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

Sci Rep. 2023 May 11;13(1):7667. doi: 10.1038/s41598-022-22978-4.

Authors

Alexandra-Maria Tăuƫan^#^{1

2

3}, Elias P Casula^#^{4

5}, Maria Concetta Pellicciari⁴, Ilaria Borghi⁴, Michele Maiella⁴, Sonia Bonni⁴, Marilena Minei⁴, Martina Assogna⁴, Annalisa Palmisano^{1

2

6}, Carmelo Smeralda⁷, Sara M Romanella^{1

2

7}, Bogdan Ionescu³, Giacomo Koch^#^{8

4}, Emiliano Santarnecchi^#^{9

10}

Affiliations

¹ Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
² Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
³ AI Multimedia Lab, Research Center CAMPUS, University Politehnica of Bucharest, 061344, Bucharest, Romania.
⁴ Santa Lucia Foundation, 00179, Rome, Italy.
⁵ Department of Psychology, La Sapienza University, Via dei Marsi 78, 00185, Rome, Italy.
⁶ Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy.
⁷ Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy.
⁸ Department of Neuroscience and Rehabilitation, Section of Human Physiology, University of Ferrara, 44121, Ferrara, Italy.
⁹ Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. esantarnecchi@mgh.harvard.edu.
¹⁰ Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA. esantarnecchi@mgh.harvard.edu.

^# Contributed equally.

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.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Alzheimer Disease* / diagnosis
Biomarkers
Brain
Electroencephalography
Humans
Magnetic Resonance Imaging

Substances

Biomarkers

Abstract

Publication types

MeSH terms

Substances

Grants and funding