Author:
Tăuƫan Alexandra-Maria,Casula Elias P.,Pellicciari Maria Concetta,Borghi Ilaria,Maiella Michele,Bonni Sonia,Minei Marilena,Assogna Martina,Palmisano Annalisa,Smeralda Carmelo,Romanella Sara M.,Ionescu Bogdan,Koch Giacomo,Santarnecchi Emiliano
Abstract
AbstractThe 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.
Funder
Romanian-US Fulbright Commission
Facoltà di Medicina e Psicologiaa, Sapienza Università di Roma
Beth Israel Deaconess Medical Center
Defense Sciences Office, DARPA
National Institute for Health Care Management Foundation
Alzheimer's Drug Discovery Foundation
Publisher
Springer Science and Business Media LLC
Cited by
8 articles.
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