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.
Die Original-Version des Kapitels wurde korrigiert. Ein Erratum finden Sie unter
Chapter PDF
Similar content being viewed by others
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer-Verlag GmbH Deutschland
About this paper
Cite this paper
Al-Zubaidi, A., Mertins, A., Heldmann, M., Jauch-Chara, K., Münte, T.F. (2018). Amplitude of brain signals classify hunger status based on machine learning in resting-state fMRI. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-662-56537-7_13
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-56536-0
Online ISBN: 978-3-662-56537-7
eBook Packages: Computer Science and Engineering (German Language)