Quantitative EEG in progressing <i>vs</i> stable mild cognitive impairment (MCI): results of a 1‐year follow‐up study

Luckhaus, Christian; Grass-Kapanke, Brigitte; Blaeser, Ingo; Ihl, Ralf; Supprian, Tillmann; Winterer, Georg; Zielasek, Jiirgen; Brinkmeyer, Jiirgen

doi:10.1002/gps.2042

Cited by 89 publications

(75 citation statements)

References 37 publications

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“…Brainwave components of the resting EEG could be altered in the early stages of AD. There is evidence that EEG power in the alpha band declines with AD-related cognitive impairment [293]. Other studies have shown enhanced low-frequency brain oscillation in the theta [294] and delta bands in temporal and occipital areas as well as reduction of beta power in temporal and occipital areas in MCI [295].…”

Section: Resting-state Neuroelectrical/neuromagnetic Markersmentioning

confidence: 98%

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: A long-range point of view beyond 2020

Hampel

Lista

Teipel

et al. 2014

Biochemical Pharmacology

102

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Recent advances in understanding the molecular mechanisms underlying various paths toward the pathogenesis of Alzheimer's disease (AD) has begun to provide new insight for interventions to modify disease progression. The evolving knowledge gained from multidisciplinary basic research has begun to identify new concepts for treatments and distinct classes of therapeutic targets; as well as putative disease-modifying compounds that are now being tested in clinical trials. There is a mounting consensus that such disease modifying compounds and/or interventions are more likely to be effectively administered as early as possible in the cascade of pathogenic processes preceding and underlying the clinical expression of AD. The budding sentiment is that "treatments" need to be applied before various molecular mechanisms converge into an irreversible pathway leading to morphological, metabolic and functional alterations that characterize the pathophysiology of AD. In light of this, biological indicators of pathophysiological mechanisms are desired to chart and detect AD throughout the asymptomatic early molecular stages into the prodromal and early dementia phase. A major conceptual development in the clinical AD research field was the recent proposal of new diagnostic criteria, which specifically incorporate the use of biomarkers as defining criteria for preclinical stages of AD. This paradigm shift in AD definition, conceptualization, operationalization, detection and diagnosis represents novel fundamental opportunities for the modification of interventional trial designs. This perspective summarizes not only present knowledge regarding biological markers but also unresolved questions on the status of surrogate indicators for detection of the disease in asymptomatic people and diagnosis of AD

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Section: Resting-state Neuroelectrical/neuromagnetic Markersmentioning

confidence: 98%

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: A long-range point of view beyond 2020

Hampel

Lista

Teipel

et al. 2014

Biochemical Pharmacology

102

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“…The main effects are summarized as follows: 1) combined alpha-theta power density and mean frequency from left temporal-occipital regions [316]; 2) anterior localization of alpha sources [315]; 3) high temporal delta sources [378]; 4) high theta power density [379]; and 5) low posterior alpha power density [380]. …”

Section: Contribution and Role Of Electroencephalography (Eeg)mentioning

confidence: 99%

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

Hampel

Toschi

Babiloni

et al. 2018

JAD

126

113

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The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an “omics”-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer’s disease (AD). The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group “Alzheimer Precision Medicine” (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development towards breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

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“…EEG biomarkers are optimal for screening purposes because the EEG recording can be obtained using relative cheap and non-invasive equipment, which is widely available and fast to use. Several previous EEG studies of conversion from mild cognitive impairment to Alzheimer's disease have been conducted (Jelic et al, 1996, 2000; Huang et al, 2000; Stam et al, 2003; Schoonenboom et al, 2004; Rombouts et al, 2005; Babiloni et al, 2006, 2011; Kwak, 2006; Rossini et al, 2006, 2008; Lehmann et al, 2007; Moretti et al, 2007a, b, 2008, 2011; Luckhaus et al, 2008) mainly using biomarkers such as spectral measures and synchronization between brain regions. Machine-learning techniques have been used to explore differences between MCI and AD with varying success (Huang et al, 2000; Bennys et al, 2001; Prichep et al, 2006; Buscema et al, 2007; Lehmann et al, 2007; Prichep, 2007; Rossini et al, 2008), however, only few studies have tried to predict the conversion from MCI to AD (Prichep et al, 2006; Prichep, 2007; Antila et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

Poil¹,

Haan²,

Flier³

et al. 2013

Front. Aging Neurosci.

163

114

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Alzheimer's disease (AD) is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI) is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG) biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a 2-year period. We followed 86 patients initially diagnosed with MCI for 2 years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13–30 Hz) can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/). We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention.

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Quantitative EEG in progressing vs stable mild cognitive impairment (MCI): results of a 1‐year follow‐up study

Cited by 89 publications

References 37 publications

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: A long-range point of view beyond 2020

Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: A long-range point of view beyond 2020

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

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