Background and Hypothesis: Neuroimaging studies investigating the neural substrates of auditory verbal hallucinations (AVH) in schizophrenia have yielded mixed results, which may be reconciled by network localization. We sought to examine whether AVH-state and AVH-trait brain alterations in schizophrenia localize to common or distinct networks. Study Design: We initially identified AVH-state and AVH-trait brain alterations in schizophrenia reported in 48 previous studies. By integrating these affected brain locations with large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we then leveraged novel functional connectivity network mapping to construct AVH-state and AVH-trait dysfunctional networks. Study Results: The neuroanatomically heterogeneous AVH-state and AVH-trait brain alterations in schizophrenia localized to distinct and specific networks. The AVH-state dysfunctional network comprised a broadly distributed set of brain regions mainly involving the auditory, salience, basal ganglia, language, and sensorimotor networks. Contrastingly, the AVH-trait dysfunctional network manifested as a pattern of circumscribed brain regions principally implicating the caudate and inferior frontal gyrus. Additionally, the AVH-state dysfunctional network aligned with the neuromodulation targets for effective treatment of AVH, indicating possible clinical relevance. Conclusions: Apart from unifying the seemingly irreproducible neuroimaging results across prior AVH studies, our findings suggest different neural mechanisms underlying AVH state and trait in schizophrenia from a network perspective and more broadly may inform future neuromodulation treatment for AVH.

The occurrence of depression is higher in patients with chronic liver disease (CLD) than that in the general population. The mechanism described in previous studies mainly focused on inflammation and stress, which not only exists in CLD, but also emerges in common chronic diseases, leaving the specific mechanism unknown. This review was to summarize the prevalence and risk factors of depression in CLD including chronic hepatitis B (CH-B), chronic hepatitis C (CH-C), alcoholic liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD), and to point out the possible underlying mechanism of this potential link. Clarifying the origins of this common comorbidity (depression and CLD) may provide more information to understand both diseases.

Gut microbiota dysbiosis is linked to Alzheimer’s disease (AD), but our understanding of the molecular and neuropathological bases underlying such association remains fragmentary. Using 16S rDNA amplicon sequencing, untargeted metabolomics, and multi-modal magnetic resonance imaging, we examined group differences in gut microbiome, fecal metabolome, neuroimaging measures, and cognitive variables across 30 patients with AD, 75 individuals with mild cognitive impairment (MCI), and 61 healthy controls (HC). Furthermore, we assessed the associations between these multi-omics changes using correlation and mediation analyses. There were significant group differences in gut microbial composition, which were driven by 8 microbial taxa (e.g., Staphylococcus and Bacillus) exhibiting a progressive increase in relative abundance from HC to MCI to AD, and 2 taxa (e.g., Anaerostipes) showing a gradual decrease. 26 fecal metabolites (e.g., Arachidonic, Adrenic, and Lithocholic acids) exhibited a progressive increase from HC to MCI to AD. We also observed progressive gray matter atrophy in broadly distributed gray matter regions and gradual micro-structural integrity damage in widespread white matter tracts along the AD continuum. Integration of these multi-omics changes revealed significant associations between microbiota, metabolites, neuroimaging, and cognition. More importantly, we identified two potential mediation pathways: (1) microbiota → metabolites → neuroimaging → cognition, and (2) microbiota → metabolites → cognition. Aside from elucidating the underlying mechanism whereby gut microbiota dysbiosis is linked to AD, our findings may contribute to groundwork for future interventions targeting the microbiota-metabolites-brain-cognition pathways as a therapeutic strategy in the AD continuum.

Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual’s unique functional connectome may help advance the translation of ‘brain connectivity fingerprinting’ into real-world personality psychological settings.