Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data.

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.

3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images.

Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infants. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes.

Patients with major depressive disorder (MDD) exhibit concurrent deficits in both sensory and higher-order cognitive processing. Connectome studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this gradient structure is disrupted in patients with MDD and how this disruption associates with gene expression profiles and treatment outcome remain unknown. Using a large cohort of resting-state fMRI data from 2227 participants (1148 MDD patients and 1079 healthy controls) recruited at nine sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth, postmortem gene expression, and an 8-week antidepressant treatment (20 MDD patients) data to assess the meta-analytic cognitive functions, transcriptional profiles, and treatment outcomes related to MDD gradient alterations, respectively. Relative to the controls, MDD patients exhibited global topographic alterations in the principal primary-to-transmodal gradient, including reduced explanation ratio, gradient range, and gradient variation (Cohen's d = 0.16-0.21), and focal alterations mainly in the primary and transmodal systems (d = 0.18-0.25). These gradient alterations were significantly correlated with meta-analytic terms involving sensory processing and higher-order cognition. The transcriptional profiles explained 53.9% variance of the altered gradient pattern, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. The baseline gradient maps of patients significantly predicted symptomatic improvement after treatment. These results highlight the connectome gradient dysfunction in MDD and its linkage with gene expression profiles and clinical management, providing insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence.

Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.

First Report of Colletotrichum godetiae Causing Anthracnose on Walnut (Juglans regia and Juglans sigillata) in China.

In August 2020, anthracnose lesions were observed on fruits of Juglans regia and J. sigillata in walnut orchards, in Yijun (Shaanxi Province) and Nanhua (Yunnan Province) counties, China. Symptoms on walnut fruits first appeared as small necrotic spots that rapidly enlarged into subcircular or irregular sunken black lesions (Fig. 1a, b). Sixty diseased walnut fruits (30 fruits of J. regia and J. sigillata, respectively) were randomly sampled from six orchards (10-15 ha each orchard, three orchards were selected in each county) with severe anthracnose (incidence rate of fruit anthracnose is over 60% in the orchard.) in two counties. Twenty-six single spore isolates were obtained from diseased fruits as described by Cai et al. (2009). After seven days, isolates formed grey to milky white colony with abundant aerial hyphae on the upper surface of colony, and milky white to light olive on the back of PDA (Fig. 1c). Conidiogenous cells were hyaline, smooth-walled, and cylindrical to clavate (Fig. 1d). Conidia were smooth-walled, aseptate, cylindrical to fusiform, with both ends acute or one end round and one end slightly acute (Fig. 1e), and ranged in size from 15.5-24.3×4.9-8.1 µm (n=30). Appressoria were brown to medium brown, clavate to elliptical, with the edge entire or undulate (Fig. 1f), and ranged in size from 8.0-27.6×4.7-13.7µm (n=30). The morphological characteristics of 26 isolates were similar to those of the species complex Colletotrichum acutatum (Damm et al. 2012). Six representative isolates were randomly selected (three isolates for each province) for molecular analysis. The ribosomal internal transcribed spacers (ITS) (White et al. 1990), beta-tubulin (TUB2) (Glass and Donaldson 1995), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Templeton et al.1992) and chitin synthase 1 (CHS-1) (Carbone and Kohn 1999) genes were amplified and sequenced. Sequences of 6 of 26 isolates were submitted to GenBank (Accession Nos: ITS: MT799938-MT799943, TUB: MT816321-MT816326, GAPDH:MT816327-MT816332, CHS-1: MT816333-816338). Multi-locus phylogenetic analyses revealed that six isolates clustered together with Colletotrichum godetiae ex-type culture isolates CBS133.44 and CBS130251, and the bootstrap support value was 100% (Fig.2). The pathogenicity of two representative isolates (CFCC54247 and CFCC54244) was tested using healthy fruits of the " J. regia cv. Xiangling" and " J. sigillata cv. Yangbi" varieties. Forty sterilized fruits (20 fruits were inoculated with CFCC54247, and 20 fruits with CFCC54244) were wounded by puncturing with a sterile needle through walnut pericarp and inoculated in the wound site with 10 µl of conidial suspension (106 conidia/ml) from seven day old colonies grown on PDA at 25℃. Twenty wounded fruits were inoculated with sterile water as control. Inoculated and control fruits were incubated in containers at 25℃ in a 12/12h light/dark cycle. The experiment was repeated three times. Anthracnose symptoms (Fig. 1g-h) were observed in all inoculated fruits after 12 days, whereas controls showed no symptoms. Fungal isolates from inoculated diseased fruits showed the same morphological and molecular characteristics as the isolates obtained in this study, confirming Koch's postulates. To our knowledge, this is the first report of C. godetiae causing anthracnose on the two walnut species in China. The result will be helpful for providing a basis for further research on the control of the disease.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.

Structural insight into the individual variability architecture of the functional brain connectome.

Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study.

The default-mode network (DMN) is a set of functionally connected regions that play crucial roles in internal cognitive processing. Previous resting-state fMRI studies have demonstrated that the intrinsic functional organization of the DMN undergoes remarkable reconfigurations during childhood and adolescence. However, these studies have mainly focused on cross-sectional designs with small sample sizes, limiting the consistency and interpretations of the findings. Here, we used a large sample of longitudinal resting-state fMRI data comprising 305 typically developing children (6-12 years of age at baseline, 491 scans in total) and graph theoretical approaches to delineate the developmental trajectories of the functional architecture of the DMN. For each child, the DMN was constructed according to a prior parcellation with 32 brain nodes. We showed that the overall connectivity increased in strength from childhood to adolescence and became spatially similar to that in the young adult group (N = 61, 18-28 years of age). These increases were primarily located in the midline structures. Global and local network efficiency in the DMN also increased with age, indicating an enhanced capability in parallel information communication within the brain system. Based on the divergent developmental rates of nodal centrality, we identified three subclusters within the DMN, with the fastest rates in the cluster mainly comprising the anterior medial prefrontal cortex and posterior cingulate cortex. Together, our findings highlight the developmental patterns of the functional architecture in the DMN from childhood to adolescence, which has implications for the understanding of network mechanisms underlying the cognitive development of individuals.

Characteristics of Intrinsic Brain Functional Connectivity Alterations in Major Depressive Disorder Patients With Suicide Behavior.

BACKGROUND: The intrinsic brain functional connectivity of suicide attempts in major depressive disorder (MDD) remains incompletely understood. PURPOSE: To investigate graph-theoretical based functional connectivity strength (FCS) alterations in MDD patients with suicidal behavior. STUDY TYPE: Prospective. SUBJECTS: Fifty medication-free MDD patients, with (suicide attempters, SA, N = 15) and without (non-attempters, nSA, N = 35) a history of a suicide attempt, and 37 healthy controls (HC). FIELD STRENGTH/SEQUENCE: Resting-state functional magnetic resonance imaging (fMRI) using a gradient-echo imaging sequence was acquired at 3.0 T. ASSESSMENT: For each individual, voxel-wise whole-brain functional network was constructed and graph-theoretical based FCS map was calculated. For each individual in two patient groups, the seed-based functional connectivity map was constructed. STATISTICAL TESTS: Non-parameter permutation tests, analysis of covariance, two-sample t-test, Chi-square test, and Pearson correlation analysis. A P value <0.05 was considered statistically significant. RESULTS: Relative to the HC group, two MDD patient groups showed significantly lower FCS in the left hippocampus, while nSA patients showed additionally lower FCS in more widespread regions (P < 0.05). Importantly, comparing to nSA patients, SA patients had significantly higher FCS in the right orbitofrontal cortex (OFC) and bilateral dorsomedial prefrontal cortex (dmPFC) (P < 0.05). Further seed-based functional connectivity analysis revealed that the right OFC exhibited significantly higher connectivity to right middle frontal gyrus and lower connectivity to the left anterior cingulate cortex and left calcarine sulcus, and the bilateral dmPFC had significantly higher connectivity to the left middle frontal gyrus and right inferior temporal gyrus in the SA patients than in the nSA patients (P < 0.05). DATA CONCLUSION: This study identified disconnections of the OFC and dmPFC which were putatively related to a higher risk of suicidal behavior in MDD patients, thus extended the understanding of suicidal behavior at a brain circuit level. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

The spatial organization of the chronnectome associates with cortical hierarchy and transcriptional profiles in the human brain.

The chronnectome of the human brain represents dynamic connectivity patterns of brain networks among interacting regions, but its organization principle and related transcriptional signatures remain unclear. Using task-free fMRI data from the Human Connectome Project (681 participants) and microarray-based gene expression data from the Allen Institute for Brain Science (1791 brain tissue samples from six donors), we conduct a transcriptome-chronnectome association study to investigate the spatial configurations of dynamic brain networks and their linkages with transcriptional profiles. We first classify the dynamic brain networks into four categories of nodes according to their time-varying characteristics in global connectivity and modular switching: the primary sensorimotor regions with large global variations, the paralimbic/limbic regions with frequent modular switching, the frontoparietal cortex with both high global and modular dynamics, and the sensorimotor association cortex with limited dynamics. Such a spatial layout reflects the cortical functional hierarchy, microarchitecture, and primary connectivity gradient spanning from primary to transmodal areas, and the cognitive spectrum from perception to abstract processing. Importantly, the partial least squares regression analysis reveals that the transcriptional profiles could explain 28% of the variation in this spatial layout of network dynamics. The top-related genes in the transcriptional profiles are enriched for potassium ion channel complex and activity and mitochondrial part of the cellular component. These findings highlight the hierarchically spatial arrangement of dynamic brain networks and their coupling with the variation in transcriptional signatures, which provides indispensable implications for the organizational principle and cellular and molecular functions of spontaneous network dynamics.

Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain.

Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31-42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.

First Report of Colletotrichum aenigma Causing Walnut Anthracnose in China.

English/Persian walnut (Juglans regia L.) is grown as an economically valuable crop in temperate and subtropical regions. In August of 2018, serious fruit anthracnose, with brown to black circular or subcircular or irregular sunken lesions (Fig.1A), occurred on walnut trees ("Xiangling" and "lvling") in 33 ha., 23 ha. and 20 ha. orchards in Lincheng and Neiqiu county, in Xingtai, Hebei, China. Diseased fruits were observed on 41% (19,000 trees), 31% (13,300 trees) and 34% (11,400 trees) walnut trees. Diseased leaves, with circular or irregular brown to gray sunken lesions, were observed on 2% (19,000 trees), 2% (13,300 trees) and 1% (11,400 trees) walnut trees. From each orchard, 25 diseased fruits and leaves were collected, respectively. Twenty-one single spore isolates were obtained from fruits of three orchards and none from leaves as described by Cai et al. (2009). Six representative isolates 1811-1, 1811-4, 1811-7, 1811-8, 1811-11 and 1811-18, two from each orchard, were selected for further study. Colonies on PDA grew 11.8 mm d-1 at 25℃ under a 12/12 h light/dark cycle for 7 d. The upper side of colonies was milky (Fig.1 B), and reverse side was dark brown to brownish yellow. A few acervuli were observed on colonies. Conidiogenous cells were cylindrical to clavate, 10.6-29.7 × 3.1-5.3 µm (mean=21.3 × 4.0 µm, n=30) (Fig.1F). Setae were not observed. Conidia were smooth-walled, aseptate, straight or slightly distorted, cylindrical with one end slightly acute or broadly rounded ends, and 16.6-21.6 × 6.0-7.5 µm (mean=19.2 × 6.7 µm, n=30) (Fig.1 C). Appressoria were mostly irregular in outline, deeply lobed or lightly lobed, gray brown to dark brown, 8.3-16.6 × 7.1-14.5 µm (mean=12.5 × 9.7 µm, n=30) (Fig.1 D-E). Microscopic features were similar to the description of C. aenigma (Weir et al. 2012). To further identify isolates, the ribosomal internal transcribed spacers (ITS), ß-tubulin 2 (TUB2), calmodulin (CAL), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), glutamine synthetase (GS) and chitin synthase (CHS-1) loci of representative isolates were amplified using ITS4/ITS5, Bt2a/Bt2b, CL1/CL2, GDF1/GDR1, GSF1/GSR1 and CHS-79F/CHS-345R primers (Prihastuti et al. 2009; Carbone & Kohn 1999). Sequences of representative isolate 1811-1 were submitted to GenBank (ITS: MN893316, TUB: MN893317, CAL: MN893312, GAPDH: MN893314, GS: MN893315, CHS-1: MN893313). Maximum likehood analysis of sequences of representative isolates and reference sequences of Colletotrichum spp. from GenBank revealed that six isolates clustered together with C. aenigma ex-type culture ICMP18608, and the bootstrap value was 100% (Fig.2). Pathogenicity tests were conducted on walnut fruit as described by Wang et al. (2017, 2018) and Cai et al. (2009). 10 wounded and 10 nonwounded fruits ("Xiangling", 35 mm diameter) were inoculated with isolates 1811-1, 1811-7 and 1811-11 conidial suspension (106 spore/mL) obtained from 10 d colonies grown on PDA at 25℃, respectively. 10 wounded and 10 nonwounded fruits were inoculated with sterile water. Inoculated and control fruits were incubated in containers at 25℃ in a 12/12 h light/dark cycle. After 10 days, necrotic lesions were observed in all inoculated fruits. The pathogen C. aenigma was reisolated from all inoculated fruits but not from control fruits. To our knowledge, this is the first report of C. aenigma causing walnut anthracnose in China. It is urgent to control walnut anthracnose caused by different species of Colletotrichum.

Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder.

Social anxiety disorder (SAD) is a common and disabling condition characterized by excessive fear and avoidance of public scrutiny. Psychoradiology studies have suggested that the emotional and behavior deficits in SAD are associated with abnormalities in regional brain function and functional connectivity. However, little is known about whether intrinsic functional brain networks in patients with SAD are topologically disrupted. Here, we collected resting-state fMRI data from 33 drug-naive patients with SAD and 32 healthy controls (HC), constructed functional networks with 34 predefined regions based on previous meta-analytic research with task-based fMRI in SAD, and performed network-based statistic and graph-theory analyses. The network-based statistic analysis revealed a single connected abnormal circuitry including the frontolimbic circuit (termed the "fear circuit", including the dorsolateral prefrontal cortex, ventral medial prefrontal cortex and insula) and posterior cingulate/occipital areas supporting perceptual processing. In this single altered network, patients with SAD had higher functional connectivity than HC. At the global level, graph-theory analysis revealed that the patients exhibited a lower normalized characteristic path length than HC, which suggests a disorder-related shift of network topology toward randomized configurations. SAD-related deficits in nodal degree, efficiency and participation coefficient were detected in the parahippocampal gyrus, posterior cingulate cortex, dorsolateral prefrontal cortex, insula and the calcarine sulcus. Aspects of abnormal connectivity were associated with anxiety symptoms. These findings highlight the aberrant topological organization of functional brain network organization in SAD, which provides insights into the neural mechanisms underlying excessive fear and avoidance of social interactions in patients with debilitating social anxiety.

Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals.

Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD-relevant alterations is still limited due to the scarcity of large-sample, multisite datasets. Here, we address this issue by including a large R-fMRI dataset with 1434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long-range connections are computed using the amplitude of low-frequency fluctuations, regional homogeneity and distance-related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across-center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between-group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain-clinical variable relationship exhibits poor cross-center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P < .05 and a height threshold of P < .001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder.

Dynamic changes of functional segregation and integration in vulnerability and resilience to schizophrenia.

Schizophrenia (SZ) is a highly heritable disease with neurodevelopmental origins and significant functional brain network dysfunction. Functional network is heavily influenced by neurodevelopment processes and can be characterized by the degree of segregation and integration. This study examines functional segregation and integration in SZ and their first-degree relatives (high risk [HR]) to better understand the dynamic changes in vulnerability and resiliency, and disease markers. Resting-state functional magnetic resonance imaging data acquired from 137 SZ, 89 HR, and 210 healthy controls (HCs). Small-worldness σ was computed at voxel level to quantify balance between segregation and integration. Interregional functional associations were examined based on Euclidean distance between regions and reflect degree of segregation and integration. Distance strength maps were used to localize regions of altered distance-based functional connectivity. σ was significantly decreased in SZ compared to HC, with no differences in high risk (HR). In three-group comparison, significant differences were noted in short-range connectivity (primarily in the primary sensory, motor and their association cortices, and the thalamus) and medium/long-range connectivity (in the prefrontal cortices [PFCs]). Decreased short- and increased medium/long-range connectivity was found in SZ. Decreased short-range connectivity was seen in SZ and HR, while HR had decreased medium/long-range connectivity. We observed disrupted balance between segregation and integration in SZ, whereas relatively preserved in HR. Similarities and differences between SZ and HR, specific changes of SZ were found. These might reflect dynamic changes of segregation in primary cortices and integration in PFCs in vulnerability and resilience, and disease markers in SZ.

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.

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.

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.

The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.

PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data.

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease.

Reorganization of cerebro-cerebellar circuit in patients with left hemispheric gliomas involving language network: A combined structural and resting-state functional MRI study.

The role of cerebellum and cerebro-cerebellar system in neural plasticity induced by cerebral gliomas involving language network has long been ignored. Moreover, whether or not the process of reorganization is different in glioma patients with different growth kinetics remains largely unknown. To address this issue, we utilized preoperative structural and resting-state functional MRI data of 78 patients with left cerebral gliomas involving language network areas, including 46 patients with low-grade glioma (LGG, WHO grade II), 32 with high-grade glioma (HGG, WHO grade III/IV), and 44 healthy controls. Spontaneous brain activity, resting-state functional connectivity and gray matter volume alterations of the cerebellum were examined. We found that both LGG and HGG patients exhibited bidirectional alteration of brain activity in language-related cerebellar areas. Brain activity in areas with increased alteration was significantly correlated with the language and MMSE scores. Structurally, LGG patients exhibited greater gray matter volume in regions with increased brain activity, suggesting a structure-function coupled alteration in cerebellum. Furthermore, we observed that cerebellar regions with decreased brain activity exhibited increased functional connectivity with contralesional cerebro-cerebellar system in LGG patients. Together, our findings provide empirical evidence for a vital role of cerebellum and cerebro-cerebellar circuit in neural plasticity following lesional damage to cerebral language network. Moreover, we highlight the possible different reorganizational mechanisms of brain functional connectivity underlying different levels of behavioral impairments in LGG and HGG patients.