TMS-EEG perturbation biomarkers for Alzheimer’s disease patients classification
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 11, 2023
- Source ID
- 10.1038/s41598-022-22978-4
Entities
People
- Alexandra-maria Tăuƫan
- Annalisa Palmisano
- Bogdan Ionescu
- Carmelo Smeralda
- Elias P. Casula
- Emiliano Santarnecchi
- Giacomo Koch
- Ilaria Borghi
- Maria Concetta Pellicciari
- Marilena Minei
- Martina Assogna
- Michele Maiella
- Sara M. Romanella
- Sonia Bonni
Organizations
- Alzheimer's Drug Discovery Foundation
- Beth Israel Deaconess Medical Center
- Faculty of Medicine and Psychology, Sapienza University of Rome
- National Institute for Health Care Management