Functional magnetic resonance data acquired in a task-absent condition (“resting state”) require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular “betweenness centrality” - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.

We present a publicly available dataset of 227 healthy participants comprising a young (N=153, 25.1±3.1 years, range 20–35 years, 45 female) and an elderly group (N=74, 67.6±4.7 years, range 59–77 years, 37 female) acquired cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-body-emotion interactions. During a two-day assessment, participants completed MRI at 3 Tesla (resting-state fMRI, quantitative T1 (MP2RAGE), T2-weighted, FLAIR, SWI/QSM, DWI) and a 62-channel EEG experiment at rest. During task-free resting-state fMRI, cardiovascular measures (blood pressure, heart rate, pulse, respiration) were continuously acquired. Anthropometrics, blood samples, and urine drug tests were obtained. Psychiatric symptoms were identified with Standardized Clinical Interview for DSM IV (SCID-I), Hamilton Depression Scale, and Borderline Symptoms List. Psychological assessment comprised 6 cognitive tests as well as 21 questionnaires related to emotional behavior, personality traits and tendencies, eating behavior, and addictive behavior. We provide information on study design, methods, and details of the data. This dataset is part of the larger MPI Leipzig Mind-Brain-Body database.

In this study, we investigate the brain mechanisms of the conscious regulation of the desire for food using functional magnetic resonance imaging. Further, we examine associations between hemodynamic responses and participants’ cognitive restraint of eating (CRE), as well as their susceptibility to uncontrolled eating. Seventeen non-vegetarian, right-handed, female Caucasian participants (age: 20–30 years, mean 25.3 years±3.1 s.d.; BMI: 20.2–31.2 kg m⁻², mean 25.1±3.5 s.d.). During scanning, our participants viewed pictures of food items they had pre-rated according to tastiness and healthiness. Participants were either allowed to admit to the desire for the food (ADMIT) or they were instructed to downregulate their desire using a cognitive reappraisal strategy, that is, thinking of negative long-term health-related and social consequences (REGULATE). Comparing the hemodynamic responses of the REGULATE with the ADMIT condition, we observed robust activation in the dorsolateral prefrontal cortex (DLPFC), the pre-supplementary motor area, the inferior frontal gyrus (IFG), the dorsal striatum (DS), the bilateral orbitofrontal cortex (OFC), the anterior insula and the temporo-parietal junction (TPJ). Activation in the DLPFC and the DS strongly correlated with the degree of dietary restraint under both conditions. Cortical activation in the DLPFC, the pre-supplementary motor area and the inferior frontal gyrus (IFG) are known to underpin top-down control, inhibition of learned associations and pre-potent responses. The observed hemodynamic responses in the lateral OFC, the DS, the anterior insula and the TPJ support the notion of reward valuation and integration, interoceptive awareness, and self-reflection as key processes during active regulation of desire for food. In conclusion, an active reappraisal of unhealthy food recruits the brain's valuation system in combination with prefrontal cognitive control areas associated with response inhibition. The correlations between brain responses and CRE suggest that individuals with increased cognitive restraint show an automatic predisposition to regulate the hedonic aspects of food stimuli. This cognitive control might be necessary to counterbalance a lack of homeostatic mechanisms.

The Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994) is often used to assess decision-making deficits in clinical populations. The interpretation of the results hinges on 3 key assumptions: (a) healthy participants learn to prefer the good options over the bad options; (b) healthy participants show homogeneous choice behavior; and (c) healthy participants first explore the different options and then exploit the most profitable ones. Here we test these assumptions using 2 extensive literature reviews and analysis of 8 data sets. The results show that all 3 assumptions may be invalid; that is, (a) healthy participants often prefer decks with infrequent losses; (b) healthy participants show idiosyncratic choice behavior; and (c) healthy participants do not show a systematic decrease in the number of switches across trials. Our findings question the prevailing interpretation of IGT data and suggest that, in future applications of the IGT, key assumptions about performance of healthy participants warrant close scrutiny.