Persistent auditory verbal hallucinations (p-AVH) in schizophrenia are often resistant to treatment and linked to dysconnectivity within the cortical–cerebellar–subcortical network. This study investigated whether continuous theta burst stimulation (cTBS) over the left cerebellum alleviates p-AVH and whether treatment-related functional connectivity (FC) changes predict symptom improvement.

Thirty schizophrenia patients with p-AVH received 14-days of left cerebellar cTBS. Whole-brain FC changes (post- vs. pretreatment) were used as neuroimaging features, and the reduction rate of PSYRATS-AH (RR_PSYRATS-AH) at 2 weeks served as the clinical feature. Connectome-based predictive modeling (CPM) was applied, controlling for age, sex, and framewise displacement.

cTBS significantly reduced PSYRATS-AH and PANSS P3 scores (all p < 0.05) and improved verbal, attention, and delayed memory performances. CPM revealed that FC changes predicted RR_PSYRATS-AH (r = −0.269, p = 0.047), mainly involving cerebellar–prefrontal, cerebellar–temporal, and cerebellar–subcortical circuits.

Left cerebellar cTBS may help alleviate auditory hallucinations and improve cognitive performance in schizophrenia, likely by modulating cortical–cerebellar–subcortical–cortical connectivity, highlighting the cerebellum as a promising target for neuromodulation in treatment-resistant hallucinations.

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.