Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization

  • Pavel Prado
  • , Jhony A. Mejía
  • , Agustín Sainz-Ballesteros
  • , Agustina Birba
  • , Sebastian Moguilner
  • , Rubén Herzog
  • , Mónica Otero
  • , Jhosmary Cuadros
  • , Lucía Z-Rivera
  • , Daniel Franco O'Byrne
  • , Mario Parra
  • , Agustín Ibáñez

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Introduction: Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. Methods: We implemented an automatic processing pipeline incorporating electrode layout integrations, patient–control normalizations, and multi-metric EEG source space connectomics analyses. Results: Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Discussion: Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia.

Original languageEnglish
Article numbere12455
Pages (from-to)e12455
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume15
Issue number3
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

ASJC Scopus subject areas

  • Clinical Neurology
  • Psychiatry and Mental health

Fingerprint

Dive into the research topics of 'Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization'. Together they form a unique fingerprint.

Cite this