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Extensive neuroimaging research on brain structural and functional correlates of suicide has produced inconsistent results. Despite increasing recognition that damage in multiple different brain locations that causes the same symptom can map to a common brain network, there is still a paucity of research investigating network localization of suicide. To clarify this issue, we initially identified brain structural and functional damage locations in relation to suicide from 63 published studies with 2135 suicidal and 2606 nonsuicidal individuals. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 suicide brain damage networks corresponding to different imaging modalities. The suicide gray matter volume damage network comprised widely distributed brain areas primarily involving the dorsal default mode, basal ganglia, and anterior salience networks. The suicide task-induced activation damage network was similar to but less extensive than the gray matter volume damage network, predominantly implicating the same canonical networks. The suicide resting-state activity damage network manifested as a localized set of brain regions encompassing the orbitofrontal cortex and middle cingulate cortex. Our findings not only may help reconcile prior heterogeneous neuroimaging results, but also may provide insights into the neurobiological mechanisms of suicide from a network perspective, which may ultimately inform more targeted and effective strategies to prevent suicide. [ABSTRACT FROM AUTHOR]

The precuneus shows considerable heterogeneity in multiple dimensions including anatomy, function, and involvement in brain disorders. Leveraging the state-of-the-art functional gradient approach, we aimed to investigate the hierarchical organization of the precuneus, which may hold promise for a unified understanding of precuneus heterogeneity. Resting-state functional MRI data from 793 healthy individuals were used to discover and validate functional gradients of the precuneus, which were calculated based on the voxel-wise precuneus-to-cerebrum functional connectivity patterns. Then, we further explored the potential relationships of the precuneus functional gradients with cortical morphology, intrinsic geometry, canonical functional networks, and behavioral domains. We found that the precuneus principal and secondary gradients showed dorsoanterior-ventral and ventroposterior-dorsal organizations, respectively. Concurrently, the principal gradient was associated with cortical morphology, and both the principal and secondary gradients showed geometric distance dependence. Importantly, precuneus functional subdivisions corresponding to canonical functional networks (behavioral domains) were distributed along both gradients in a hierarchical manner, i.e., from the sensorimotor network (somatic movement and sensation) at one extreme to the default mode network (abstract cognitive functions) at the other extreme for the principal gradient and from the visual network (vision) at one end to the dorsal attention network (top-down control of attention) at the other end for the secondary gradient. These findings suggest that the precuneus functional gradients may provide mechanistic insights into the multifaceted nature of precuneus heterogeneity. [ABSTRACT FROM AUTHOR]

• The alterations in dFC and SC networks unveil distinct characteristics associated with cLBP. • The enhanced connectivity between the FPN, SMN and thalamus signifies the most relevant changes in brain networks associated with cLBP. • The integration of different features effectively enhances the classification accuracy between cLBP patients and HC. The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Neuroimaging research has revealed significant changes in brain structure and function in patients with cervical spondylotic myelopathy(CSM). The thalamus plays a crucial role in this process, although its mechanisms of action remain incompletely understood. This study aimed to investigate whether spinal cord compression leads to alterations in the functional connectivity between the thalamus and the cerebral cortex, and to determine if such changes are associated with structural and functional remodeling of the brain in patients with CSM, and to identify potential neuroimaging biomarkers for classification. The study included 40 patients with CSM and 34 healthy controls(HCs) who underwent resting-state functional magnetic resonance imaging(fMRI) and structural MRI scans. Brain structural and functional metrics were quantified using functional connectivity(FC), fractional amplitude of low-frequency fluctuations(fALFF), surface-based morphometry(SBM), and independent component analysis(ICA) based on functional and structural MRI. Patients with CSM exhibited significantly reduced fALFF in the bilateral lateral lingual gyrus, bilateral calcarine fissure, left precentral gyrus and postcentral gyrus, left middle and superior occipital gyrus, left superior marginal gyrus, left inferior parietal gyrus, and right Rolandic operculum. ICA results revealed weakened functional connectivity between the sensorimotor network (SMN) and the left and right frontoparietal network(FPN), and lateral visual network (lVN), along with decreased connectivity between lVN and rFPN, and increased connectivity between lFPN and rFPN. Patients with CSM also had decreased sulcus depth in the bilateral insula, left precentral and postcentral gyrus, and right lingual gyrus and calcarine fissure. Furthermore, cervical spondylotic myelopathy patients showed decreased functional connectivity between the left ventral posterolateral nucleus (VPL) of the thalamus and the right middle occipital gyrus (MOG). Finally,multimodal neuroimaging with support vector machine(SVM) classified patients with CSM and healthy controls with 86.00% accuracy. Our study revealed that the decrease in functional connectivity between the thalamus and cortex mediated by spinal cord compression leads to structural and functional reorganization of the cortex. Features based on neuroimaging markers have the potential to become neuroimaging biomarkers for CSM. • CSM patients exhibit significant alterations in both brain structure and function. • Reduced thalamo-cortical synchronization in CSM patients mediates brain remodeling. • Neuroimaging metrics with SVM can accurately classify CSM patients and healthy controls. [ABSTRACT FROM AUTHOR]