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Updated: Jul 12, 2025

Control of Eating Behavior Using a Novel Feedback System
Published on: May 8, 2018
Mechanisms underlying fNIRS-neurofeedback over the prefrontal cortex for participants with binge-eating disorder
Sarah A Rösch1, Ricarda Schmidt2, Jytte Wimmer2
1Integrated Research and Treatment Center AdiposityDiseases, Behavioral Medicine Research Unit, Leipzig University Medical Center, Stephanstrasse 9a, 04103 Leipzig, Germany; International Max Planck Research School NeuroCom, Leipzig, Germany.
Objective:
Despite the increasing popularity of neurofeedback (NF), aiming at voluntary modulation of dysfunctional prefrontal cortex (PFC) signals in the treatment of binge-eating disorder (BED) and/or overweight, mechanisms remain poorly understood.
Methods:
Based on a randomized-controlled trial offering 12 food-specific real-time functional near-infrared spectroscopy (rtfNIRS)-NF sessions to participants with BED (n = 22), this preregistered study examined (1) online regulation success as predictor for offline regulation success, defined by PFC signals during regulation versus watch, and subjective regulation success, and (2) changes in loss of control (LOC) eating after vs. before and across 12 rtfNIRS-NF-sessions.
Results:
Higher online regulation success expectedly predicted better subjective, but worse offline regulation success. LOC eating decreased after vs. before, but not over rtfNIRS-NF-sessions, and was not associated with subjective or offline regulation success.
Conclusions:
The association between online and subjective regulation success confirmed the presumed mechanism of operant conditioning underlying rtfNIRS-NF-learning. The contrary association between online and offline regulation indicated differential PFC involvement upon subtraction of automatic food-specific responses from regulation signals for offline success. Decreased LOC eating after food-specific rtfNIRS-NF-sessions suggested the potential of NF in BED treatment.
Significance:
Results may guide the optimization of future NF studies in larger samples with BED.

