A Hybrid Routing Pattern in Human Brain Structural Network Revealed By Evolutionary Computation.

The human brain functional connectivity network (FCN) is constrained and shaped by the communication processes in the structural connectivity network (SCN). The underlying communication mechanism thus becomes a critical issue for understanding the formation and organization of the FCN. A number of communication models supported by different routing strategies have been proposed, with shortest path (SP), random diffusion (DIF), and spatial navigation (NAV) as the most typical, respectively requiring network global knowledge, local knowledge, and both for path seeking. Yet these models all assumed every brain region to use one routing strategy uniformly, ignoring convergent evidence that supports the regional heterogeneity in both terms of biological substrates and functional roles. In this regard, the current study developed a hybrid communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB). The HYB was found to outperform the three typical routing strategies in predicting FCN and facilitating robust communication. Analyses on HYB further revealed that brain regions in lower-order functional modules inclined to route signals using global knowledge, while those in higher-order functional modules preferred DIF that requires only local knowledge. Compared to regions that used global knowledge for routing, regions using DIF had denser structural connections, participated in more functional modules, but played a less dominant role within modules. Together, our findings further evidenced that hybrid routing underpins efficient SCN communication and locally heterogeneous structure-function coupling.

Secondary Degeneration of White Matter Tract following Basal Ganglia Infarction: A Longitudinal Diffusion Tensor Imaging Study.

INTRODUCTION: We explored the relationship between secondary degeneration of white matter (WM) tracts and motor outcomes after left basal ganglia infarction and investigated alterations in the diffusion indices of WM tracts in distal areas. METHODS: Clinical neurological evaluations were accomplished using the Fugl-Meyer scale (FMS). Then, the fractional anisotropy (FA) of the bilateral superior corona radiata (SCR), cerebral peduncle (CP), corticospinal tracts (CST), and corpus callosum (CC) were measured in all patients and control subjects. RESULTS: Regional-based analysis revealed decreased FA values in the ipsilesional SCR, CP, and CST of the patients, compared to the control subjects at 5- time points. The relative FA (rFA) values of the SCR, CP, and CST decreased progressively with time, the lowest values recorded at 90 days before increasing slightly at 180 days after stroke. Compared to the contralateral areas, the FA values of the ipsilesional SCR and CST areas were significantly decreased (P=0.023), while those of the CP decreased at 180 days (P=0.008). Compared with the values at 7 days, the rFA values of the ipsilesional SCR and CP areas were significantly reduced at 14, 30, and 90 days, while those in the CST area were significantly reduced at 14, 90, and 180 days. The CP rFA value at 7 days correlated positively with the FM scores at 180 days (r=0.469, P=0.037). CONCLUSION: This study provides an objective, comprehensive, and automated protocol for detecting secondary degeneration of WM, which is important in understanding rehabilitation mechanisms after stroke.

Functional connectome through the human life span.

The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.

Magnetic resonance evidence of increased iron content in subcortical brain regions in asymptomatic Alzheimer's disease.

While iron over-accumulation has been reported in late stage Alzheimer's disease (AD), whether this occurs early in the asymptomatic stage of AD remains unknown. We aimed to assess brain iron levels in asymptomatic AD using quantitative MR relaxometry of effective transverse relaxation rate (R2*) and longitudinal relaxation rate (R1), and recruited 118 participants comprised of three groups including healthy young participants, and cognitively normal older individuals without or with positive AD biomarkers based on cerebrospinal fluid (CSF) proteomics analysis. Compared with the healthy young group, increased R2* was found in widespread cortical and subcortical regions in the older groups. Further, significantly higher levels of R2* were found in the cognitively normal older subjects with positive CSF AD biomarker (i.e., asymptomatic AD) compared with those with negative AD biomarker in subcortical regions including the left and right caudate, left and right putamen, and left and right globus pallidus (p < .05 for all regions), suggesting increased iron content in these regions. Subcortical R2* of some regions was found to significantly correlate with CSF AD biomarkers and neuropsychological assessments of visuospatial functions. In conclusion, R2* could be a valuable biomarker for studying early pathophysiological changes in AD.

Multimodal magnetic resonance imaging reveals distinct sensitivity of hippocampal subfields in asymptomatic stage of Alzheimer's disease.

While hippocampal atrophy and its regional susceptibility to Alzheimer's disease (AD) are well reported at late stages of AD, studies of the asymptomatic stage of AD are limited but could elucidate early stage pathophysiology as well as provide predictive biomarkers. In this study, we performed multi-modal magnetic resonance imaging (MRI) to estimate morphometry, functional connectivity, and tissue microstructure of hippocampal subfields in cognitively normal adults including those with asymptomatic AD. High-resolution resting-state functional, diffusion and structural MRI, cerebral spinal fluid (CSF), and neuropsychological evaluations were performed in healthy young adults (HY: n = 40) and healthy older adults with negative (HO-: n = 47) and positive (HO+ : n = 25) CSF biomarkers of AD. Morphometry, functional connectivity, and tissue microstructure were estimated from the structural, functional, and diffusion MRI images, respectively. Our results indicated that normal aging affected morphometry, connectivity, and microstructure in all hippocampal subfields, while the subiculum and CA1-3 demonstrated the greatest sensitivity to asymptomatic AD pathology. Tau, rather than amyloid-ß, was closely associated with imaging-derived synaptic and microstructural measures. Microstructural metrics were significantly associated with neuropsychological assessments. These findings suggest that the subiculum and CA1-3 are the most vulnerable in asymptomatic AD and tau level is driving these early changes.

Functional Connectivity Prediction With Deep Learning for Graph Transformation.

Inferring resting-state functional connectivity (FC) from anatomical brain wiring, known as structural connectivity (SC), is of enormous significance in neuroscience for understanding biological neuronal networks and treating mental diseases. Both SC and FC are networks where the nodes are brain regions, and in SC, the edges are the physical fiber nerves among the nodes, while in FC, the edges are the nodes' coactivation relations. Despite the importance of SC and FC, until very recently, the rapidly growing research body on this topic has generally focused on either linear models or computational models that rely heavily on heuristics and simple assumptions regarding the mapping between FC and SC. However, the relationship between FC and SC is actually highly nonlinear and complex and contains considerable randomness; additional factors, such as the subject's age and health, can also significantly impact the SC-FC relationship and hence cannot be ignored. To address these challenges, here, we develop a novel SC-to-FC generative adversarial network (SF-GAN) framework for mapping SC to FC, along with additional metafeatures based on a newly proposed graph neural network-based generative model that is capable of learning the stochasticity. Specifically, a new graph-based conditional generative adversarial nets model is proposed, where edge convolution layers are leveraged to encode the graph patterns in the SC in the form of a graph representation. New edge deconvolution layers are then utilized to decode the representation back to FC. Additional metafeatures of subjects' profile information are integrated into the graph representation with newly designed sparse-regularized layers that can automatically select features that impact FC. Finally, we have also proposed new post hoc explainer of our SF-GAN, which can identify which subgraphs in SC strongly influence which subgraphs in FC by a new multilevel edge-correlation-guided graph clustering problem. The results of experiments conducted to test the new model confirm that it significantly outperforms existing state-of-the-art methods, with additional interpretability for identifying important metafeatures and subgraphs.

Frontal Metabolites and Alzheimer's Disease Biomarkers in Healthy Older Women and Women Diagnosed with Mild Cognitive Impairment.

BACKGROUND: Women account for two thirds of the prevalence and incidence of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Evidence suggest that sex may differently influence the expression of proteins amyloid-beta (Aß1-42) and tau, for which early detection is crucial in prevention of the disease. OBJECTIVE: We investigated the effect of aging and cerebrospinal fluid (CSF) levels of Aß1-42 and tau on frontal metabolites measured with proton magnetic resonance spectroscopy (MRS) in a cohort of cognitively normal older women and women with MCI. METHODS: 3T single-voxel MRS was performed on the medial frontal cortex, using Point Resolved Spectroscopy (PRESS) and Mescher-Garwood Point Resolved Spectroscopy (MEGA-PRESS) in 120 women (age range 50-85). CSF samples of Aß1-42 and tau and scores of general cognition were also obtained. RESULTS: Levels of frontal gamma aminobutyric acid (GABA+) were predicted by age, independently of disease and CSF biomarkers. Importantly, levels of GABA+ were reduced in MCI patients. Additionally, we found that levels of N-acetylaspartate relative to myo-inositol (tNAA/mI) predicted cognition in MCI patients only and were not related to CSF biomarkers. CONCLUSION: This study is the first to demonstrate a strong association between frontal GABA+ levels and neurological aging in a sample consisting exclusively of healthy older women with various levels of CSF tau and Aß1-42 and women with MCI. Importantly, our results show no correlation between CSF biomarkers and MRS metabolites in this sample.

Selectively disrupted sensorimotor circuits in chronic stroke with hand dysfunction.

AIM: To investigate the directional and selective disconnection of the sensorimotor cortex (SMC) subregions in chronic stroke patients with hand dysfunction. METHODS: We mapped the resting-state fMRI effective connectivity (EC) patterns for seven SMC subregions in each hemisphere of 65 chronic stroke patients and 40 healthy participants and correlated these patterns with paretic hand performance. RESULTS: Compared with controls, patients demonstrated disrupted EC in the ipsilesional primary motor cortex_4p, ipsilesional primary somatosensory cortex_2 (PSC_2), and contralesional PSC_3a. Moreover, we found that EC values of the contralesional PSC_1 to contralesional precuneus, the ipsilesional inferior temporal gyrus to ipsilesional PSC_1, and the ipsilesional PSC_1 to contralesional postcentral gyrus were correlated with paretic hand performance across all patients. We further divided patients into partially (PPH) and completely (CPH) paretic hand subgroups. Compared with CPH patients, PPH patients demonstrated decreased EC in the ipsilesional premotor_6 and ipsilesional PSC_1. Interestingly, we found that paretic hand performance was positively correlated with seven sensorimotor circuits in PPH patients, while it was negatively correlated with five sensorimotor circuits in CPH patients. CONCLUSION: SMC neurocircuitry was selectively disrupted after chronic stroke and associated with diverse hand outcomes, which deepens the understanding of SMC reorganization.

Relationships between frontal metabolites and Alzheimer's disease biomarkers in cognitively normal older adults.

Elevated expression of ß-amyloid (Aß1-42) and tau are considered risk-factors for Alzheimer's disease in healthy older adults. We investigated the effect of aging and cerebrospinal fluid levels of Aß1-42 and tau on 1) frontal metabolites measured with proton magnetic resonance spectroscopy (MRS) and 2) cognition in cognitively normal older adults (n = 144; age range 50-85). Levels of frontal gamma aminobutyric acid (GABA+) and myo-inositol relative to creatine (mI/tCr) were predicted by age. Levels of GABA+ predicted cognitive performance better than mI/tCr. Additionally, we found that frontal levels of n-acetylaspartate relative to creatine (tNAA/tCr) were predicted by levels of t-tau. In cognitively normal older adults, levels of frontal GABA+ and mI/tCr are predicted by aging, with levels of GABA+ decreasing with age and the opposite for mI/tCr. These results suggest that age- and biomarker-related changes in brain metabolites are not only located in the posterior cortex as suggested by previous studies and further demonstrate that MRS is a viable tool in the study of aging and biomarkers associated with pathological aging and Alzheimer's disease.

Association of aerobic glycolysis with the structural connectome reveals a benefit-risk balancing mechanism in the human brain.

Aerobic glycolysis (AG), that is, the nonoxidative metabolism of glucose, contributes significantly to anabolic pathways, rapid energy generation, task-induced activity, and neuroprotection; yet high AG is also associated with pathological hallmarks such as amyloid-ß deposition. An important yet unresolved question is whether and how the metabolic benefits and risks of brain AG is structurally shaped by connectome wiring. Using positron emission tomography and magnetic resonance imaging techniques as well as computational models, we investigate the relationship between brain AG and the macroscopic connectome. Specifically, we propose a weighted regional distance-dependent model to estimate the total axonal projection length of a brain node. This model has been validated in a macaque connectome derived from tract-tracing data and shows a high correspondence between experimental and estimated axonal lengths. When applying this model to the human connectome, we find significant associations between the estimated total axonal projection length and AG across brain nodes, with higher levels primarily located in the default-mode and prefrontal regions. Moreover, brain AG significantly mediates the relationship between the structural and functional connectomes. Using a wiring optimization model, we find that the estimated total axonal projection length in these high-AG regions exhibits a high extent of wiring optimization. If these high-AG regions are randomly rewired, their total axonal length and vulnerability risk would substantially increase. Together, our results suggest that high-AG regions have expensive but still optimized wiring cost to fulfill metabolic requirements and simultaneously reduce vulnerability risk, thus revealing a benefit-risk balancing mechanism in the human brain.

Diverse functional connectivity patterns of resting-state brain networks associated with good and poor hand outcomes following stroke.

Motor stroke has been characterized by disruptions in multiple large-scale functional brain networks. However, it remains unclear whether stroke patients with good hand outcomes show different connectivity profiles within and between networks from those with poor hand outcomes. In this cross-sectional study, we recruited 52 chronic subcortical stroke patients [illness duration (mean ± SD): 16 ± 16.2 months] and 52 healthy controls from the local hospital and community from June 2010 to August 2016. We first performed independent component analysis (ICA) on resting-state fMRI data to extract fifteen resting-state networks. Then, we compared the functional connectivity within and between networks across 52 healthy controls, 26 patients with a partially paralyzed hand (PPH), and 26 patients with a completely paralyzed hand (CPH). Compared to the patients with a PPH, the patients with a CPH showed increased connectivity in the contralesional sensorimotor cortex within the contralesional sensorimotor network; the increased connectivity was negatively correlated with the performance of the paretic hand. Moreover, the patients with a CPH, compared to those with a PPH, showed decreased strengths of connectivity between the ipsilesional sensorimotor network and both the dorsal sensorimotor network and ventral visual network; the decreased strengths of connectivity were positively correlated with the performance of the paretic hand. Collectively, our findings suggest that stroke patients with different hand outcomes show distinct functional reorganization patterns in large-scale brain networks. These findings shed light on the network-level neuromechanisms that help explain why stroke survivors in the chronic stage show different hand outcomes.

Long-term Chinese calligraphic handwriting reshapes the posterior cingulate cortex: A VBM study.

As a special kind of handwriting with a brush, Chinese calligraphic handwriting (CCH) requires a large amount of practice with high levels of concentration and emotion regulation. Previous studies have showed that long-term CCH training has positive effects physically (induced by handwriting activities) and psychologically (induced by the state of relaxation and concentration), the latter of which is similar to the effects of meditation. The aim of this study was to investigate the long-term CCH training effect on anxiety and attention, as well as brain structure. Participants were 32 individuals who had at least five years of CCH experience and 44 controls. Results showed that CCH training benefited individuals' selective and divided attention but did not decrease their anxiety level. Moreover, the VBM analysis showed that long-term CCH training was mainly associated with smaller grey matter volumes (GMV) in the right precuneus/posterior cingulate cortex (PCC). No brain areas showed larger GMV in the CCH group than the control group. Using two sets of regions of interest (ROIs), one related to meditation and the other to handwriting, ROI analysis showed significant differences between the CCH and the control group only at the meditation-related ROIs, not at the handwriting-related ROIs. Finally, for the whole sample, the GMV of both the whole brain and the PCC were negatively correlated with selective attention and divided attention. The present study was cross-sectional and had a relatively small sample size, but its results suggested that CCH training might benefit attention and influence particular brain structure through mental processes such as meditation.

Long-term Chinese calligraphic handwriting training has a positive effect on brain network efficiency.

As a visual art form, Chinese calligraphic handwriting (CCH) has been found to correlate with certain brain activity and to induce functional connectivity reorganization of the brain. This study investigated the effect of long-term CCH training on brain functional plasticity as assessed with network measures. With the resting-state fMRI data from 31 participants with at least five years of CCH training and 40 controls, we constructed brain functional networks, examined group differences at both the whole brain and modular levels, and correlated the topological characteristics with calligraphy skills. We found that, compared to the control group, the CCH group showed shorter characteristic path lengths and higher local efficiency in certain brain areas in the frontal and parietal cortices, limbic system, basal ganglia, and thalamus. Moreover, these network measures in the cingulate cortex, caudate nucleus, and thalamus were associated with CCH performance (i.e., copying and creating skills). These results suggest that long-term CCH training has a positive effect on the topological characteristics of brain networks.

Disrupted structural and functional brain networks in Alzheimer's disease.

Studies have demonstrated that the clinical manifestations of Alzheimer's disease (AD) are associated with abnormal connections in either functional connectivity networks (FCNs) or structural connectivity networks (SCNs). However, the FCN and SCN of AD have usually been examined separately, and the results were inconsistent. In this multimodal study, we collected resting-state functional magnetic resonance imaging and diffusion magnetic resonance imaging data from 46 patients with AD and 39 matched healthy controls (HCs). Graph-theory analysis was used to investigate the topological organization of the FCN and SCN simultaneously. Compared with HCs, both the FCN and SCN of patients with AD showed disrupted network integration (i.e., increased characteristic path length) and segregation (i.e., decreased intramodular connections in the default mode network). Moreover, the FCN, but not the SCN, exhibited a reduced clustering coefficient and reduced rich club connections in AD. The coupling (i.e., correlation) of the FCN and SCN in AD was increased in connections of the default mode network and the rich club. These findings demonstrated overlapping and distinct network disruptions in the FCN and SCN and a strengthened correlation between FCNs and SCNs in AD, which provides a novel perspective for understanding the pathophysiological mechanisms underlying AD.

Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity by Machine Learning.

Individuals show marked variability in determining to be honest or deceptive in daily life. A large number of studies have investigated the neural substrates of deception; however, the brain networks contributing to the individual differences in deception remain unclear. In this study, we sought to address this issue by employing a machine-learning approach to predict individuals' deceptive propensity based on the topological properties of whole-brain resting-state functional connectivity (RSFC). Participants finished the resting-state functional MRI (fMRI) data acquisition, and then, one week later, participated as proposers in a modified ultimatum game in which they spontaneously chose to be honest or deceptive. A linear relevance vector regression (RVR) model was trained and validated to examine the relationship between topological properties of networks of RSFC and actual deceptive behaviors. The machine-learning model sufficiently decoded individual differences in deception using three brain networks based on RSFC, including the executive controlling network (dorsolateral prefrontal cortex, middle frontal cortex, and orbitofrontal cortex), the social and mentalizing network (the temporal lobe, temporo-parietal junction, and inferior parietal lobule), and the reward network (putamen and thalamus). These networks have been found to form a signaling cognitive framework of deception by coding the mental states of others and the reward or values of deception or honesty, and integrating this information to make a final decision about being deceptive or honest. These findings suggest the potential of using RSFC as a task-independent neural trait for predicting deceptive propensity, and shed light on using machine-learning approaches in deception detection.

Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models.

Recently, functional connectome studies based on resting-state functional magnetic resonance imaging (R-fMRI) and graph theory have greatly advanced our understanding of the topological principles of healthy and diseased brains. However, how different strategies for R-fMRI data preprocessing and for connectome analyses jointly affect topological characterization and contrastive research of brain networks remains to be elucidated. Here, we used two R-fMRI data sets, a healthy young adult data set and an Alzheimer's disease (AD) patient data set, and up to 42 analysis strategies to comprehensively investigate the joint influence of three key factors (global signal regression, regional parcellation schemes, and null network models) on the topological analysis and contrastive research of whole-brain functional networks. At the global level, we first found that these three factors affected not only the quantitative values but also the individual variability profile in small-world related metrics and modularity, wherein global signal regression exhibited the predominant influence. Moreover, strategies without global signal regression and with topological randomization null model enhanced the sensitivity of the detection of differences between AD and control groups in small-worldness and modularity. At the nodal level, strategies of global signal regression dominantly influenced the spatial distribution of both hubs and between-group differences in terms of nodal degree centrality. Together, we highlight the remarkable joint influence of global signal regression, regional parcellation schemes and null network models on functional connectome analyses in both health and diseases, which may provide guidance for the choice of analysis strategies in future functional network studies.

Changes in Brain Lateralization in Patients with Mild Cognitive Impairment and Alzheimer's Disease: A Resting-State Functional Magnetic Resonance Study from Alzheimer's Disease Neuroimaging Initiative.

PURPOSE: To detect changes in brain lateralization in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) using resting-state functional magnetic resonance imaging (fMRI). MATERIALS AND METHODS: Data from 61 well-matched right-handed subjects were obtained from the Alzheimer's Disease Neuroimaging Initiative, including 19 healthy controls (HCs), 25 patients with MCI, and 17 patients with AD. First, we divided 256 pairs of seed regions from each hemisphere covering the entire cerebral gray matter. Then, we used the intrinsic laterality index (iLI) approach to quantify the functional laterality using fMRI. One-way ANOVA was employed to estimate the differences in iLI among the three groups. The sum, number and mean value of the iLI were calculated within the thresholds of 0 < |iLI| < 0.2, 0.2 ≤ |iLI| < 0.4, 0.4 ≤ |iLI| < 0.8, and |iLI| ≥ 0.8, to explore the changes in the lateralization of resting-state brain function in patients with MCI and AD. RESULTS: One-way ANOVA revealed that the iLIs of the three groups were significantly different. The HCs showed a significant leftward interhemispheric difference within |iLI| ≥ 0.8. Compared with the HCs, the patients with MCI manifested a distinct abnormal rightward interhemispheric asymmetry, mainly within the thresholds of 0.2 ≤ |iLI| < 0.4 and 0.4 ≤ |iLI| < 0.8; in the patients with AD, the normal leftward lateralization that was observed in the HCs disappeared, and an abnormal rightward laterality was expressed within 0.4 ≤ |iLI| < 0.8. By directly comparing the patients with MCI with the patients with AD, an exclusive abnormal rightward laterality was observed in the patients with MCI within the 0.2 ≤ |iLI| < 0.4 threshold, and the normal leftward asymmetry vanished in the patients with AD within the |iLI| ≥ 0.8 threshold. CONCLUSION: Global brain lateralization was different among three groups. The abnormal rightward dominance observed in the patients with MCI and AD may indicate that these patients use additional brain resources to compensate for the loss of cognitive function, and the observed disappearance of the leftward laterality in the patients with AD was likely associated with the damage in the left hemisphere. The observed disappearance of the rightward asymmetry in the patients with AD using the 0.2 ≤ |iLI| < 0.4 threshold was likely a sign of decompensation. Our study provides new insights that may improve our understanding of MCI and AD.

Differentially categorized structural brain hubs are involved in different microstructural, functional, and cognitive characteristics and contribute to individual identification.

Very little is known regarding whether structural hubs of human brain networks that enable efficient information communication may be classified into different categories. Using three multimodal neuroimaging data sets, we construct individual structural brain networks and further identify hub regions based on eight widely used graph-nodal metrics, followed by comprehensive characteristics and reproducibility analyses. We show the three categories of structural hubs in the brain network, namely, aggregated, distributed, and connector hubs. Spatially, these distinct categories of hubs are primarily located in the default-mode system and additionally in the visual and limbic systems for aggregated hubs, in the frontoparietal system for distributed hubs, and in the sensorimotor and ventral attention systems for connector hubs. These categorized hubs exhibit various distinct characteristics to support their differentiated roles, involving microstructural organization, wiring costs, topological vulnerability, functional modular integration, and cognitive flexibility; moreover, these characteristics are better in the hubs than nonhubs. Finally, all three categories of hubs display high across-session spatial similarities and act as structural fingerprints with high predictive rates (100%, 100%, and 84.2%) for individual identification. Collectively, we highlight three categories of brain hubs with differential microstructural, functional and, cognitive associations, which shed light on topological mechanisms of the human connectome.

Meta-Connectomic Analysis Reveals Commonly Disrupted Functional Architectures in Network Modules and Connectors across Brain Disorders.

Neuropsychiatric disorders are increasingly conceptualized as disconnection syndromes that are associated with abnormal network integrity in the brain. However, whether different neuropsychiatric disorders show commonly dysfunctional connectivity architectures in large-scale brain networks remains largely unknown. Here, we performed a meta-connectomic study to identify disorder-related functional modules and brain regions by combining meta-analyses of 182 published resting-state functional MRI studies in 11 neuropsychiatric disorders and graph-theoretical analyses of 3 independent resting-state functional MRI datasets with healthy and diseased populations (Alzheimer's disease and major depressive disorder [MDD]). Three major functional modules, the default mode, frontoparietal, and sensorimotor networks were commonly abnormal across disorders. Moreover, most of the disorders preferred to target the network connector nodes that were primarily involved in intermodule communications and multiple cognitive components. Apart from these common dysfunctions, different brain disorders were associated with specific alterations in network modules and connector regions. Finally, these meta-connectomic findings were confirmed by two empirical example cases of Alzheimer's disease and MDD. Collectively, our findings shed light on the shared biological mechanisms of network dysfunctions of diverse disorders and have implications for clinical diagnosis and treatment from a network perspective.

Dissociable intrinsic functional networks support noun-object and verb-action processing.

The processing mechanism of verbs-actions and nouns-objects is a central topic of language research, with robust evidence for behavioral dissociation. The neural basis for these two major word and/or conceptual classes, however, remains controversial. Two experiments were conducted to study this question from the network perspective. Experiment 1 found that nodes of the same class, obtained through task-evoked brain imaging meta-analyses, were more strongly connected with each other than nodes of different classes during resting-state, forming segregated network modules. Experiment 2 examined the behavioral relevance of these intrinsic networks using data from 88 brain-damaged patients, finding that across patients the relative strength of functional connectivity of the two networks significantly correlated with the noun-object vs. verb-action relative behavioral performances. In summary, we found that verbs-actions and nouns-objects are supported by separable intrinsic functional networks and that the integrity of such networks accounts for the relative noun-object- and verb-action-selective deficits.