TMS-EEG perturbation biomarkers for Alzheimer’s disease patients classification

Autor: Alexandra-Maria Tăuƫan, Elias P. Casula, Maria Concetta Pellicciari, Ilaria Borghi, Michele Maiella, Sonia Bonni, Marilena Minei, Martina Assogna, Annalisa Palmisano, Carmelo Smeralda, Sara M. Romanella, Bogdan Ionescu, Giacomo Koch, Emiliano Santarnecchi
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)
Druh dokumentu: article
ISSN: 2045-2322
26390590
DOI: 10.1038/s41598-022-22978-4
Popis: Abstract The combination of TMS and EEG has the potential to capture relevant features of Alzheimer’s disease (AD) pathophysiology. We used a machine learning framework to explore time-domain features characterizing AD patients compared to age-matched healthy controls (HC). More than 150 time-domain features including some related to local and distributed evoked activity were extracted from TMS-EEG data and fed into a Random Forest (RF) classifier using a leave-one-subject out validation approach. The best classification accuracy, sensitivity, specificity and F1 score were of 92.95%, 96.15%, 87.94% and 92.03% respectively when using a balanced dataset of features computed globally across the brain. The feature importance and statistical analysis revealed that the maximum amplitude of the post-TMS signal, its Hjorth complexity and the amplitude of the TEP calculated in the window 45–80 ms after the TMS-pulse were the most relevant features differentiating AD patients from HC. TMS-EEG metrics can be used as a non-invasive tool to further understand the AD pathophysiology and possibly contribute to patients’ classification as well as longitudinal disease tracking.
Databáze: Directory of Open Access Journals
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