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The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition

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Abstract

Hyperventilation (HV) is a voluntary activity that causes changes in the neuronal firing characteristics noticeable in the electroencephalogram (EEG) signals. HV-related changes have been scribed to modulation of pO2/pCO2 blood contents. Therefore, an HV test is routinely used for highlighting brain abnormalities including those depending to neurobiological mechanisms at the basis of neurodegenerative disorders. The main aim of the present paper is to study the effectiveness of HV test in modifying the functional connectivity from the EEG signals that can be typical of a prodromal state of Alzheimer’s disease (AD), the Mild Cognitive Impairment prodromal to Alzheimer condition. MCI subjects and a group of age-matched healthy elderly (Ctrl) were enrolled and subjected to EEG recording during HV, eyes-closed (EC), and eyes-open (EO) conditions. Since the cognitive decline in MCI seems to be a progressive disconnection syndrome, the approach we used in the present study is the graph theory, which allows to describe brain networks with a series of different parameters. Small world (SW), modularity (M), and global efficiency (GE) indexes were computed among the EC, EO, and HV conditions comparing the MCI group to the Ctrl one. All the three graph parameters, computed in the typical EEG frequency bands, showed significant changes among the three conditions, and more interestingly, a significant difference in the GE values between the MCI group and the Ctrl one was obtained, suggesting that the combination of HV test and graph theory parameters should be a powerful tool for the detection of possible cerebral dysfunction and alteration.

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Data availability

The data that support the findings of this study are available on request from the corresponding author.

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Funding

This work was partially supported by the Italian Ministry of Health for Institutional Research (Ricerca corrente), for the project “Prediction of conversion from Mild Cognitive Impairment to Alzheimer’s disease based on TMS-EEG biomarkers” (GR-2016-02361802) and by Toto Holding.

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Authors and Affiliations

  1. Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy

    Francesca Miraglia, Chiara Pappalettera, Alessia Cacciotti, Fabrizio Vecchio & Paolo Maria Rossini

  2. Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy

    Francesca Miraglia, Chiara Pappalettera, Alessia Cacciotti & Fabrizio Vecchio

  3. Dipartimento Neuroscienze, Organi di Senso e Torace, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy

    Valeria Guglielmi

  4. Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

    Rosa Manenti

  5. Department of Neurorehabilitation Sciences, Casa di Cura IGEA, Milano, Italy

    Elda Judica

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  1. Francesca Miraglia
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  2. Chiara Pappalettera
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Miraglia, F., Pappalettera, C., Guglielmi, V. et al. The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition. GeroScience 45, 1857–1867 (2023). https://doi.org/10.1007/s11357-023-00733-5

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