EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.

Journal: Schizophrenia bulletin
PMID: 39248267

Abstract

BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders.

Authors

  • Elif Sarisik
    Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
  • David Popovic
    Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.
  • Daniel Keeser
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Adyasha Khuntia
    Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
  • Kolja Schiltz
    Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
  • Peter Falkai
    Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany;
  • Oliver Pogarell
    Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich Nussbaumstr. 7, 80336 Munich, Germany.
  • Nikolaos Koutsouleris
    Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.

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