Snack food as a modulator of human resting-state functional connectivity

Mendez-Torrijos, Andrea; Kreitz, Silke; Ivan, Claudiu; Konerth, Laura; Rösch, Julie; Pischetsrieder, Monika; Moll, Gunther H.; Kratz, Oliver; Dörfler, Arnd; Horndasch, Stefanie; Heß, Andreas

doi:10.1017/s1092852918000767

Cited by 2 publications

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References 84 publications

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“…The lateral occipital cortex (LOC) is part of the visual association cortex and is activated in response to the perception of emotionally salient stimuli, such as food, which is thought to be a correlate of heightened attention (Killgore and Yurgelun-Todd, 2007; van der Laan et al, 2011). For instance, a recent rs-fMRI study using SVM on graph theory analysis indicates that the LOC is partly important for classification between high-caloric (potato chips) vs. low-caloric (zucchini) food ingestion on the brain of healthy subjects (Mendez-Torrijos et al, 2018). Furthermore, it has been suggested that the processing of visual salience of a stimulus depends on the affective state of the individual and the motivational value of a stimulus (Killgore and Yurgelun-Todd, 2007).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety)

Al-Zubaidi

Mertins

Heldmann

et al. 2019

Front. Hum. Neurosci.

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Objective Resting-state functional magnetic resonance imaging (rs-fMRI) has become an essential measure to investigate the human brain’s spontaneous activity and intrinsic functional connectivity. Several studies including our own previous work have shown that the brain controls the regulation of energy expenditure and food intake behavior. Accordingly, we expected different metabolic states to influence connectivity and activity patterns in neuronal networks. Methods The influence of hunger and satiety on rs-fMRI was investigated using three connectivity models (local connectivity, global connectivity and amplitude rs-fMRI signals). After extracting the connectivity parameters of 90 brain regions for each model, we used sequential forward floating selection strategy in conjunction with a linear support vector machine classifier and permutation tests to reveal which connectivity model differentiates best between metabolic states (hunger vs. satiety). Results We found that the amplitude of rs-fMRI signals is slightly more precise than local and global connectivity models in order to detect resting brain changes during hunger and satiety with a classification accuracy of 81%. Conclusion The amplitude of rs-fMRI signals serves as a suitable basis for machine learning based classification of brain activity. This opens up the possibility to apply this combination of algorithms to similar research questions, such as the characterization of brain states (e.g., sleep stages) or disease conditions (e.g., Alzheimer’s disease, minimal cognitive impairment).

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