Knowledge is growing on the essential role of neural circuits involved in aberrant cognitive control and reward sensitivity for the onset and maintenance of binge eating.

To investigate how the brain's reward (bottom-up) and inhibition control (top-down) systems potentially and dynamically interact to contribute to subclinical binge eating.

Functional magnetic resonance imaging data were acquired from 30 binge eaters and 29 controls while participants performed a food reward Go/NoGo task. Dynamic causal modelling with the parametric empirical Bayes framework, a novel brain connectivity technique, was used to examine between-group differences in the directional influence between reward and executive control regions. We explored the proximal risk factors for binge eating and its neural basis, and assessed the predictive ability of neural indices on future disordered eating and body weight.

The binge eating group relative to controls displayed fewer reward-inhibition undirectional and directional synchronisations (i.e. medial orbitofrontal cortex [mOFC]–superior parietal gyrus [SPG] connectivity, mOFC → SPG excitatory connectivity) during food reward_nogo condition. Trait impulsivity is a key proximal factor that could weaken the mOFC–SPG connectivity and exacerbate binge eating. Crucially, this core mOFC–SPG connectivity successfully predicted binge eating frequency 6 months later.

These findings point to a particularly important role of the bottom-up interactions between cortical reward and frontoparietal control circuits in subclinical binge eating, which offers novel insights into the neural hierarchical mechanisms underlying problematic eating, and may have implications for the early identification of individuals suffering from strong binge eating-associated symptomatology in the general population.

Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).

CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.

The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.

These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.