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Personality traits capture stable patterns of behavior and thought, and neurobiological correlates were identified in structural and functional brain networks. Here, we investigate whether the coupling between structural and functional brain networks (SC‐FC coupling), during resting state and seven tasks of varying trait‐relevance, is associated with individual differences in the Big Five personality traits. We used diffusion‐weighted and functional magnetic resonance imaging from 764 participants of the Human Connectome Project and modelled individual differences in SC‐FC coupling with similarity and communication measures. These measures approximate functional interactions unfolding on top of the structural connectome and were set in relation to individual variations in personality traits. Small but significant associations in the main analysis were only observed during trait‐relevant tasks: for agreeableness during social cognition, and conscientiousness could be predicted from task‐general coupling patterns. We conclude that optimizing trait‐relevance of tasks during neuroscientific measurement presents a promising means to increase effect sizes in studies on brain‐behavior associations. Key Points Significant relationships between the Big Five personality traits and SC‐FC coupling were absent during resting state but emerge during trait‐relevant tasks. Carefully designed tasks may amplify individual differences and thereby enhance the detectability of trait‐related neural characteristics. Our study exemplifies how established behavioral personality theories can be transferred to the neural level. Significant associations between the Big Five personality traits and structural‐functional brain network coupling are absent during resting state but emerge during trait‐relevant tasks. Carefully designed trait‐relevant tasks may amplify individual differences in trait‐related neural characteristics and thereby enhance their detectability. This exemplifies how established behavioral personality theories can be transferred to the neural level.

Previous studies on brain functional connectivity networks in children have mainly focused on changes in function in specific brain regions, as opposed to whole brain connectivity in healthy children. By analyzing the independent components of activation and network connectivity between brain regions, we examined brain activity status and development trends in children aged 3 and 5 years. These data could provide a reference for brain function rehabilitation in children with illness or abnormal function. We acquired functional magnetic resonance images from 15 3-year-old children and 15 5-year-old children under natural sleep conditions. The participants were recruited from five kindergartens in the Nanshan District of Shenzhen City, China. The parents of the participants signed an informed consent form with the premise that they had been fully informed regarding the experimental protocol. We used masked independent component analysis and BrainNet Viewer software to explore the independent components of the brain and correlation connections between brain regions. We identified seven independent components in the two groups of children, including the executive control network, the dorsal attention network, the default mode network, the left frontoparietal network, the right frontoparietal network, the salience network, and the motor network. In the default mode network, the posterior cingulate cortex, medial frontal gyrus, and inferior parietal lobule were activated in both 3- and 5-year-old children, supporting the \"three-brain region theory\" of the default mode network. In the frontoparietal network, the frontal and parietal gyri were activated in the two groups of children, and functional connectivity was strengthened in 5-year-olds compared with 3-year-olds, although the nodes and network connections were not yet mature. The high-correlation network connections in the default mode networks and dorsal attention networks had been significantly strengthened in 5-year-olds vs. 3-year-olds. Further, the salience network in the 3-year-old children included an activated insula/inferior frontal gyrus-anterior cingulate cortex network circuit and an activated thalamus-parahippocampal-posterior cingulate cortex-subcortical regions network circuit. By the age of 5 years, nodes and high-correlation network connections (edges) were reduced in the salience network. Overall, activation of the dorsal attention network, default mode network, left frontoparietal network, and right frontoparietal network increased (the volume of activation increased, the signals strengthened, and the high-correlation connections increased and strengthened) in 5-year-olds compared with 3-year-olds, but activation in some brain nodes weakened or disappeared in the salience network, and the network connections (edges) were reduced. Between the ages of 3 and 5 years, we observed a tendency for function in some brain regions to be strengthened and for the generalization of activation to be reduced, indicating that specialization begins to develop at this time. The study protocol was approved by the local ethics committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences in China with approval No. SIAT-IRB-131115-H0075 on November 15, 2013.

Stimulation at specific acupoints can activate cortical regions in human subjects. Previous studies have mainly focused on a single brain region. However, the brain is a network and many brain regions participate in the same task. The study of a single brain region alone cannot clearly explain any brain-related issues. Therefore, for the present study, magnetic stimulation was used to stimulate the Neiguan (PC6) acupoint, and 32-channel electroencephalography data were recorded before and after stimulation. Brain functional networks were constructed based on electroencephalography data to determine the relationship between magnetic stimulation at the PC6 acupoint and cortical excitability. Results indicated that magnetic stimulation at the PC6 acupoint increased connections between cerebral cortex regions.

SignificanceMastering diverse cognitive tasks is crucial for humans. We study how the brain’s functional organization at rest is configured to support diverse cognitive phenotypes. Emphasizing the multilevel, hierarchical modular structure of brain’s functional connectivity to derive eigenmode-based measures, we demonstrate that the resting brain’s functional organization in healthy young adults is configured to maintain a balance between network segregation and integration. This functional balance is associated with better memory. Furthermore, brains tending toward stronger segregation versus integration foster different cognitive abilities. Thus, the segregation–integration balance empowers the brain to support diverse cognitive abilities. These findings yield high potential to understand the role of whole-brain resting state dynamics in human cognition and to develop neural biomarkers of atypical cognition. Diverse cognitive processes set different demands on locally segregated and globally integrated brain activity. However, it remains an open question how resting brains configure their functional organization to balance the demands on network segregation and integration to best serve cognition. Here we use an eigenmode-based approach to identify hierarchical modules in functional brain networks and quantify the functional balance between network segregation and integration. In a large sample of healthy young adults (n = 991), we combine the whole-brain resting state functional magnetic resonance imaging (fMRI) data with a mean-filed model on the structural network derived from diffusion tensor imaging and demonstrate that resting brain networks are on average close to a balanced state. This state allows for a balanced time dwelling at segregated and integrated configurations and highly flexible switching between them. Furthermore, we employ structural equation modeling to estimate general and domain-specific cognitive phenotypes from nine tasks and demonstrate that network segregation, integration, and their balance in resting brains predict individual differences in diverse cognitive phenotypes. More specifically, stronger integration is associated with better general cognitive ability, stronger segregation fosters crystallized intelligence and processing speed, and an individual’s tendency toward balance supports better memory. Our findings provide a comprehensive and deep understanding of the brain’s functioning principles in supporting diverse functional demands and cognitive abilities and advance modern network neuroscience theories of human cognition.

The human brain develops rapidly in the first postnatal year, in which rewired functional brain networks could shape later behavioral and cognitive performance. Resting-state functional magnetic resonances imaging (rs-fMRI) and complex network analysis have been widely used for characterizing the developmental brain functional connectome. Yet, such studies focusing on the first year of postnatal life are still very limited. Leveraging normally developing longitudinal infant rs-fMRI scans from neonate to one year of age, we investigated how brain functional networks develop at a fine temporal scale (every 3 months). Considering challenges in the infant fMRI-based network analysis, we developed a novel algorithm to construct the robust, temporally consistent and modular structure augmented group-level network based on which functional modules were detected at each age. Our study reveals that the brain functional network is gradually subdivided into an increasing number of functional modules accompanied by the strengthened intra- and inter-modular connectivities. Based on the developing modules, we found connector hubs (the high-centrality regions connecting different modules) emerging and increasing, while provincial hubs (the high-centrality regions connecting regions in the same module) diminishing. Further region-wise longitudinal analysis validates that different hubs have distinct developmental trajectories of the intra- and inter-modular connections suggesting different types of role transitions in network, such as non-hubs to hubs or provincial hubs to connector hubs et al. All findings indicate that functional segregation and integration are both increased in the first year of postnatal life. The module reorganization and hub transition lead to more efficient brain networks, featuring increasingly segregated modular structure and more connector hubs. This study provides the first comprehensive report of the development of functional brain networks at a 3-month interval throughout the first postnatal year of life, which provides essential information to the future neurodevelopmental and developmental disorder studies. ∙A modularity and hub study at every 3 months in the first postnatal year∙Brains functional networks are gradually subdivided in the first postnatal year∙Connector hubs are spatially expanded, whereas provincial hubs are shrinking∙Different regions have distinct developmental trajectories toward hubs

BackgroundThe potential negative influences of short video platforms (SVPs) usage on mental health have been attracting increasing attention in recent years. This study aimed to investigate the possible effects of SVP usage on brain functions using the resting-state functional magnetic resonance imaging (fMRI) methods.MethodsResting-state fMRI data were acquired from a total of 55 young healthy adults. Based on self-reported daily usage time of SVPs, these participants were divided into a lower SVP usage (SVP-) group (< 1 h per day, n = 20) and a higher SVP usage (SVP+) group (≥1 h per day, n = 35). Between-group comparisons of functional brain measures were performed across multiple spatial levels.ResultsAt the single-edge level, the SVP + group showed significantly increased functional connectivity (FC) across many edges linking most major brain networks, including sensorimotor, visual, auditory, subcortical, default-mode, attention, and cingulo-opercular networks. Network-level analyses confirmed this widespread hyperconnectivity, with particularly robust increases within sensorimotor, auditory, subcortical, and cingulo-opercular networks after multiple comparisons correction. Voxel-wise analyses revealed higher fractional amplitude of low-frequency fluctuations (fALFF) in the left precentral gyrus and lower fALFF in the right frontal lobe in the SVP + group. Global topological analysis indicated that the SVP + group had significantly higher global efficiency, local efficiency, and clustering coefficient, as well as lower characteristic path length, suggesting an altered network topology.ConclusionThis multi-level fMRI study suggests that a relatively higher-intensity SVP use is associated with an altered pattern of brain functional organization, characterized by widespread hyperconnectivity across most major brain networks, localized spontaneous activity alterations in sensorimotor regions, and an altered topology at the global level. These findings highlight the importance of considering potential impacts of SVP usage on brain functioning, and calls for future larger-sample and longitudinal studies to further understand such relationships.

•Multiscale functional connectivity pattern of the aging brain were learned from a large-scale multisite fMRI datasets.•A machine learning model built on the multiscale functional connectivity measures achieved accurate brain age prediction.•Functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction.•Data harmonization significantly improved the brain age prediction performance. To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3 T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.

Music can effectively influence human emotions, with different melodies and rhythms eliciting varying emotional responses. Among these, tempo is one of the most important parameters affecting emotions. This study explores the impact of music tempo on emotional states and the associated brain functional networks. A total of 26 participants without any history of neurological or psychiatric disorders and music training took part in the experiment, using classical piano music clips at different tempi (56, 106, 156 bpm) as stimuli. The study was conducted using emotional scales and electroencephalogram (EEG) analysis. The results showed that the valence level of emotions significantly increased with music tempo, while the arousal level exhibited a “V” shape relationship. EEG analysis revealed significant changes in brainwave signals across different frequency bands under different tempi. For instance, slow tempo induced higher Theta and Alpha power in the frontal region, while fast tempo increased Beta and Gamma band power. Moreover, fast tempo enhanced the average connectivity strength in the frontal, temporal, and occipital regions, and increased phase synchrony value (PLV) between the frontal and parietal regions. However, slow tempo improves PLV between the occipital and parietal regions. The findings of this study elucidate the effects of music tempo on the brain functional networks related to emotion regulation, providing a theoretical basis for music-assisted diagnosis and treatment of mood disorders. Furthermore, these results suggest potential applications in emotion robotics, emotion-based human-computer interaction, and emotion-based intelligent control.

Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold‐based geometric neural network for functional brain networks (called “Geo‐Net4Net” for short) to learn the intrinsic low‐dimensional feature representations of resting‐state brain networks on the Riemannian manifold. This tool allows us to answer the scientific question of how the spontaneous fluctuation of FC supports behavior and cognition. We deploy a set of positive maps and rectified linear unit (ReLU) layers to uncover the intrinsic low‐dimensional feature representations of functional brain networks on the Riemannian manifold taking advantage of the symmetric positive‐definite (SPD) form of the correlation matrices. Due to the lack of well‐defined ground truth in the resting state, existing learning‐based methods are limited to unsupervised methodologies. To go beyond this boundary, we propose to self‐supervise the feature representation learning of resting‐state functional networks by leveraging the task‐based counterparts occurring before and after the underlying resting state. With this extra heuristic, our Geo‐Net4Net allows us to establish a more reasonable understanding of resting‐state FCs by capturing the geometric patterns (aka. spectral/shape signature) associated with resting states on the Riemannian manifold. We have conducted extensive experiments on both simulated data and task‐based functional resonance magnetic imaging (fMRI) data from the Human Connectome Project (HCP) database, where our Geo‐Net4Net not only achieves more accurate change detection results than other state‐of‐the‐art counterpart methods but also yields ubiquitous geometric patterns that manifest putative insights into brain function. Learning low‐dimensional feature signatures of resting‐state brain network on Riemannian manifold. Capturing the geometric patterns manifested in evolving functional fluctuations in resting state. Answering the question of how the spontaneous fluctuation of FC supports behavior and cognition.