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Dimitriadis SI, Laskaris NA, Bitzidou MP, et al. A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Front Neurosci. 2015;9:350doi: 10.3389/fnins.2015.00350.
Dimitriadis, S. I., Laskaris, N. A., Bitzidou, M. P., Tarnanas, I., & Tsolaki, M. N. (2015). A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Frontiers in neuroscience, 9350. https://doi.org/10.3389/fnins.2015.00350
Dimitriadis, Stavros I, et al. "A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses." Frontiers in neuroscience vol. 9 (2015): 350. doi: https://doi.org/10.3389/fnins.2015.00350
Dimitriadis SI, Laskaris NA, Bitzidou MP, Tarnanas I, Tsolaki MN. A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Front Neurosci. 2015 Oct 20;9:350. doi: 10.3389/fnins.2015.00350. eCollection 2015. PMID: 26539070; PMCID: PMC4611062.
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Front Neurosci. 2015 Oct 20;9:350. doi: 10.3389/fnins.2015.00350. eCollection 2015.

A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses.

Frontiers in neuroscience

Stavros I Dimitriadis, Nikolaos A Laskaris, Malamati P Bitzidou, Ioannis Tarnanas, Magda N Tsolaki

Affiliations

  1. Artificial Intelligence Information Analysis Lab, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece ; Neuroinformatics Group, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece.
  2. Artificial Intelligence Information Analysis Lab, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece.
  3. Health-IS Lab, Chair of Information Management, ETH Zurich Zurich, Switzerland ; 3rd Department of Neurology, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece.
  4. 3rd Department of Neurology, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece.

PMID: 26539070 PMCID: PMC4611062 DOI: 10.3389/fnins.2015.00350

Abstract

The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis.

Keywords: ERPs; cognitive impairment; connectomic biomarkers; dynamic coordination; dynome; functional connectomics; phase-amplitude coupling

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