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Multivariate empirical mode decomposition reveals markers of Alzheimer's Disease in the oscillatory response to transcranial magnetic stimulation

  • 1Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy; Department of Physics and Astronomy, University of Padova, Padova, Italy. Electronic address: davide.bernardi@unipd.it.
  • 2Department of Systems Medicine, University of Tor Vergata, Rome, Italy; Department of Clinical and Behavioral Neurology, Santa Lucia Foundation IRCCS, Rome, Italy.
  • 3Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.
  • 4Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy; Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy. Electronic address: luciano.fadiga@iit.it.
  • 5Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy; Department of Clinical and Behavioral Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy.
  • 6Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy; Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy.
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June 14, 2025

Abstract

OBJECTIVE

To investigate EEG activity following transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex of Alzheimer's Disease (AD) patients and control subjects using a data-driven characterization of brain oscillatory activity without prescribed frequency bands.

METHODS

We employed multivariate empirical mode decomposition (MEMD) to analyze the TMS-EEG response of 38 AD patients and 21 control subjects. We used the distinct features of EEG oscillatory modes to train a classification algorithm, a support vector machine.

RESULTS

AD patients exhibited a weakened slow-frequency response. Faster oscillatory modes displayed a biphasic response pattern in controls, characterized by an early increase followed by a widespread suppression, which was reduced in AD patients. Classification achieved robust discrimination performance (85%/23% true/false positive rate).

CONCLUSIONS

AD causes an impairment in the oscillatory response to TMS that has distinct features in different frequency ranges. These features uncovered by MEMD could serve as an effective EEG diagnostic marker.

SIGNIFICANCE

Early detection of AD requires diagnostic tools that are both effective and accessible. Combining EEG with TMS shows great promise. Our results and method enhance TMS-EEG both as a practical diagnostic tool, and as a way to further our understanding of AD pathophysiology.

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