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.

Mapping Gilles de la Tourette syndrome through the distress and relief associated with tic-related behaviors: an fMRI study.

Personal distress associated with tic urges or inhibition and relief associated with tic production are defining features of the personal experience in Gilles de la Tourette syndrome (GTS). These affective phenomena have not been studied using fMRI, hindering our understanding of GTS pathophysiology and possible treatments. Here, we present a novel cross-sectional fMRI study designed to map tic-related phenomenology using distress and relief as predicting variables.

How images of food become cravingly salient in obesity.

Objective: This case-control study was aimed at testing two main hypotheses: (i) obesity is characterized by neurofunctional alterations within the mesocorticolimbic reward system, a brain network originating from the midbrain ventral tegmental area (VTA); and (ii) these alterations are associated with a bias for food-related stimuli and craving.

Methods: Normal-weight individuals and individuals with obesity underwent a resting-state functional magnetic resonance imaging scan and the assessment of impulsivity, food craving, appetite, and implicit bias for food and non-food stimuli. The VTA was used as a seed to map, for each participant, the strength of its functional connections with the rest of the brain.

Neural structural abnormalities behind altered brain activation in obesity: Evidence from meta-analyses of brain activation and morphometric data.

Obesity represents a risk factor for disability with a major bearing on life expectancy. Neuroimaging techniques are contributing to clarify its neurobiological underpinnings. Here, we explored whether structural brain abnormalities might accompany altered brain activations in obesity.

Repetitive deep TMS for the reduction of body weight: Bimodal effect on the functional brain connectivity in "diabesity".

Background And Aims: Deep repetitive Transcranial Magnetic Stimulation (deep rTMS) over the bilateral insula and prefrontal cortex (PFC) can promote weight-loss in obesity, preventing cardiometabolic complications as Type 2 Diabetes (T2D). To investigate the changes in the functional brain integration after dTMS, we conducted a resting-state functional connectivity (rsFC) study in obesity.

Methods And Results: This preliminary study was designed as a randomized, double-blind, sham-controlled study: 9 participants were treated with high-frequency stimulation (realTMS group), 8 were sham-treated (shamTMS group).

Altered sense of agency in Gilles de la Tourette syndrome: behavioural, clinical and functional magnetic resonance imaging findings.

Current neurocognitive models of motor control postulate that accurate action monitoring is crucial for a normal experience of agency-the ability to attribute the authorship of our actions and their consequences to ourselves. Recent studies demonstrated that action monitoring is impaired in Gilles de la Tourette syndrome, a movement disorder characterized by motor and vocal tics. It follows that Tourette syndrome patients may suffer from a perturbed sense of agency, the hypothesis tested in this study.

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 "cluster composition analysis," 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.