Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer's disease

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review



  • Author: Musaeus, Christian Sandøe

    University of Copenhagen

  • Author: Engedal, Knut

    University of Oslo

  • Author: Høgh, Peter

    Zealand University Hospital

  • Author: Jelic, Vesna

    Karolinska Institutet

  • Author: Mørup, Morten

    Cognitive Systems, Department of Applied Mathematics and Computer Science , Technical University of Denmark, Richard Petersens Plads, 2800, Kgs. Lyngby, Denmark

  • Author: Naik, Mala

    Haraldsplass Deaconess Hospital

  • Author: Oeksengaard, Anne-Rita

    Karolinska Institutet

  • Author: Snaedal, Jon

    Landspitali University Hospital

  • Author: Wahlund, Lars-Olof

    Karolinska Institutet

  • Author: Waldemar, Gunhild

    University of Copenhagen

  • Author: Andersen, Birgitte Bo

    University of Copenhagen, Denmark

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Objective: Quantitative EEG power has not been as effective in discriminating between healthy aging and Alzheimer's disease as conventional biomarkers. But EEG coherence has shown promising results in small samples. The overall aim was to evaluate if EEG connectivity markers can discriminate between Alzheimer's disease, mild cognitive impairment, and healthy aging and to explore the early underlying changes in coherence. Methods: EEGs were included in the analysis from 135 healthy controls, 117 patients with mild cognitive impairment, and 117 patients with Alzheimer's disease from six Nordic memory clinics. Principal component analysis was performed before multinomial regression. Results: We found classification accuracies of above 95% based on coherence, imaginary part of coherence, and the weighted phase-lag index. The most prominent changes in coherence were decreased alpha coherence in Alzheimer's disease, which was correlated to the scores of the 10-word test in the Consortium to Establish a Registry for Alzheimer's Disease battery. Conclusions: The diagnostic accuracies for EEG connectivity measures are higher than findings from studies investigating EEG power and conventional Alzheimer's disease biomarkers. Furthermore, decreased alpha coherence is one of the earliest changes in Alzheimer's disease and associated with memory function. Significance: EEG connectivity measures may be useful supplementary diagnostic classifiers.

Original languageEnglish
JournalClinical Neurophysiology
Issue number10
Pages (from-to)1889-1899
Publication statusPublished - 1 Oct 2019
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Alzheimer's disease, Coherence, Diagnostic, EEG, Imaginary part of coherence, Mild cognitive impairment, Weighted phase-lag index

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