Trait food craving predicts functional connectivity between dopaminergic midbrain and the fusiform food area during eating imagery

Neurofunctional coupling between the dopaminergic midbrain (i.e., ventral tegmental area, VTA) and higher-order visual regions may contribute to food craving, leading to the onset or maintenance of obesity. We recently showed that the VTA resting-state functional connectivity with the occipitotemporal cortex, at the level of the fusiform gyrus (FFG), was specifically associated with trait food craving and the implicit bias for food images, suggesting that VTA-FFG connectivity may reflect the association between the visual representations of food and its motivational properties. To further test this hypothesis, this time we studied task-based functional connectivity in twenty-eight healthy-weight participants while imagining eating their most liked high-calorie (HC) or least liked low-calorie food (LC) or drinking water (control condition). Trait food craving scores were used to predict changes in task-based functional connectivity of the VTA during imagery of HC compared to LC foods (relative to the control condition). Trait food craving was positively associated with the functional connectivity of the VTA with the left FFG: people with higher trait food craving scores show stronger VTA-FFG connectivity, specifically for the imagery of the liked HC foods. This association was not linked to the quality of imagery nor to state measures of craving, appetite, or thirst. These findings emphasize the contribution of the functional coupling between dopaminergic midbrain and higher-order visual regions to food craving, suggesting a neurofunctional mechanism by which the mental representations of the HC food we like can become much more salient if not irresistible

Neural circuits mediating food cue-reactivity: toward a new model shaping the interplay of internal and external factors

In the current opinion paper, we suggest a new perspective on the neuroimaging studies investigating the neural bases of food-cue regulation. Stemming from the evidence that different factors can modulate the neural response to drug cues (Jasinska et al. 2014, Neurosc. Biobeh. Rev.), we addressed the role of the major internal (e.g., biological, psychological) and external (e.g., environmental, cue-specific) factors that influence the neural reactivity to food-related cues, highlighting the brain circuits affected by the simple and interactive effects across different factors. The proposed model will be useful to frame new research ideas in which different contextual factors are modeled according to a factorial design, allowing to explore higher-order interactions at the neurofunctional level. Elucidating such interactions will not only lead to a better understanding of the neurocognitive bases of the normal and pathological eating behavior, but it will also pave the way to more effective, ecological, and tailor-made (behavioral or brain-centered) interventions, where internal and external contextual factors are incorporated in the treatment

Clustering the Brain With “CluB”: A New Toolbox for Quantitative Meta-Analysis of Neuroimaging Data

In this paper we describe and validate a new coordinate-based method for meta-analysis of neuroimaging data based on an optimized hierarchical clustering algorithm: CluB (Clustering the Brain). The CluB toolbox permits both to extract a set of spatially coherent clusters of activations from a database of stereotactic coordinates, and to explore each single cluster of activation for its composition according to the cognitive dimensions of interest. This last step, called \u201ccluster composition analysis,\u201d permits to explore neurocognitive effects by adopting a factorial-design logic and by testing the working hypotheses using either asymptotic tests, or exact tests either in a classic inference, or in a Bayesian-like context. To perform our validation study, we selected the fMRI data from 24 normal controls involved in a reading task. We run a standard random-effects second level group analysis to obtain a \u201cGold Standard\u201d of reference. In a second step, the subject-specific reading effects (i.e., the linear t-contrast \u201creading > baseline\u201d) were extracted to obtain a coordinates-based database that was used to run a meta-analysis using both CluB and the popular Activation Likelihood Estimation method implemented in the software GingerALE. The results of the two meta-analyses were compared against the \u201cGold Standard\u201d to compute performance measures, i.e., sensitivity, specificity, and accuracy. The GingerALE method obtained a high level of accuracy (0.967) associated with a high sensitivity (0.728) and specificity (0.971). The CluB method obtained a similar level of accuracy (0.956) and specificity (0.969), notwithstanding a lower level of sensitivity (0.14) due to the lack of prior Gaussian transformation of the data. Finally, the two methods obtained a good-level of concordance (AC1 = 0.93). These results suggested that methods based on hierarchical clustering (and post-hoc statistics) and methods requiring prior Gaussian transformation of the data can be used as complementary tools, with the GingerALE method being optimal for neurofunctional mapping of pooled data according to simpler designs, and the CluB method being preferable to test more specific, and localized, neurocognitive hypotheses according to factorial designs