Prof. Dr. Anne Roefs, Professor of Psychology and Neuroscience of Abnormal Eating
Anne’s research focuses mainly on the neuroscience and psychology of overweight, obesity and eating disorders. Funded by an NWO-VIDI grant, she examined how dietary restraint, bodyweight, and mindset influence neural representations of food, using fMRI. In 2023, she started a new project, funded by an NWO-VICI grant, focused on personalized understanding and treatment of overweight and obesity. The network approach to psychopathology features centrally in her recent research as well, and she is a co-PI in the Gravitation Project New Science of Mental Disorders (www.nsmd.eu). Life is not only work, and she loves a healthy lifestyle. As often as time allows, she is training on a tennis court or running, and recently started teaching kids the fundamentals of tennis.
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Publications that I’m most proud of
It is a matter of perspective: Attentional focus rather than dietary restraint drives brain response to food stimuli. NeuroImage, 2023.
We showed that the level of neural activity in response to food stimuli was affected by a hedonic versus a neutral and health mindset, and not proportionate to food reward value. Food palatability and caloric content could be decoded from multivoxel patterns of neural activity. This study challenges dominant theorizing in the field by providing evidence for a central role of motivational states in neural food-reward processing.
The dynamic nature of food reward processing in the brain. Current Opinion in Clinical Nutrition and Metabolic Care, 2018.
This literature review challenges the dominant view in the literature that increased neural reactivity to high-caloric food in the reward-circuitry of the brain is a stable and specific characteristic of obese people. We propose that the neural response to food stimuli is dynamic, and in synchrony with someone’s current motivational and cognitive state.
Machine learning techniques in eating behavior e-coaching: Balancing between generalization and personalization. Personal and Ubiquitous Computing, 2017.
We presented a framework of how machine learning techniques can be used to fully exploit EMA-data, by analyzing individual states of a person (emotions, location, etc.) and assessing their impact on (un)healthy eating. A classification-algorithm was used to warn people prior to a possible unhealthy eating-event, and a clustering-algorithm was used for profiling participants.
