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Amplitude of brain signals classify hunger status based on machine learning in resting-state fMRI

  • Arkan Al-Zubaidi
  • Alfred Mertins
  • Marcus Heldmann
  • Kamila Jauch-Chara
  • Thomas F. Münte
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Resting-state fMRI (rs-fMRI) is a method of functional brain imaging that allows the task-free exploration of the intrinsic functional connectivity in humans. Since central nervous pathways regulate food intake and eating behavior, it is assumed that changes in the homeostatic state have an impact on the connectivity patterns of rs-fMRI. Here, we compare the accuracy of three data-driven approaches in classifying two metabolic states (hunger vs. satiety) depending on the observed rs-fMRI fluctuations.

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Arkan Al-Zubaidi
    • 1
  • Alfred Mertins
    • 2
  • Marcus Heldmann
    • 1
  • Kamila Jauch-Chara
    • 3
  • Thomas F. Münte
    • 1
  1. 1.Department of NeurologyUniversity of LübeckLübeckDeutschland
  2. 2.Institute for Signal ProcessingUniversity of LübeckLübeckDeutschland
  3. 3.Department of PsychiatryUniversity of KielKielDeutschland

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