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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.
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1 Massachusetts General Hospital, Harvard Medical School, Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Beth Israel Deaconess Medical Center, Harvard Medical School, Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); University Politehnica of Bucharest, AI Multimedia Lab, Research Center CAMPUS, Bucharest, Romania (GRID:grid.4551.5) (ISNI:0000 0001 2109 901X)
2 Santa Lucia Foundation, Rome, Italy (GRID:grid.417778.a) (ISNI:0000 0001 0692 3437); La Sapienza University, Department of Psychology, Rome, Italy (GRID:grid.7841.a)
3 Santa Lucia Foundation, Rome, Italy (GRID:grid.417778.a) (ISNI:0000 0001 0692 3437)
4 Massachusetts General Hospital, Harvard Medical School, Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Beth Israel Deaconess Medical Center, Harvard Medical School, Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); University of Bari Aldo Moro, Department of Education, Psychology and Communication, Bari, Italy (GRID:grid.7644.1) (ISNI:0000 0001 0120 3326)
5 University of Siena, Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery, Neurology and Clinical Neurophysiology Section, Siena, Italy (GRID:grid.9024.f) (ISNI:0000 0004 1757 4641)
6 Massachusetts General Hospital, Harvard Medical School, Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Beth Israel Deaconess Medical Center, Harvard Medical School, Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); University of Siena, Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery, Neurology and Clinical Neurophysiology Section, Siena, Italy (GRID:grid.9024.f) (ISNI:0000 0004 1757 4641)
7 University Politehnica of Bucharest, AI Multimedia Lab, Research Center CAMPUS, Bucharest, Romania (GRID:grid.4551.5) (ISNI:0000 0001 2109 901X)
8 University of Ferrara, Department of Neuroscience and Rehabilitation, Section of Human Physiology, Ferrara, Italy (GRID:grid.8484.0) (ISNI:0000 0004 1757 2064); Santa Lucia Foundation, Rome, Italy (GRID:grid.417778.a) (ISNI:0000 0001 0692 3437)
9 Massachusetts General Hospital, Harvard Medical School, Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Beth Israel Deaconess Medical Center, Harvard Medical School, Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)