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Pascarella A, Manzo L, Ferlazzo E. Modern neurophysiological techniques indexing normal or abnormal brain aging. Seizure 2024:S1059-1311(24)00194-8. [PMID: 38972778 DOI: 10.1016/j.seizure.2024.07.001] [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: 04/09/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024] Open
Abstract
Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even in the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations in neurogenesis, neuronal senescence and synaptic plasticity are implicated. Recent years have seen advancements in neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) and transcranial magnetic stimulation (TMS), offering insights into physiological and pathological brain aging. These methods provide real-time information on brain activity, connectivity and network dynamics. Integration of Artificial Intelligence (AI) techniques promise as a tool enhancing the diagnosis and prognosis of age-related cognitive decline. Our review highlights recent advances in these electrophysiological techniques (focusing on EEG, ERP, TMS and TMS-EEG methodologies) and their application in physiological and pathological brain aging. Physiological aging is characterized by changes in EEG spectral power and connectivity, ERP and TMS parameters, indicating alterations in neural activity and network function. Pathological aging, such as in Alzheimer's disease, is associated with further disruptions in EEG rhythms, ERP components and TMS measures, reflecting underlying neurodegenerative processes. Machine learning approaches show promise in classifying cognitive impairment and predicting disease progression. Standardization of neurophysiological methods and integration with other modalities are crucial for a comprehensive understanding of brain aging and neurodegenerative disorders. Advanced network analysis techniques and AI methods hold potential for enhancing diagnostic accuracy and deepening insights into age-related brain changes.
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Affiliation(s)
- Angelo Pascarella
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy.
| | - Lucia Manzo
- Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy
| | - Edoardo Ferlazzo
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy
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2
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Torres-Simon L, Cuesta P, del Cerro-Leon A, Chino B, Orozco LH, Marsh EB, Gil P, Maestu F. The effects of white matter hyperintensities on MEG power spectra in population with mild cognitive impairment. Front Hum Neurosci 2023; 17:1068216. [PMID: 36875239 PMCID: PMC9977191 DOI: 10.3389/fnhum.2023.1068216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Cerebrovascular disease is responsible for up to 20% of cases of dementia worldwide, but also it is a major comorbid contributor to the progression of other neurodegenerative diseases, like Alzheimer's disease. White matter hyperintensities (WMH) are the most prevalent imaging marker in cerebrovascular disease. The presence and progression of WMH in the brain have been associated with general cognitive impairment and the risk to develop all types of dementia. The aim of this piece of work is the assessment of brain functional differences in an MCI population based on the WMH volume. One-hundred and twenty-nine individuals with mild cognitive impairment (MCI) underwent a neuropsychological evaluation, MRI assessment (T1 and Flair), and MEG recordings (5 min of eyes closed resting state). Those participants were further classified into vascular MCI (vMCI; n = 61, mean age 75 ± 4 years, 35 females) or non-vascular MCI (nvMCI; n = 56, mean age 72 ± 5 years, 36 females) according to their WMH total volume, assessed with an automatic detection toolbox, LST (SPM12). We used a completely data-driven approach to evaluate the differences in the power spectra between the groups. Interestingly, three clusters emerged: One cluster with widespread larger theta power and two clusters located in both temporal regions with smaller beta power for vMCI compared to nvMCI. Those power signatures were also associated with cognitive performance and hippocampal volume. Early identification and classification of dementia pathogenesis is a crucially important goal for the search for more effective management approaches. These findings could help to understand and try to palliate the contribution of WMH to particular symptoms in mixed dementia progress.
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Affiliation(s)
- Lucia Torres-Simon
- Center of Cognitive and Computational Neuroscience, Universidad Complutense de Madrid (UCM), Madrid, Spain
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid (UCM), Madrid, Spain
| | - Pablo Cuesta
- Center of Cognitive and Computational Neuroscience, Universidad Complutense de Madrid (UCM), Madrid, Spain
- Department of Radiology, Rehabilitation, and Physiotherapy, Universidad Complutense de Madrid (UCM), Madrid, Spain
- Instituto de investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
| | - Alberto del Cerro-Leon
- Center of Cognitive and Computational Neuroscience, Universidad Complutense de Madrid (UCM), Madrid, Spain
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid (UCM), Madrid, Spain
| | - Brenda Chino
- Center of Cognitive and Computational Neuroscience, Universidad Complutense de Madrid (UCM), Madrid, Spain
- Institute of Neuroscience, Autonomous University of Barcelona (UAB), Barcelona, Spain
| | - Lucia H. Orozco
- Center of Cognitive and Computational Neuroscience, Universidad Complutense de Madrid (UCM), Madrid, Spain
| | - Elisabeth B. Marsh
- Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Pedro Gil
- Instituto de investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
- Department of Geriatric Medicine, Hospital Universitario San Carlos, Madrid, Spain
| | - Fernando Maestu
- Center of Cognitive and Computational Neuroscience, Universidad Complutense de Madrid (UCM), Madrid, Spain
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid (UCM), Madrid, Spain
- Instituto de investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
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3
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Giustiniani A, Danesin L, Bozzetto B, Macina A, Benavides-Varela S, Burgio F. Functional changes in brain oscillations in dementia: a review. Rev Neurosci 2023; 34:25-47. [PMID: 35724724 DOI: 10.1515/revneuro-2022-0010] [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: 02/01/2022] [Accepted: 05/16/2022] [Indexed: 01/11/2023]
Abstract
A growing body of evidence indicates that several characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) play a functional role in cognition and could be linked to the progression of cognitive decline in some neurological diseases such as dementia. The present paper reviews previous studies investigating changes in brain oscillations associated to the most common types of dementia, namely Alzheimer's disease (AD), frontotemporal degeneration (FTD), and vascular dementia (VaD), with the aim of identifying pathology-specific patterns of alterations and supporting differential diagnosis in clinical practice. The included studies analysed changes in frequency power, functional connectivity, and event-related potentials, as well as the relationship between electrophysiological changes and cognitive deficits. Current evidence suggests that an increase in slow wave activity (i.e., theta and delta) as well as a general reduction in the power of faster frequency bands (i.e., alpha and beta) characterizes AD, VaD, and FTD. Additionally, compared to healthy controls, AD exhibits alteration in latencies and amplitudes of the most common event related potentials. In the reviewed studies, these changes generally correlate with performances in many cognitive tests. In conclusion, particularly in AD, neurophysiological changes can be reliable early markers of dementia.
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Affiliation(s)
| | - Laura Danesin
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
| | | | - AnnaRita Macina
- Department of Developmental Psychology and Socialization, University of Padua, via Venezia 8, 35131 Padova, Italy
| | - Silvia Benavides-Varela
- Department of Developmental Psychology and Socialization, University of Padua, via Venezia 8, 35131 Padova, Italy.,Department of Neuroscience, University of Padova, 35128 Padova, Italy.,Padova Neuroscience Center, University of Padova, 35131 Padova, Italy
| | - Francesca Burgio
- IRCCS San Camillo Hospital, via Alberoni 70, 30126 Venice, Italy
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4
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Salvadori E, Brambilla M, Maestri G, Nicotra A, Cova I, Pomati S, Pantoni L. The clinical profile of cerebral small vessel disease: Toward an evidence-based identification of cognitive markers. Alzheimers Dement 2023; 19:244-260. [PMID: 35362229 PMCID: PMC10084195 DOI: 10.1002/alz.12650] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 01/21/2022] [Accepted: 02/14/2022] [Indexed: 01/18/2023]
Abstract
There is no consensus on which test is more suited to outline the cognitive deficits of cerebral small vessel disease (cSVD) patients. We explored the ability of eight cognitive tests, selected in a previous systematic review as the most commonly used in this population, to differentiate among cSVD patients, controls, and other dementing conditions performing a meta-analysis of 86 studies. We found that cSVD patients performed worse than healthy controls in all tests while data on the comparison to neurodegenerative diseases were limited. We outlined a lack of data on these tests' accuracy on the diagnosis. Cognitive tests measuring processing speed were those mostly associated with neuroimaging cSVD markers. There is currently incomplete evidence that a single test could differentiate cSVD patients with cognitive decline from other dementing diseases. We make preliminary proposals on possible strategies to gain information about the clinical definition of cSVD that currently remains a neuroimaging-based one.
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Affiliation(s)
| | | | - Giorgia Maestri
- Neurology Unit, Luigi Sacco University Hospital, Milan, Italy
| | - Alessia Nicotra
- Neurology Unit, Luigi Sacco University Hospital, Milan, Italy
| | - Ilaria Cova
- Neurology Unit, Luigi Sacco University Hospital, Milan, Italy
| | - Simone Pomati
- Neurology Unit, Luigi Sacco University Hospital, Milan, Italy
| | - Leonardo Pantoni
- "Luigi Sacco" Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy.,Stroke and Dementia Lab, 'Luigi Sacco' Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
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5
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Buján A, Sampaio A, Pinal D. Resting-state electroencephalographic correlates of cognitive reserve: Moderating the age-related worsening in cognitive function. Front Aging Neurosci 2022; 14:854928. [PMID: 36185469 PMCID: PMC9521492 DOI: 10.3389/fnagi.2022.854928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
This exploratory study aimed to investigate the resting-state electroencephalographic (rsEEG) correlates of the cognitive reserve from a life span perspective. Current source density (CSD) and lagged-linear connectivity (LLC) measures were assessed to this aim. We firstly explored the relationship between rsEEG measures for the different frequency bands and a socio-behavioral proxy of cognitive reserve, the Cognitive Reserve Index (CRI). Secondly, we applied moderation analyses to assess whether any of the correlated rsEEG measures showed a moderating role in the relationship between age and cognitive function. Moderate negative correlations were found between the CRI and occipital CSD of delta and beta 2. Moreover, inter- and intrahemispheric LLC measures were correlated with the CRI, showing a negative association with delta and positive associations with alpha 1, beta 1, and beta 2. Among those correlated measures, just two rsEEG variables were significant moderators of the relationship between age and cognition: occipital delta CSD and right hemispheric beta 2 LLC between occipital and limbic regions. The effect of age on cognitive performance was stronger for higher values of both measures. Therefore, lower values of occipital delta CSD and lower beta 2 LLC between right occipital and limbic regions might protect or compensate for the effects of age on cognition. Results of this exploratory study might be helpful to allocate more preventive efforts to curb the progression of cognitive decline in adults with less CR, possibly characterized by these rsEEG parameters at a neural level. However, given the exploratory nature of this study, more conclusive work on these rsEEG measures is needed to firmly establish their role in the cognition–age relationship, for example, verifying if these measures moderate the relationship between brain structure and cognition.
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6
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Torres-Simón L, Doval S, Nebreda A, Llinas SJ, Marsh EB, Maestú F. Understanding brain function in vascular cognitive impairment and dementia with EEG and MEG: A systematic review. Neuroimage Clin 2022; 35:103040. [PMID: 35653914 PMCID: PMC9163840 DOI: 10.1016/j.nicl.2022.103040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 04/09/2022] [Accepted: 05/06/2022] [Indexed: 11/22/2022]
Abstract
Vascular Cognitive Impairment (VCI) is the second most prevalent dementia worldwide. Cerebrovascular disease is a major comorbid contributor to neurodegenerative diseases. VCI patients show specific spectral, connectivity and evoked responses patterns. Literature suggests that EEG-MEG might provide promising biomarkers for early VCI. Further neurophysiological research is needed for VCI subtypes differentiation.
Vascular Cognitive Impairment (VCI) is the second most prevalent dementia after Alzheimer’s Disease (AD), and cerebrovascular disease (CBVD) is a major comorbid contributor to the progression of most neurodegenerative diseases. Early differentiation of cognitive impairment is critical given both the high prevalence of CBVD, and that its risk factors are modifiable. The ability for electroencephalogram (EEG) and magnetoencephalogram (MEG) to detect changes in brain functioning for other dementias suggests that they may also be promising biomarkers for early VCI. The present systematic review aims to summarize the literature regarding electrophysiological patterns of mild and major VCI. Despite considerable heterogeneity in clinical definition and electrophysiological methodology, common patterns exist when comparing patients with VCI to healthy controls (HC) and patients with AD, though there is a low specificity when comparing between VCI subgroups. Similar to other dementias, slowed frequency patterns and disrupted inter- and intra-hemispheric connectivity are repeatedly reported for VCI patients, as well as longer latencies and smaller amplitudes in evoked responses. Further study is needed to fully establish MEG and EEG as clinically useful biomarkers, including a clear definition of VCI and standardized methodology, allowing for comparison across groups and consolidation of multicenter efforts.
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Affiliation(s)
- Lucía Torres-Simón
- Center of Cognitive and Computational Neuroscience; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain.
| | - Sandra Doval
- Center of Cognitive and Computational Neuroscience; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Alberto Nebreda
- Center of Cognitive and Computational Neuroscience; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Sophia J Llinas
- Department of Neurology, the Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Elisabeth B Marsh
- Department of Neurology, the Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Fernando Maestú
- Center of Cognitive and Computational Neuroscience; Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
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7
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Kumral D, Cesnaite E, Beyer F, Hofmann SM, Hensch T, Sander C, Hegerl U, Haufe S, Villringer A, Witte AV, Nikulin VV. Relationship between regional white matter hyperintensities and alpha oscillations in older adults. Neurobiol Aging 2021; 112:1-11. [PMID: 35007997 DOI: 10.1016/j.neurobiolaging.2021.10.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 09/22/2021] [Accepted: 10/12/2021] [Indexed: 11/17/2022]
Abstract
Aging is associated with increased white matter hyperintensities (WMHs) and with alterations of alpha oscillations (7-13 Hz). However, a crucial question remains, whether changes in alpha oscillations relate to aging per se or whether this relationship is mediated by age-related neuropathology like WMHs. Using a large cohort of cognitively healthy older adults (N = 907, 60-80 years), we assessed relative alpha power, alpha peak frequency, and long-range temporal correlations from resting-state EEG. We further associated these parameters with voxel-wise WMHs from 3T MRI. We found that a higher prevalence of WMHs in the superior and posterior corona radiata as well as in the thalamic radiation was related to elevated alpha power, with the strongest association in the bilateral occipital cortex. In contrast, we observed no significant relation of the WMHs probability with alpha peak frequency and long-range temporal correlations. Finally, higher age was associated with elevated alpha power via total WMH volume. We suggest that an elevated alpha power is a consequence of WMHs affecting a spatial organization of alpha sources.
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Affiliation(s)
- Deniz Kumral
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Psychology, Neuropsychology, University of Freiburg, Freiburg im Breisgau, Germany; Clinical Psychology and Psychotherapy Unit, Institute of Psychology, University of Freiburg, Freiburg im Breisgau, Germany.
| | - Elena Cesnaite
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Frauke Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; CRC Obesity Mechanisms, Subproject A1, University of Leipzig, Leipzig, Germany
| | - Simon M Hofmann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tilman Hensch
- Department of Psychiatry and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany; LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany; Department of Psychology, IU International University of Applied Sciences, Erfurt, Germany
| | - Christian Sander
- Department of Psychiatry and Psychotherapy, University of Leipzig Medical Center, Leipzig, Germany; LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Ulrich Hegerl
- Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe University Frankfurt, Frankfurt, Germany
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Clinic for Cognitive Neurology, University Medical Center Leipzig, Germany
| | - A Veronica Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; CRC Obesity Mechanisms, Subproject A1, University of Leipzig, Leipzig, Germany; Clinic for Cognitive Neurology, University Medical Center Leipzig, Germany
| | - Vadim V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany; Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russia; Neurophysics Group, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
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8
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Fröhlich S, Kutz DF, Müller K, Voelcker-Rehage C. Characteristics of Resting State EEG Power in 80+-Year-Olds of Different Cognitive Status. Front Aging Neurosci 2021; 13:675689. [PMID: 34456708 PMCID: PMC8387136 DOI: 10.3389/fnagi.2021.675689] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 07/07/2021] [Indexed: 11/28/2022] Open
Abstract
Compared with healthy older adults, patients with Alzheimer's disease show decreased alpha and beta power as well as increased delta and theta power during resting state electroencephalography (rsEEG). Findings for mild cognitive impairment (MCI), a stage of increased risk of conversion to dementia, are less conclusive. Cognitive status of 213 non-demented high-agers (mean age, 82.5 years) was classified according to a neuropsychological screening and a cognitive test battery. RsEEG was measured with eyes closed and open, and absolute power in delta, theta, alpha, and beta bands were calculated for nine regions. Results indicate no rsEEG power differences between healthy individuals and those with MCI. There were also no differences present between groups in EEG reactivity, the change in power from eyes closed to eyes open, or the topographical pattern of each frequency band. Overall, EEG reactivity was preserved in 80+-year-olds without dementia, and topographical patterns were described for each frequency band. The application of rsEEG power as a marker for the early detection of dementia might be less conclusive for high-agers.
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Affiliation(s)
- Stephanie Fröhlich
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Dieter F Kutz
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Katrin Müller
- Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany.,Department of Social Science of Physical Activity and Health, Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Claudia Voelcker-Rehage
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
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9
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Babiloni C, Arakaki X, Bonanni L, Bujan A, Carrillo MC, Del Percio C, Edelmayer RM, Egan G, Elahh FM, Evans A, Ferri R, Frisoni GB, Güntekin B, Hainsworth A, Hampel H, Jelic V, Jeong J, Kim DK, Kramberger M, Kumar S, Lizio R, Nobili F, Noce G, Puce A, Ritter P, Smit DJA, Soricelli A, Teipel S, Tucci F, Sachdev P, Valdes-Sosa M, Valdes-Sosa P, Vergallo A, Yener G. EEG measures for clinical research in major vascular cognitive impairment: recommendations by an expert panel. Neurobiol Aging 2021; 103:78-97. [PMID: 33845399 DOI: 10.1016/j.neurobiolaging.2021.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 02/17/2021] [Accepted: 03/05/2021] [Indexed: 11/22/2022]
Abstract
Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy; San Raffaele Cassino, Cassino, FR, Italy.
| | | | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G D'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Ana Bujan
- Psychological Neuroscience Lab, School of Psychology, University of Minho, Portugal
| | | | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Gary Egan
- Foundation Director of the Monash Biomedical Imaging (MBI) research facilities, Monash University, Clayton, Australia
| | - Fanny M Elahh
- Memory and Aging Center, University of California, San Francisco
| | - Alan Evans
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | | | - Giovanni B Frisoni
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland; Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey; REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab, Istanbul Medipol University, Istanbul, Turkey
| | - Atticus Hainsworth
- University of London St George's Molecular and Clinical Sciences Research Institute, London, UK
| | - Harald Hampel
- Sorbonne University, GRC No. 21, Alzheimer Precision Medicine, Pitié-Salpêtrière Hospital, Paris, France
| | - Vesna Jelic
- Division of Clinical Geriatrics, NVS Department, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Jaeseung Jeong
- Department of Bio and Brain Engineering/Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Doh Kwan Kim
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Milica Kramberger
- Center for cognitive and movement disorders, Department of neurology, University Medical Center Ljubljana, Slovenia
| | - Sanjeev Kumar
- Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | | | - Flavio Nobili
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy; Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI)
| | | | - Aina Puce
- Department of Psychological and Brain Sciences at Indiana University in Bloomington, Indiana, USA
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Dirk J A Smit
- Department of Psychiatry Academisch Medisch Centrum Universiteit van Amsterdam, Amsterdam, the Netherlands
| | - Andrea Soricelli
- IRCCS SDN, Naples, Italy; Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | - Stefan Teipel
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany; German Center for Neurodegenerative Diseases (DZNE) - Rostock/Greifswald, Rostock, Germany
| | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales; Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney, Australia
| | | | - Pedro Valdes-Sosa
- Cuban Neuroscience Center, Havana, Cuba; Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Andrea Vergallo
- Sorbonne University, GRC No. 21, Alzheimer Precision Medicine, Pitié-Salpêtrière Hospital, Paris, France
| | - Görsev Yener
- Izmir Biomedicine and Genome Center. Dokuz Eylul University Health Campus, Izmir, Turkey
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10
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EEG Synchronization-Parameters in Patients With Subcortical Arteriosclerotic Encephalopathy and Gait Disorder. J Clin Neurophysiol 2021; 38:331-339. [PMID: 32501954 DOI: 10.1097/wnp.0000000000000701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Subcortical arteriosclerotic encephalopathy (SAE) is characterized by extensive white matter lesions in the MRI. Clinical symptoms are cognitive impairment, ranging from mild deficits to vascular dementia, impaired executive functioning, and gait disorders. In the EEG of SAE patients with vascular dementia, the lower frequencies are increased. However, it is unclear whether EEG changes also exist in SAE patients with gait disorders but without vascular dementia. METHODS The authors analyzed the EEGs of 50 nondemented patients with SAE and gait disorders and 50 healthy controls applying pointwise transinformation as a measure of synchronization. RESULTS Hundred seconds of waking EEG that appeared unaltered in visual analysis were sufficient to prove changes in synchronization. The authors found a decrease in the mean level of synchronization, combined with an elongation of synchronization time in all examined brain areas. These effects correlated slightly with the extent of subcortical lesions. CONCLUSIONS Changes in EEG synchronization in patients with SAE and gait disorders seem to occur independently of cognitive function. The causal relationship of the changes in EEG synchronization and gait disorders remains to be clarified. The results of this study might point to a decrease in coupling efficiency in these patients, with the increase in synchronization duration as a possible compensatory mechanism. Because a time-efficient signal transmission particularly during gait execution is crucial, reduced efficiency might contribute to an impairment of postural stabilization. The study results might indicate a neuronal network for planning and execution of motor activity and particularly gait, extending from the frontal over the central to the parietal cortex.
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Quandt F, Fischer F, Schröder J, Heinze M, Lettow I, Frey BM, Kessner SS, Schulz M, Higgen FL, Cheng B, Gerloff C, Thomalla G. Higher white matter hyperintensity lesion load is associated with reduced long-range functional connectivity. Brain Commun 2020; 2:fcaa111. [PMID: 33134915 PMCID: PMC7585696 DOI: 10.1093/braincomms/fcaa111] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/08/2020] [Accepted: 06/12/2020] [Indexed: 01/18/2023] Open
Abstract
Cerebral small vessel disease is a common disease in the older population and is recognized as a major risk factor for cognitive decline and stroke. Small vessel disease is considered a global brain disease impacting the integrity of neuronal networks resulting in disturbances of structural and functional connectivity. A core feature of cerebral small vessel disease commonly present on neuroimaging is white matter hyperintensities. We studied high-resolution resting-state EEG, leveraging source reconstruction methods, in 35 participants with varying degree of white matter hyperintensities without clinically evident cognitive impairment in an observational study. In patients with increasing white matter lesion load, global theta power was increased independently of age. Whole-brain functional connectivity revealed a disrupted network confined to the alpha band in participants with higher white matter hyperintensities lesion load. The decrease of functional connectivity was evident in long-range connections, mostly originating or terminating in the frontal lobe. Cognitive testing revealed no global cognitive impairment; however, some participants revealed deficits of executive functions that were related to larger white matter hyperintensities lesion load. In summary, participants without clinical signs of mild cognitive impairment or dementia showed oscillatory changes that were significantly related to white matter lesion load. Hence, oscillatory neuronal network changes due to white matter lesions might act as biomarker prior to clinically relevant behavioural impairment.
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Affiliation(s)
- Fanny Quandt
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
- Correspondence to: Dr. Fanny Quandt Department of Neurology Martinistr. 52, 20246 Hamburg, Germany E-mail:
| | - Felix Fischer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Julian Schröder
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Marlene Heinze
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Iris Lettow
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Benedikt M Frey
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Simon S Kessner
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Maximilian Schulz
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Focko L Higgen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Germany
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Subclinical white matter lesions and medial temporal lobe atrophy are associated with EEG slowing in a memory clinic cohort. Clin Neurophysiol 2017; 128:1575-1582. [PMID: 28709123 DOI: 10.1016/j.clinph.2017.05.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 04/30/2017] [Accepted: 05/29/2017] [Indexed: 01/16/2023]
Abstract
OBJECTIVE The aim of the study was to describe the relationship between electroencephalographic (EEG) findings obtained by standardized visual analysis, subclinical white matter lesions (WML) and brain atrophy in a large memory clinic population. METHODS Patients with Alzheimer's disease (AD, n=58), mild cognitive impairment (MCI, n=141), subjective cognitive impairment (SCI, n=194) had clinical, MRI based WML severity and regional atrophy assessments, and routine resting EEG recording. Background activity (BA) and episodic and continuous abnormalities were assessed visually in EEG. RESULTS WML (p=0.006) and atrophy in medial temporal regions (MTA) (p=<0.001) were associated with slower BA in all diagnoses. WML were associated in SCI with total episodic EEG abnormalities (p=0.03). CONCLUSIONS EEG is associated with subclinical WML burden and cortical brain atrophy in a memory clinic population. SIGNIFICANCE Even the standard visually assessed EEG can complement a memory clinic diagnostic workup.
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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: 34] [Impact Index Per Article: 4.3] [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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Dey AK, Stamenova V, Turner G, Black SE, Levine B. Pathoconnectomics of cognitive impairment in small vessel disease: A systematic review. Alzheimers Dement 2016; 12:831-45. [DOI: 10.1016/j.jalz.2016.01.007] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Revised: 10/21/2015] [Accepted: 01/15/2016] [Indexed: 11/26/2022]
Affiliation(s)
- Ayan K. Dey
- Faculty of Medicine, Institute of Medical Science University of Toronto Toronto Ontario Canada
- Rotman Research Institute Baycrest Hospital Toronto Ontario Canada
| | | | - Gary Turner
- Department of Psychology, Faculty of Health York University Toronto Ontario Canada
| | - Sandra E. Black
- Faculty of Medicine, Institute of Medical Science University of Toronto Toronto Ontario Canada
- Rotman Research Institute Baycrest Hospital Toronto Ontario Canada
- Evaluative Clinical Sciences, Hurvitz Brain Sciences Research Program Sunnybrook Research Institute Toronto Ontario Canada
- Division of Neurology Department of Medicine Sunnybrook Health Sciences Centre Toronto Ontario Canada
- L.C. Campbell Cognitive Neurology Research Unit Sunnybrook Health Sciences Centre Toronto Ontario Canada
| | - Brian Levine
- Faculty of Medicine, Institute of Medical Science University of Toronto Toronto Ontario Canada
- Rotman Research Institute Baycrest Hospital Toronto Ontario Canada
- Department of Psychology University of Toronto Toronto Ontario Canada
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Graph theoretical analysis of EEG effective connectivity in vascular dementia patients during a visual oddball task. Clin Neurophysiol 2016; 127:324-334. [DOI: 10.1016/j.clinph.2015.04.063] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Revised: 03/30/2015] [Accepted: 04/20/2015] [Indexed: 11/22/2022]
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van Straaten E, den Haan J, de Waal H, van der Flier W, Barkhof F, Prins N, Stam C. Disturbed phase relations in white matter hyperintensity based vascular dementia: An EEG directed connectivity study. Clin Neurophysiol 2015; 126:497-504. [DOI: 10.1016/j.clinph.2014.05.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2013] [Revised: 05/20/2014] [Accepted: 05/21/2014] [Indexed: 10/25/2022]
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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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Wang C, Xu J, Lou W, Zhao S. Dynamic information flow analysis in Vascular Dementia patients during the performance of a visual oddball task. Neurosci Lett 2014; 580:108-13. [DOI: 10.1016/j.neulet.2014.07.056] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2014] [Revised: 07/07/2014] [Accepted: 07/29/2014] [Indexed: 11/28/2022]
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Tsolaki A, Kazis D, Kompatsiaris I, Kosmidou V, Tsolaki M. Electroencephalogram and Alzheimer's disease: clinical and research approaches. Int J Alzheimers Dis 2014; 2014:349249. [PMID: 24868482 PMCID: PMC4020452 DOI: 10.1155/2014/349249] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Accepted: 03/16/2014] [Indexed: 01/08/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled "Cognitive Signal Processing Lab," which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.
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Affiliation(s)
- Anthoula Tsolaki
- Medical Physics Laboratory, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Dimitrios Kazis
- 3rd Department of Neurology, Aristotle University of Thessaloniki, Exochi, 57010 Thessaloniki, Greece
| | - Ioannis Kompatsiaris
- Centre of Research and Technology, Information Technologies Institute, 6th Klm Charilaou-Thermi Road, P.O. Box 60361, Thermi, 57001 Thessaloniki, Greece
| | - Vasiliki Kosmidou
- Centre of Research and Technology, Information Technologies Institute, 6th Klm Charilaou-Thermi Road, P.O. Box 60361, Thermi, 57001 Thessaloniki, Greece
| | - Magda Tsolaki
- 3rd Department of Neurology, Aristotle University of Thessaloniki, Exochi, 57010 Thessaloniki, Greece
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