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.

Structural and functional abnormalities of the cerebellum in schizophrenia have been reported. Most previous studies investigating resting-state functional connectivity (rsFC) have relied on a priori restrictions on seed regions or specific networks, which may bias observations. In this study, we aimed to elicit the connectivity alterations of the cerebellum in schizophrenia in a hypothesis-free approach. Ninety-five schizophrenia patients and 93 sex- and age-matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI). A voxel-wise data-driven method, resting-state functional connectivity density (rsFCD), was used to investigate cerebellar connectivity changes in schizophrenia patients. Regions with altered rsFCD were chosen as seeds to perform seed-based resting-state functional connectivity (rsFC) analyses. We found that schizophrenia patients exhibited decreased rsFCD in the right hemispheric VI; moreover, this cerebellar region showed increased rsFC with the prefrontal cortex and subcortical nuclei and decreased rsFC with the visual cortex and sensorimotor cortex. In addition, some rsFC changes were associated with positive symptoms. These findings suggest that abnormalities of the cerebellar hub and cerebellar-subcortical-cortical loop may be the underlying mechanisms of schizophrenia.

Network neuroscience has broadly conceptualized the functions of the brain as complex communication within and between large‐scale neural networks. Nevertheless, whether and how the gut microbiota influence functional network connectivity that in turn impact human behaviors has yet to be determined. We collected fecal samples from 157 healthy young adults and used 16S sequencing to assess gut microbial diversity and enterotypes. Large‐scale inter‐ and intranetwork functional connectivity was measured using a combination of resting‐state functional MRI data and independent component analysis. Sleep quality and core executive functions were also evaluated. Then, we tested for potential associations between gut microbiota, functional network connectivity and behaviors. We found significant associations of gut microbial diversity with internetwork functional connectivity between the executive control, default mode and sensorimotor systems, and intranetwork connectivity of the executive control system. Moreover, some internetwork functional connectivity mediated the relations of microbial diversity with sleep quality, working memory, and attention. In addition, there was a significant effect of enterotypes on intranetwork connectivity of the executive control system, which could mediate the link between enterotypes and executive function. Our findings not only may expand existing biological knowledge of the gut microbiota‐brain‐behavior relationships from the perspective of large‐scale functional network organization, but also may ultimately inform a translational conceptualization of how to improve sleep quality and executive functions through the regulation of gut microbiota.