at monitoring severity levels (i.e., mild AD versus moderate AD). Moreover, the proposed features were shown to outperform traditional rsEEG features. Finally, we validated the biological origin of the proposed features by using source localization and comparing the obtained results with ones reported in the AD literature.
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11
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Cassani R, Estarellas M, San-Martin R, Fraga FJ, Falk TH. Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment. DISEASE MARKERS 2018; 2018:5174815. [PMID: 30405860 PMCID: PMC6200063 DOI: 10.1155/2018/5174815] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 07/12/2018] [Accepted: 07/29/2018] [Indexed: 12/17/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.
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Affiliation(s)
- Raymundo Cassani
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
| | - Mar Estarellas
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
- Department of Bioengineering, Imperial College London, London, UK
| | - Rodrigo San-Martin
- Center for Mathematics, Computation and Cognition, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Francisco J. Fraga
- Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Tiago H. Falk
- Institut national de la recherche scientifique (INRS-EMT), University of Québec, Montreal, Canada
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12
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Mariani S, Tarokh L, Djonlagic I, Cade BE, Morrical MG, Yaffe K, Stone KL, Loparo KA, Purcell SM, Redline S, Aeschbach D. Evaluation of an automated pipeline for large-scale EEG spectral analysis: the National Sleep Research Resource. Sleep Med 2018; 47:126-136. [PMID: 29803181 PMCID: PMC5976521 DOI: 10.1016/j.sleep.2017.11.1128] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Revised: 11/15/2017] [Accepted: 11/20/2017] [Indexed: 11/18/2022]
Abstract
STUDY OBJECTIVES We present an automated sleep electroencephalogram (EEG) spectral analysis pipeline that includes an automated artifact detection step, and we test the hypothesis that spectral power density estimates computed with this pipeline are comparable to those computed with a commercial method preceded by visual artifact detection by a sleep expert (standard approach). METHODS EEG data were analyzed from the C3-A2 lead in a sample of polysomnograms from 161 older women participants in a community-based cohort study. We calculated the sensitivity, specificity, accuracy, and Cohen's kappa measures from epoch-by-epoch comparisons of automated to visual-based artifact detection results; then we computed the average EEG spectral power densities in six commonly used EEG frequency bands and compared results from the two methods using correlation analysis and Bland-Altman plots. RESULTS Assessment of automated artifact detection showed high specificity [96.8%-99.4% in non-rapid eye movement (NREM), 96.9%-99.1% in rapid eye movement (REM) sleep] but low sensitivity (26.7%-38.1% in NREM, 9.1-27.4% in REM sleep). However, large artifacts (total power > 99th percentile) were removed with sensitivity up to 87.7% in NREM and 90.9% in REM, with specificities of 96.9% and 96.6%, respectively. Mean power densities computed with the two approaches for all EEG frequency bands showed very high correlation (≥0.99). The automated pipeline allowed for a 100-fold reduction in analysis time with regard to the standard approach. CONCLUSION Despite low sensitivity for artifact rejection, the automated pipeline generated results comparable to those obtained with a standard method that included manual artifact detection. Automated pipelines can enable practical analyses of recordings from thousands of individuals, allowing for use in genetics and epidemiological research requiring large samples.
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Affiliation(s)
- Sara Mariani
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
| | - Leila Tarokh
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Chronobiology and Sleep Research, Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Ina Djonlagic
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA; Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Michael G Morrical
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Kristine Yaffe
- University of California, San Francisco School of Medicine, San Francisco, CA, USA
| | - Katie L Stone
- Research Institute, California Pacific Medical Center, Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | | | - Shaun M Purcell
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA; Case Western Reserve University, Cleveland, OH, USA
| | - Daniel Aeschbach
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA; Division of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany
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13
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Abstract
Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.
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14
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Nimmy John T, D Puthankattil S, Menon R. Analysis of long range dependence in the EEG signals of Alzheimer patients. Cogn Neurodyn 2018; 12:183-199. [PMID: 29564027 DOI: 10.1007/s11571-017-9467-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 11/14/2017] [Accepted: 12/19/2017] [Indexed: 11/28/2022] Open
Abstract
Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.
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Affiliation(s)
- T Nimmy John
- 1Department of Electrical Engineering, National Institute of Technology Calicut, Kozhikode, India
| | - Subha D Puthankattil
- 1Department of Electrical Engineering, National Institute of Technology Calicut, Kozhikode, India
| | - Ramshekhar Menon
- 2Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India
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Tian Y, Yang L, Xu W, Zhang H, Wang Z, Zhang H, Zheng S, Shi Y, Xu P. Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics. Eur J Pharm Sci 2017; 110:26-36. [PMID: 28456573 DOI: 10.1016/j.ejps.2017.04.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 04/24/2017] [Accepted: 04/24/2017] [Indexed: 01/21/2023]
Abstract
Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.
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Affiliation(s)
- Yin Tian
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China.
| | - Li Yang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Wei Xu
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Huiling Zhang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Zhongyan Wang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Haiyong Zhang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Shuxing Zheng
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Yupan Shi
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Peng Xu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Ianof JN, Fraga FJ, Ferreira LA, Ramos RT, Demario JLC, Baratho R, Basile LFH, Nitrini R, Anghinah R. Comparative analysis of the electroencephalogram in patients with Alzheimer's disease, diffuse axonal injury patients and healthy controls using LORETA analysis. Dement Neuropsychol 2017; 11:176-185. [PMID: 29213509 PMCID: PMC5710686 DOI: 10.1590/1980-57642016dn11-020010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 05/24/2017] [Indexed: 12/23/2022] Open
Abstract
Alzheimer's disease (AD) is a dementia that affects a large contingent of the elderly population characterized by the presence of neurofibrillary tangles and senile plaques. Traumatic brain injury (TBI) is a non-degenerative injury caused by an external mechanical force. One of the main causes of TBI is diffuse axonal injury (DAI), promoted by acceleration-deceleration mechanisms. OBJECTIVE To understand the electroencephalographic differences in functional mechanisms between AD and DAI groups. METHODS The study included 20 subjects with AD, 19 with DAI and 17 healthy adults submitted to high resolution EEG with 128 channels. Cortical sources of EEG rhythms were estimated by exact low-resolution electromagnetic tomography (eLORETA) analysis. RESULTS The eLORETA analysis showed that, in comparison to the control (CTL) group, the AD group had increased theta activity in the parietal and frontal lobes and decreased alpha 2 activity in the parietal, frontal, limbic and occipital lobes. In comparison to the CTL group, the DAI group had increased theta activity in the limbic, occipital sublobar and temporal areas. CONCLUSION The results suggest that individuals with AD and DAI have impairment of electrical activity in areas important for memory and learning.
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Affiliation(s)
- Jéssica Natuline Ianof
- Neurology Department, University of São Paulo Medical School
Hospital (FMUSP-HC), São Paulo, SP, Brazil
| | - Francisco José Fraga
- Engineering, Modeling and Applied Social Sciences Center (CECS) -
Federal University of ABC (UFABC), São Paulo, SP, Brazil
| | - Leonardo Alves Ferreira
- Engineering, Modeling and Applied Social Sciences Center (CECS) -
Federal University of ABC (UFABC), São Paulo, SP, Brazil
| | | | - José Luiz Carlos Demario
- Department of Actuarial and Quantitative Methods - Pontifical
Catholic of São Paulo, São Paulo, SP, Brazil
| | - Regina Baratho
- Department of Actuarial and Quantitative Methods - Pontifical
Catholic of São Paulo, São Paulo, SP, Brazil
| | - Luís Fernando Hindi Basile
- Neurology Department, University of São Paulo Medical School
Hospital (FMUSP-HC), São Paulo, SP, Brazil
- Laboratory of Psychophysiology - Methodist University of São
Paulo, São Paulo, SP, Brazil
| | - Ricardo Nitrini
- Neurology Department, University of São Paulo Medical School
Hospital (FMUSP-HC), São Paulo, SP, Brazil
| | - Renato Anghinah
- Neurology Department, University of São Paulo Medical School
Hospital (FMUSP-HC), São Paulo, SP, Brazil
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Neto E, Biessmann F, Aurlien H, Nordby H, Eichele T. Regularized Linear Discriminant Analysis of EEG Features in Dementia Patients. Front Aging Neurosci 2016; 8:273. [PMID: 27965568 PMCID: PMC5127828 DOI: 10.3389/fnagi.2016.00273] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Accepted: 10/31/2016] [Indexed: 10/24/2022] Open
Abstract
The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer's disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10-fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD, and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features, we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC = 0.84). We also obtained AUC = 0.74 for discrimination of AD from HC, AUC = 0.77 for discrimination of VaD from HC, and finally AUC = 0.61 for discrimination of AD from VaD. Our models were able to separate HC from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia.
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Affiliation(s)
- Emanuel Neto
- Section for Clinical Neurophysiology, Haukeland University HospitalBergen, Norway; Institute of Biological and Medical Psychology, University of BergenBergen, Norway
| | | | - Harald Aurlien
- Section for Clinical Neurophysiology, Haukeland University Hospital Bergen, Norway
| | - Helge Nordby
- Institute of Biological and Medical Psychology, University of Bergen Bergen, Norway
| | - Tom Eichele
- Section for Clinical Neurophysiology, Haukeland University HospitalBergen, Norway; Institute of Biological and Medical Psychology, University of BergenBergen, Norway; K.G. Jebsen Center for Neuropsychiatric DisordersBergen, Norway
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18
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Holston EC. The Electrophysiological Phenomenon of Alzheimer's Disease: A Psychopathology Theory. Issues Ment Health Nurs 2015; 36:603-13. [PMID: 26379134 DOI: 10.3109/01612840.2015.1015696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The current understanding of Alzheimer's disease (AD) is based on the Aβ and tau pathology and the resulting neuropathological changes, which are associated with manifested clinical symptoms. However, electrophysiological brain changes may provide a more expansive understanding of AD. Hence, the objective of this systematic review is to propose a theory about the electrophysiological phenomenon of Alzheimer's disease (EPAD). The review of literature resulted from an extensive search of PubMed and MEDLINE databases. One-hundred articles were purposively selected. They provided an understanding of the concepts establishing the theory of EPAD (neuropathological changes, neurochemical changes, metabolic changes, and electrophysiological brain changes). Changes in the electrophysiology of the brain are foundational to the association or interaction of the concepts. Building on Berger's Psychophysical Model, it is evident that electrophysiological brain changes occur and affect cortical areas to generate or manifest symptoms from onset and across the stages of AD, which may be prior to pathological changes. Therefore, the interaction of the concepts demonstrates how the psychopathology results from affected electrophysiology of the brain. The theory of the EPAD provides a theoretical foundation for appropriate measurements of AD without dependence on neuropathological changes. Future research is warranted to further test this theory. Ultimately, this theory contributes to existing knowledge because it shows how electrophysiological changes are useful in understanding the risk and progression of AD across the stages.
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Affiliation(s)
- Ezra C Holston
- a University of Tennessee-Knoxville , College of Nursing , Knoxville , Tennessee , USA
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19
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Neto E, Allen EA, Aurlien H, Nordby H, Eichele T. EEG Spectral Features Discriminate between Alzheimer's and Vascular Dementia. Front Neurol 2015; 6:25. [PMID: 25762978 PMCID: PMC4327579 DOI: 10.3389/fneur.2015.00025] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2014] [Accepted: 01/29/2015] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.
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Affiliation(s)
- Emanuel Neto
- Institute of Biological and Medical Psychology, University of Bergen , Bergen , Norway ; Section for Clinical Neurophysiology, Haukeland University Hospital , Bergen , Norway
| | - Elena A Allen
- Institute of Biological and Medical Psychology, University of Bergen , Bergen , Norway ; K. G. Jebsen Center for Research on Neuropsychiatric Disorders , Bergen , Norway ; The Mind Research Network , Albuquerque, NM , USA
| | - Harald Aurlien
- Section for Clinical Neurophysiology, Haukeland University Hospital , Bergen , Norway
| | - Helge Nordby
- Institute of Biological and Medical Psychology, University of Bergen , Bergen , Norway
| | - Tom Eichele
- Institute of Biological and Medical Psychology, University of Bergen , Bergen , Norway ; Section for Clinical Neurophysiology, Haukeland University Hospital , Bergen , Norway ; K. G. Jebsen Center for Research on Neuropsychiatric Disorders , Bergen , Norway
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