Literature DB >> 24933410

Quantitative EEG in Alzheimer's disease: cognitive state, resting state and association with disease severity.

Heinrich Garn1, Markus Waser2, Manfred Deistler3, Reinhold Schmidt4, Peter Dal-Bianco5, Gerhard Ransmayr6, Josef Zeitlhofer7, Helena Schmidt8, Stephan Seiler9, Guenter Sanin10, Georg Caravias11, Peter Santer12, Dieter Grossegger13, Wolfgang Fruehwirt14, Thomas Benke15.   

Abstract

BACKGROUND: Quantitative electroencephalogram (qEEG) recorded during cognitive tasks has been shown to differentiate between patients with Alzheimer's disease (AD) and healthy individuals. However, the association between various qEEG markers recorded during mnestic paradigms and clinical measures of AD has not been studied in detail.
OBJECTIVE: To evaluate if 'cognitive' qEEG is a useful diagnostic option, particularly if memory paradigms are used as cognitive stimulators.
METHODS: This study is part of the Prospective Registry on Dementia in Austria (PRODEM), a multicenter dementia research project. A cohort of 79 probable AD patients was included in a cross-sectional analysis. qEEG recordings performed in resting states were compared with recordings during cognitively active states. Cognition was evoked with a face-name paradigm and a paired-associate word list task, respectively. Relative band powers, coherence and auto-mutual information were computed as functions of MMSE scores for the memory paradigms and during rest. Analyses were adjusted for the co-variables age, sex, duration of dementia and educational level.
RESULTS: MMSE scores explained 36-51% of the variances of qEEG-markers. Face-name encoding with eyes open was superior to resting state with eyes closed in relative theta and beta1 power as well as coherence, whereas relative alpha power and auto-mutual information yielded more significant results during resting state with eyes closed. The face-name task yielded stronger correlations with MMSE scores than the verbal memory task.
CONCLUSION: qEEG alterations recorded during mnestic activity, particularly face-name encoding showed the highest association with the MMSE and may serve as a clinically valuable marker for disease severity.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Alzheimer's disease; Cognitive state; Quantitative EEG; Resting state

Mesh:

Substances:

Year:  2014        PMID: 24933410     DOI: 10.1016/j.ijpsycho.2014.06.003

Source DB:  PubMed          Journal:  Int J Psychophysiol        ISSN: 0167-8760            Impact factor:   2.997


  12 in total

1.  Quantifying synchrony patterns in the EEG of Alzheimer's patients with linear and non-linear connectivity markers.

Authors:  Markus Waser; Heinrich Garn; Reinhold Schmidt; Thomas Benke; Peter Dal-Bianco; Gerhard Ransmayr; Helena Schmidt; Stephan Seiler; Günter Sanin; Florian Mayer; Georg Caravias; Dieter Grossegger; Wolfgang Frühwirt; Manfred Deistler
Journal:  J Neural Transm (Vienna)       Date:  2015-09-28       Impact factor: 3.575

2.  Differential diagnosis between patients with probable Alzheimer's disease, Parkinson's disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features.

Authors:  Heinrich Garn; Carmina Coronel; Markus Waser; Georg Caravias; Gerhard Ransmayr
Journal:  J Neural Transm (Vienna)       Date:  2017-02-27       Impact factor: 3.575

3.  Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment.

Authors:  Raymundo Cassani; Mar Estarellas; Rodrigo San-Martin; Francisco J Fraga; Tiago H Falk
Journal:  Dis Markers       Date:  2018-10-04       Impact factor: 3.434

4.  EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers.

Authors:  Greg Rupp; Chris Berka; Amir H Meghdadi; Marija Stevanović Karić; Marc Casillas; Stephanie Smith; Theodore Rosenthal; Kevin McShea; Emily Sones; Thomas D Marcotte
Journal:  Front Hum Neurosci       Date:  2019-01-15       Impact factor: 3.169

Review 5.  Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review.

Authors:  Jie Sun; Bin Wang; Yan Niu; Yuan Tan; Chanjuan Fan; Nan Zhang; Jiayue Xue; Jing Wei; Jie Xiang
Journal:  Entropy (Basel)       Date:  2020-02-20       Impact factor: 2.524

6.  Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment.

Authors:  Amir H Meghdadi; Marija Stevanović Karić; Marissa McConnell; Greg Rupp; Christian Richard; Joanne Hamilton; David Salat; Chris Berka
Journal:  PLoS One       Date:  2021-02-05       Impact factor: 3.240

7.  Simultaneous Assessment of Electroencephalography Microstates and Resting State Intrinsic Networks in Alzheimer's Disease and Healthy Aging.

Authors:  Stefan J Teipel; Katharina Brüggen; Anna Gesine Marie Temp; Kristina Jakobi; Marc-André Weber; Christoph Berger
Journal:  Front Neurol       Date:  2021-06-17       Impact factor: 4.003

8.  Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences.

Authors:  Dustin Fetterhoff; Robert A Kraft; Roman A Sandler; Ioan Opris; Cheryl A Sexton; Vasilis Z Marmarelis; Robert E Hampson; Sam A Deadwyler
Journal:  Front Syst Neurosci       Date:  2015-09-17

Review 9.  Precocious Alterations of Brain Oscillatory Activity in Alzheimer's Disease: A Window of Opportunity for Early Diagnosis and Treatment.

Authors:  Valentine Hamm; Céline Héraud; Jean-Christophe Cassel; Chantal Mathis; Romain Goutagny
Journal:  Front Cell Neurosci       Date:  2015-12-21       Impact factor: 5.505

10.  Neuroimaging markers of global cognition in early Alzheimer's disease: A magnetic resonance imaging-electroencephalography study.

Authors:  Markus Waser; Thomas Benke; Peter Dal-Bianco; Heinrich Garn; Jochen A Mosbacher; Gerhard Ransmayr; Reinhold Schmidt; Stephan Seiler; Helge B D Sorensen; Poul J Jennum
Journal:  Brain Behav       Date:  2018-12-27       Impact factor: 2.708

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