Abstract: Amplitude of Brain Signals Classify Hunger Status based on Machine Learning in Resting-state fMRI

Conference paper
Part of the Informatik aktuell book series (INFORMAT)


Resting-state fMRI (rs-fMRI) allows for a task-free exploration of the human brain’s intrinsic functional connectivity. 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

  1. 1.Department of NeurologyLübeckDeutschland

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