The Effects of Age and Reading Experience on the Lifespan Neurodevelopment for Reading Comprehension.

Reading comprehension is a vital cognitive skill that individuals use throughout their lives. The neurodevelopment of reading comprehension across the lifespan, however, remains underresearched. Furthermore, factors such as maturation and experience significantly influence functional brain development. Given the complexity of reading comprehension, which incorporates lower-level word reading process and higher-level semantic integration process, our study aims to investigate how age and reading experience influence the neurobiology underpinning these two processes across the lifespan. fMRI data of 158 participants aged from 7 to 77 years were collected during a passive word viewing task and a sentence comprehension task to engage the lower- and higher-level processes, respectively. We found that the neurodevelopment of the lower-level process was primarily influenced by age, showing increased activation and connectivity with age in parieto-occipital and middle/inferior frontal lobes related to morphological-semantic mapping while decreased activation in the temporoparietal regions linked to phonological processing. However, the brain function of the higher-level process was primarily influenced by reading experience, exhibiting a greater reliance on the frontotemporal semantic network with enhanced sentence-level reading performance. Furthermore, reading experience did not significantly affect the brain function of children, but had a positive effect on young adults in the lower-level process and on middle-aged and older adults in the higher-level process. These findings indicate that the brain function for lower- and higher-level processes of reading comprehension is differently affected by maturation and reading experience, and the experience effect is contingent on age regarding the two processes.

Functionally and structurally distinct fusiform face area(s) in over 1000 participants.

The fusiform face area (FFA) is a widely studied region causally involved in face perception. Even though cognitive neuroscientists have been studying the FFA for over two decades, answers to foundational questions regarding the function, architecture, and connectivity of the FFA from a large (N>1000) group of participants are still lacking. To fill this gap in knowledge, we quantified these multimodal features of fusiform face-selective regions in 1053 participants in the Human Connectome Project. After manually defining over 4,000 fusiform face-selective regions, we report five main findings. First, 68.76% of hemispheres have two cortically separate regions (pFus-faces/FFA-1 and mFus-faces/FFA-2). Second, in 26.69% of hemispheres, pFus-faces/FFA-1 and mFus-faces/FFA-2 are spatially contiguous, yet are distinct based on functional, architectural, and connectivity metrics. Third, pFus-faces/FFA-1 is more face-selective than mFus-faces/FFA-2, and the two regions have distinct functional connectivity fingerprints. Fourth, pFus-faces/FFA-1 is cortically thinner and more heavily myelinated than mFus-faces/FFA-2. Fifth, face-selective patterns and functional connectivity fingerprints of each region are more similar in monozygotic than dizygotic twins and more so than architectural gradients. As we share our areal definitions with the field, future studies can explore how structural and functional features of these regions will inform theories regarding how visual categories are represented in the brain.

Human visual cortex is organized along two genetically opposed hierarchical gradients with unique developmental and evolutionary origins.

Human visual cortex is organized with striking consistency across individuals. While recent findings demonstrate an unexpected coupling between functional and cytoarchitectonic regions relative to the folding of human visual cortex, a unifying principle linking these anatomical and functional features of the cortex remains elusive. To fill this gap in knowledge, we combined independent and ground truth measurements of cytoarchitectonic regions and genetic tissue characterization within human occipitotemporal cortex. Using a data-driven approach, we examined whether differential gene expression among cytoarchitectonic areas could contribute to the arealization of occipitotemporal cortex into a hierarchy based on transcriptomics. This approach revealed two opposing gene expression gradients: one that contains a series of genes with expression magnitudes that ascend from posterior (e.g., areas human occipital [hOc]1, hOc2, hOc3, etc.) to anterior cytoarchitectonic areas (e.g., areas fusiform gyrus [FG]1-FG4) and another that contains a separate series of genes that show a descending gradient from posterior to anterior areas. Using data from the living human brain, we show that each of these gradients correlates strongly with variations in measures related to either thickness or myelination of cortex, respectively. We further reveal that these genetic gradients emerge along unique trajectories in human development: the ascending gradient is present at 10-12 gestational weeks, while the descending gradient emerges later (19-24 gestational weeks). Interestingly, it is not until early childhood (before 5 years of age) that the two expression gradients achieve their adult-like mean expression values. Additional analyses in nonhuman primates (NHPs) reveal that homologous genes do not generate the same ascending and descending expression gradients as in humans. We discuss these findings relative to previously proposed hierarchies based on functional and cytoarchitectonic features of visual cortex. Altogether, these findings bridge macroscopic features of human cytoarchitectonic areas in visual cortex with microscopic features of cellular organization and genetic expression, which, despite the complexity of this multiscale correspondence, can be described by a sparse subset (approximately 200) of genes. These findings help pinpoint the genes contributing to healthy cortical development and explicate the cortical biology distinguishing humans from other primates, as well as establishing essential groundwork for understanding future work linking genetic mutations with the function and development of the human brain.

Behavioral and neural correlates of social network size: The unique and common contributions of face recognition and extraversion.

OBJECTIVES: Humans are inherently social creatures and can gain advantages from larger network size. Researches have shown that different cognitive and personality factors may result in individual differences of social network size (SNS). Here, we focused on whether face recognition ability and extraversion were related to SNS and the neural basis underlying the relations. METHODS: Behaviorally, we adopted the face-inversion task, NEO personality inventory, and computerized SNS test to explore the relationships between face recognition, extraversion, and SNS. Neurally, we used resting state functional magnetic resonance imaging and fractional amplitude of low-frequency fluctuation (fALFF) analysis method to investigate the neural correlates of SNS and then revealed whether face recognition and extraversion were related to SNS relevant brain regions. RESULTS: We found that individuals with better face recognition ability and more extraverted personality had larger size of social network. In addition, we found that SNS was positively associated with the fALFF in the ventromedial prefrontal cortex (vmPFC), right superior temporal sulcus, and precuneus. Interestingly, the fALFF in the vmPFC significantly correlated with face recognition ability. CONCLUSIONS: Our study suggests that both face recognition and extraversion may be important correlates of SNS, and the underlying spontaneous neural substrates are partially dissociable.

Apparent thinning of human visual cortex during childhood is associated with myelination.

Human cortex appears to thin during childhood development. However, the underlying microstructural mechanisms are unknown. Using functional magnetic resonance imaging (fMRI), quantitative MRI (qMRI), and diffusion MRI (dMRI) in children and adults, we tested what quantitative changes occur to gray and white matter in ventral temporal cortex (VTC) from childhood to adulthood, and how these changes relate to cortical thinning. T1 relaxation time from qMRI and mean diffusivity (MD) from dMRI provide independent and complementary measurements of microstructural properties of gray and white matter tissue. In face- and character-selective regions in lateral VTC, T1 and MD decreased from age 5 to adulthood in mid and deep cortex, as well as in their adjacent white matter. T1 reduction also occurred longitudinally in children's brain regions. T1 and MD decreases 1) were consistent with tissue growth related to myelination, which we verified with adult histological myelin stains, and 2) were correlated with apparent cortical thinning. In contrast, in place-selective cortex in medial VTC, we found no development of T1 or MD after age 5, and thickness was related to cortical morphology. These findings suggest that lateral VTC likely becomes more myelinated from childhood to adulthood, affecting the contrast of MR images and, in turn, the apparent gray-white boundary. These findings are important because they suggest that VTC does not thin during childhood but instead gets more myelinated. Our data have broad ramifications for understanding both typical and atypical brain development using advanced in vivo quantitative measurements and clinical conditions implicating myelin.

Quantifying the variability of neural activation in working memory: A functional probabilistic atlas.

Working memory is a fundamental cognitive ability that allows the maintenance and manipulation of information for a brief period of time. Previous studies found a set of brain regions activated during working memory tasks, such as the prefrontal and parietal cortex. However, little is known about the variability of neural activation in working memory. Here, we used functional magnetic resonance imaging to quantify individual, hemispheric, and sex differences of working memory activation in a large cohort of healthy adults (N = 477). We delineated subject-specific activated regions in each individual, including the frontal pole, middle frontal gyrus, frontal eye field, superior parietal lobule, insular, precuneus, and anterior cingulate cortex. A functional probabilistic atlas was created to quantify individual variability in working memory regions. More than 90% of the participants activated all seven regions in both hemispheres, but the intersection of regions across participants was markedly less (50%), indicating significant individual differences in working memory activations. Moreover, we found hemispheric and sex differences in activation location, extent, and magnitude. Most activation regions were larger in the right than in the left hemisphere, but the magnitude of activation did not follow a similar pattern. Men showed more extensive and stronger activations than women. Taken together, our functional probabilistic atlas quantified variabilities of neural activation in working memory, providing a robust spatial reference for standardization of functional localization.

Brain Structure and Functional Connectivity Associated with Individual Differences in the Attentional Blink.

The attentional blink (AB) has been central in characterizing the limit of temporal attention and consciousness. The neural mechanism of the AB is still in hot debate. With a large sample size, we combined multiple behavioral tests, multimodal MRI measures, and transcranial magnetic stimulation to investigate the neural basis underlying the individual differences in the AB. We found that AB magnitude correlated with the executive control functioning of working memory (WM) in behavior, which was fully mediated by T1 performance. Structural variations in the right temporoparietal junction (rTPJ) and its intrinsic functional connectivity with the left inferior frontal junction (lIFJ) accounted for the individual differences in the AB, which was moderated by the executive control of working memory. Disrupting the function of the lIFJ attenuated the AB deficit. Our findings clarified the neural correlates of the individual differences in the AB and elucidated its relationship with the consolidation-driven inhibitory control process.

Multi-Item Discriminability Pattern to Faces in Developmental Prosopagnosia Reveals Distinct Mechanisms of Face Processing.

Previous studies have shown that individuals with developmental prosopagnosia (DP) show specific deficits in face processing. However, the mechanism underlying the deficits remains largely unknown. One hypothesis suggests that DP shares the same mechanism as normal population, though their faces processing is disproportionally impaired. An alternative hypothesis emphasizes a qualitatively different mechanism of DP processing faces. To test these hypotheses, we instructed DP and normal individuals to perceive faces and objects. Instead of calculating accuracy averaging across stimulus items, we used the discrimination accuracy for each item to construct a multi-item discriminability pattern. We found DP's discriminability pattern was less similar to that of normal individuals when perceiving faces than perceiving objects, suggesting that DP has qualitatively different mechanism in representing faces. A functional magnetic resonance imaging study was conducted to reveal the neural basis and found that multi-voxel activation patterns for faces in the right fusiform face area and occipital face area of DP were deviated away from the mean activation pattern of normal individuals. Further, the face representation was more heterogeneous in DP, suggesting that deficits of DP may come from multiple sources. In short, our study provides the first direct evidence that DP processes faces qualitatively different from normal population.

A probabilistic atlas of the human motion complex built from large-scale functional localizer data.

Accurate motion perception is critical to dealing with the changing dynamics of our visual world. A cluster known as the human MT+ complex (hMT+) has been identified as a core region involved in motion perception. Several atlases defined based on cytoarchitecture, retinotopy, connectivity, and multimodal features include homologs of the hMT+. However, an hMT+ atlas defined directly based on this region's response for motion is still lacking. Here, we identified the hMT+ based on motion responses from functional magnetic resonance imaging (fMRI) localizer data in 509 participants and then built a probabilistic atlas of the hMT+. As a result, four main findings were revealed. First, the hMT+ showed large interindividual variability across participants. Second, the atlases stabilized when the number of participants used to build the atlas was more than 100. Third, the functional hMT+ showed good agreement with the hMT+ atlases built based on cytoarchitecture, retinotopy, and connectivity, suggesting a good structural-functional correspondence. Fourth, tests on multiple fMRI data sets acquired from independent participants, imaging parameters and paradigms revealed that the functional hMT+ showed higher sensitivity than all other atlases in ROI analysis except that connectivity and multimodal hMT+ atlases in the left hemisphere could infrequently attain comparable sensitivity to the functional atlas. Taken together, our findings reveal the benefit of using large-scale functional localizer data to build a reliable and representative hMT+ atlas. Our atlas is freely available for download; it can be used to localize the hMT+ in individual participants when functional localizer data are not available.

The neural network for face recognition: Insights from an fMRI study on developmental prosopagnosia.

Face recognition is supported by collaborative work of multiple face-responsive regions in the brain. Based on findings from individuals with normal face recognition ability, a neural model has been proposed with the occipital face area (OFA), fusiform face area (FFA), and face-selective posterior superior temporal sulcus (pSTS) as the core face network (CFN) and the rest of the face-responsive regions as the extended face network (EFN). However, little is known about how these regions work collaboratively for face recognition in our daily life. Here we focused on individuals suffering developmental prosopagnosia (DP), a neurodevelopmental disorder specifically impairing face recognition, to shed light on the infrastructure of the neural model of face recognition. Specifically, we used a variant of global brain connectivity method to comprehensively explore resting-state functional connectivity (FC) among face-responsive regions in a large sample of DPs (N = 64). We found that both the FCs within the CFN and those between the CFN and EFN were largely reduced in DP. Importantly, the right OFA and FFA served as the dysconnectivity hubs within the CFN, i.e., FCs concerning these two regions within the CFN were largely disrupted. In addition, DPs' right FFA also showed reduced FCs with the EFN. Moreover, these disrupted FCs were related to DP's behavioral deficit in face recognition, with the FCs from the FFA to the anterior temporal lobe (ATL) and pSTS the most predictive. Based on these findings, we proposed a revised neural model of face recognition demonstrating the relatedness of interactions among face-responsive regions to face recognition.

Genetic Variation in S100B Modulates Neural Processing of Visual Scenes in Han Chinese.

Spatial navigation is a crucial ability for living. Previous animal studies have shown that the S100B gene is causally related to spatial navigation performance in mice. However, the genetic factors influencing human navigation and its neural substrates remain unclear. Here, we provided the first evidence that the S100B gene modulates neural processing of navigationally relevant scenes in humans. First, with a novel protocol, we demonstrated that the spatial pattern of S100B gene expression in postmortem brains was associated with brain activation pattern for spatial navigation in general, and for scene processing in particular. Further, in a large fMRI cohort of healthy adults of Han Chinese (N = 202), we found that S100B gene polymorphisms modulated scene selectivity in the retrosplenial cortex (RSC) and parahippocampal place area. Finally, the serum levels of S100B protein mediated the association between S100B gene polymorphism and scene selectivity in the RSC. Our study takes the first step toward understanding the neurogenetic mechanism of human spatial navigation and suggests a novel approach to discover candidate genes modulating cognitive functions.

The Hierarchical Structure of the Face Network Revealed by Its Functional Connectivity Pattern.

A major principle of human brain organization is "integrating" some regions into networks while "segregating" other sets of regions into separate networks. However, little is known about the cognitive function of the integration and segregation of brain networks. Here, we examined the well-studied brain network for face processing, and asked whether the integration and segregation of the face network (FN) are related to face recognition performance. To do so, we used a voxel-based global brain connectivity method based on resting-state fMRI to characterize the within-network connectivity (WNC) and the between-network connectivity (BNC) of the FN. We found that 95.4% of voxels in the FN had a significantly stronger WNC than BNC, suggesting that the FN is a relatively encapsulated network. Importantly, individuals with a stronger WNC (i.e., integration) in the right fusiform face area were better at recognizing faces, whereas individuals with a weaker BNC (i.e., segregation) in the right occipital face area performed better in the face recognition tasks. In short, our study not only demonstrates the behavioral relevance of integration and segregation of the FN but also provides evidence supporting functional division of labor between the occipital face area and fusiform face area in the hierarchically organized FN. Significance statement: Although the integration and segregation are major principles of human brain organization, little is known about whether they support the cognitive processes. By correlating the within-network connectivity (WNC) and between-network connectivity (BNC) of the face network with face recognition performance, we found that individuals with stronger WNC in the right fusiform face area or weaker BNC in the right occipital face area were better at recognizing faces. Our study not only demonstrates the behavioral relevance of the integration and segregation but also provides evidence supporting functional division of labor between the occipital face area and fusiform face area in the hierarchically organized face network.

Quantifying the variability of scene-selective regions: Interindividual, interhemispheric, and sex differences.

Scene-selective regions (SSRs), including the parahippocampal place area (PPA), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS), are among the most widely characterized functional regions in the human brain. However, previous studies have mostly focused on the commonality within each SSR, providing little information on different aspects of their variability. In a large group of healthy adults (N = 202), we used functional magnetic resonance imaging to investigate different aspects of topographical and functional variability within SSRs, including interindividual, interhemispheric, and sex differences. First, the PPA, RSC, and TOS were delineated manually for each individual. We then demonstrated that SSRs showed substantial interindividual variability in both spatial topography and functional selectivity. We further identified consistent interhemispheric differences in the spatial topography of all three SSRs, but distinct interhemispheric differences in scene selectivity. Moreover, we found that all three SSRs showed stronger scene selectivity in men than in women. In summary, our work thoroughly characterized the interindividual, interhemispheric, and sex variability of the SSRs and invites future work on the origin and functional significance of these variabilities. Additionally, we constructed the first probabilistic atlases for the SSRs, which provide the detailed anatomical reference for further investigations of the scene network. Hum Brain Mapp 38:2260-2275, 2017. © 2017 Wiley Periodicals, Inc.

Functional integration of the posterior superior temporal sulcus correlates with facial expression recognition.

Face perception is essential for daily and social activities. Neuroimaging studies have revealed a distributed face network (FN) consisting of multiple regions that exhibit preferential responses to invariant or changeable facial information. However, our understanding about how these regions work collaboratively to facilitate facial information processing is limited. Here, we focused on changeable facial information processing, and investigated how the functional integration of the FN is related to the performance of facial expression recognition. To do so, we first defined the FN as voxels that responded more strongly to faces than objects, and then used a voxel-based global brain connectivity method based on resting-state fMRI to characterize the within-network connectivity (WNC) of each voxel in the FN. By relating the WNC and performance in the "Reading the Mind in the Eyes" Test across participants, we found that individuals with stronger WNC in the right posterior superior temporal sulcus (rpSTS) were better at recognizing facial expressions. Further, the resting-state functional connectivity (FC) between the rpSTS and right occipital face area (rOFA), early visual cortex (EVC), and bilateral STS were positively correlated with the ability of facial expression recognition, and the FCs of EVC-pSTS and OFA-pSTS contributed independently to facial expression recognition. In short, our study highlights the behavioral significance of intrinsic functional integration of the FN in facial expression processing, and provides evidence for the hub-like role of the rpSTS for facial expression recognition. Hum Brain Mapp 37:1930-1940, 2016. © 2016 Wiley Periodicals, Inc.

Quantifying interindividual variability and asymmetry of face-selective regions: a probabilistic functional atlas.

Face-selective regions (FSRs) are among the most widely studied functional regions in the human brain. However, individual variability of the FSRs has not been well quantified. Here we use functional magnetic resonance imaging (fMRI) to localize the FSRs and quantify their spatial and functional variabilities in 202 healthy adults. The occipital face area (OFA), posterior and anterior fusiform face areas (pFFA and aFFA), posterior continuation of the superior temporal sulcus (pcSTS), and posterior and anterior STS (pSTS and aSTS) were delineated for each individual with a semi-automated procedure. A probabilistic atlas was constructed to characterize their interindividual variability, revealing that the FSRs were highly variable in location and extent across subjects. The variability of FSRs was further quantified on both functional (i.e., face selectivity) and spatial (i.e., volume, location of peak activation, and anatomical location) features. Considerable interindividual variability and rightward asymmetry were found in all FSRs on these features. Taken together, our work presents the first effort to characterize comprehensively the variability of FSRs in a large sample of healthy subjects, and invites future work on the origin of the variability and its relation to individual differences in behavioral performance. Moreover, the probabilistic functional atlas will provide an adequate spatial reference for mapping the face network.

From sMRI to task-fMRI: A unified geometric deep learning framework for cross-modal brain anatomo-functional mapping.

Achieving predictions of brain functional activation patterns/task-fMRI maps from its underlying anatomy is an important yet challenging problem. Once successful, it will not only open up new ways to understand how brain anatomy influences functional organization of the brain, but also provide new technical support for the clinical use of anatomical information to guide the localization of cortical functional areas. However, due to the non-Euclidean complex architecture of brain anatomy and the inherent low signal-to-noise ratio (SNR) properties of fMRI signals, the key challenge in building such a cross-modal brain anatomo-functional mapping is how to effectively learn the context-aware information of brain anatomy and overcome the interference of noise-containing task-fMRI labels on the learning process. In this work, we propose a Unified Geometric Deep Learning framework (BrainUGDL) to perform the cross-modal brain anatomo-functional mapping task. Considering that both global and local structures of brain anatomy have an impact on brain functions from their respective perspectives, we innovatively propose the novel Global Graph Encoding (GGE) unit and Local Graph Attention (LGA) unit embedded into two parallel branches, focusing on learning the high-level global and local context information, respectively. Specifically, GGE learns the global context information of each mesh vertex by building and encoding global interactions, and LGA learns the local context information of each mesh vertex by selectively aggregating patch structure enhanced features from its spatial neighbors. The information learnt from the two branches is then fused to form a comprehensive representation of brain anatomical features for final brain function predictions. To address the inevitable measurement noise in task-fMRI labels, we further elaborate a novel uncertainty-filtered learning mechanism, which enables BrainUGDL to realize revised learning from the noise-containing labels through the estimated uncertainty. Experiments across seven open task-fMRI datasets from human connectome project (HCP) demonstrate the superiority of BrainUGDL. To our best knowledge, our proposed BrainUGDL is the first to achieve the prediction of individual task-fMRI maps solely based on brain sMRI data.

A large-scale fMRI dataset for the visual processing of naturalistic scenes.

One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put a lot of effort into collecting large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.

A large-scale fMRI dataset for human action recognition.

Human action recognition is a critical capability for our survival, allowing us to interact easily with the environment and others in everyday life. Although the neural basis of action recognition has been widely studied using a few action categories from simple contexts as stimuli, how the human brain recognizes diverse human actions in real-world environments still needs to be explored. Here, we present the Human Action Dataset (HAD), a large-scale functional magnetic resonance imaging (fMRI) dataset for human action recognition. HAD contains fMRI responses to 21,600 video clips from 30 participants. The video clips encompass 180 human action categories and offer a comprehensive coverage of complex activities in daily life. We demonstrate that the data are reliable within and across participants and, notably, capture rich representation information of the observed human actions. This extensive dataset, with its vast number of action categories and exemplars, has the potential to deepen our understanding of human action recognition in natural environments.

Advanced Reinforcement Learning and Its Connections with Brain Neuroscience.

In recent years, brain science and neuroscience have greatly propelled the innovation of computer science. In particular, knowledge from the neurobiology and neuropsychology of the brain revolutionized the development of reinforcement learning (RL) by providing novel interpretable mechanisms of how the brain achieves intelligent and efficient decision making. Triggered by this, there has been a boom in research about advanced RL algorithms that are built upon the inspirations of brain neuroscience. In this work, to further strengthen the bidirectional link between the 2 communities and especially promote the research on modern RL technology, we provide a comprehensive survey of recent advances in the area of brain-inspired/related RL algorithms. We start with basis theories of RL, and present a concise introduction to brain neuroscience related to RL. Then, we classify these advanced RL methodologies into 3 categories according to different connections of the brain, i.e., micro-neural activity, macro-brain structure, and cognitive function. Each category is further surveyed by presenting several modern RL algorithms along with their mathematical models, correlations with the brain, and open issues. Finally, we introduce several important applications of RL algorithms, followed by the discussions of challenges and opportunities for future research.

Functional integration of anterior insula related to meaning in life and loneliness.

BACKGROUND: Meaning in life (MIL), defined as people's feelings of life's meaningfulness, plays a vital role in buffering loneliness - an important indicator of depression and other psychological disorders. Considerable evidence shows that MIL arises from widely distributed brain activity; however, how such activity is functionally integrated and how it influences loneliness is still understudied. METHODS: We here examined how the functional integration of brain regions is related to individual MIL based on resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 970). RESULTS: We found that the global brain connectivity (GBC) of the right anterior insula (rAI) can significantly predict individual MIL. Moreover, mediation analyses were conducted to investigate how the brain influences loneliness with MIL's mediation, which revealed that MIL fully mediates the effect of this hub on loneliness. CONCLUSIONS: These findings suggest that the rAI is a key hub for MIL and loneliness. Its functional integration can be used as a biomarker to predict individual MIL and loneliness.