Qixiang Lin

Intrinsic functional network architecture of human semantic processing: Modules and hubs

Semantic processing entails the activation of widely distributed brain areas across the temporal, parietal, and frontal lobes. To understand the functional structure of this semantic system, we examined its intrinsic functional connectivity pattern using a database of 146 participants. Focusing on areas consistently activated during semantic processing generated from a meta-analysis of 120 neuroimaging studies (Binder et al., 2009), we found that these regions were organized into three stable modules corresponding to the default mode network (Module DMN), the left perisylvian network (Module PSN), and the left frontoparietal network (Module FPN). These three dissociable modules were integrated by multiple connector hubs-the left angular gyrus (AG) and the left superior/middle frontal gyrus linking all three modules, the left anterior temporal lobe linking Modules DMN and PSN, the left posterior portion of dorsal intraparietal sulcus (IPS) linking Modules DMN and FPN, and the left posterior middle temporal gyrus (MTG) linking Modules PSN and FPN. Provincial hubs, which converge local information within each system, were also identified: the bilateral posterior cingulate cortices/precuneus, the bilateral border area of the posterior AG and the superior lateral occipital gyrus for Module DMN; the left supramarginal gyrus, the middle part of the left MTG and the left orbital inferior frontal gyrus (IFG) for Module FPN; and the left triangular IFG and the left IPS for Module FPN. A neuro-functional model for semantic processing was derived based on these findings, incorporating the interactions of memory, language, and control.

A connectivity-based test-retest dataset of multi-modal magnetic resonance imaging in young healthy adults.

Recently, magnetic resonance imaging (MRI) has been widely used to investigate the structures and functions of the human brain in health and disease in vivo. However, there are growing concerns about the test-retest reliability of structural and functional measurements derived from MRI data. Here, we present a test-retest dataset of multi-modal MRI including structural MRI (S-MRI), diffusion MRI (D-MRI) and resting-state functional MRI (R-fMRI). Fifty-seven healthy young adults (age range: 19–30 years) were recruited and completed two multi-modal MRI scan sessions at an interval of approximately 6 weeks. Each scan session included R-fMRI, S-MRI and D-MRI data. Additionally, there were two separated R-fMRI scans at the beginning and at the end of the first session (approximately 20 min apart). This multi-modal MRI dataset not only provides excellent opportunities to investigate the short- and long-term test-retest reliability of the brain’s structural and functional measurements at the regional, connectional and network levels, but also allows probing the test-retest reliability of structural-functional couplings in the human brain.

Pontine Infarction: Diffusion-Tensor Imaging of Motor Pathways—A Longitudinal Study

PURPOSE To investigate the dynamic evolution of diffusion indexes in the corticospinal tract (CST) distal to a pontine infarct by using diffusion-tensor imaging, to determine the relationship of these indexes with clinical prognosis, and to explore the structural changes in the motor pathway during recovery. MATERIALS AND METHODS This study was approved by the institutional ethics committee, and written informed consent was obtained from each participant. Seventeen patients with pontine infarct underwent five diffusion-tensor imaging examinations during a period of 6 months (within 7 days of onset, 14, 30, 90, and 180 after onset). Fractional anisotropic values were measured in the medulla, cerebral peduncle, internal capsule, and centrum semiovale. Fractional anisotropic values of the CST in the ipsilateral side of the infarct were compared with those in the contralateral sides and those in control subjects by using the Student t test and one-way analysis of variance, and their relationships with clinical scores were analyzed by using Pearson correlation analysis. Reconstructions of the CST were performed. Structural changes of the damaged CST were followed up. RESULTS Fractional anisotropic ratios in the CST above the pons decreased significantly compared with those in the contralateral side and those in control subjects within 7 days, on day 14, and on day 30 after onset (P < .001). Fractional anisotropic ratios above the pons on day 14 correlated positively with Fugl-Meyer scores on day 90 (r = 0.771, P < .001) and day 180 (r = 0.730, P = .001). Follow-up diffusion-tensor tractographic images showed regeneration and reorganization of the motor pathways. CONCLUSION Secondary degeneration of the CST can be detected at diffusion-tensor imaging in the early stages after pontine infarction, which could help predict the motor outcomes. Diffusion-tensor tractography can allow detection of regeneration and reorganization of the motor pathways during recovery.

ZNF804A variants confer risk for heroin addiction and affect decision making and gray matter volume in heroin abusers.

Drug addiction shares common neurobiological pathways and risk genes with other psychiatric diseases, including psychosis. One of the commonly identified risk genes associated with broad psychosis has been ZNF804A. We sought to test whether psychosis risk variants in ZNF804A increase the risk of heroin addiction by modulating neurocognitive performance and gray matter volume (GMV) in heroin addiction. Using case‐control genetic analysis, we compared the distribution of ZNF804A variants (genotype and haplotype) in 1035 heroin abusers and 2887 healthy subjects. We also compared neurocognitive performance (impulsivity, global cognitive ability and decision‐making ability) in 224 subjects and GMV in 154 subjects based on the ZNF804A variants. We found significant differences in the distribution of ZNF804A intronic variants (rs1344706 and rs7597593) allele and haplotype frequencies between the heroin and control groups. Decision‐making impairment was worse in heroin abusers who carried the ZNF804A risk allele and haplotype. Subjects who carried more risk alleles and haplotypes of ZNF804A had greater GMV in the bilateral insular cortex, right temporal cortex and superior parietal cortex. The interaction between heroin addiction and ZNF804A variants affected GMV in the left sensorimotor cortex. Our findings revealed several ZNF804A variants that were significantly associated with the risk of heroin addiction, and these variants affected decision making and GMV in heroin abusers compared with controls. The precise neural mechanisms that underlie these associations are unknown, which requires future investigations of the effects of ZNF804A on both dopamine neurotransmission and the relative increases in the volume of various brain areas.

Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain

White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brains network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brains hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

Increased Global and Local Efficiency of Human Brain Anatomical Networks Detected with FLAIR-DTI Compared to Non-FLAIR-DTI

Diffusion-weighted MRI (DW-MRI), the only non-invasive technique for probing human brain white matter structures in vivo, has been widely used in both fundamental studies and clinical applications. Many studies have utilized diffusion tensor imaging (DTI) and tractography approaches to explore the topological properties of human brain anatomical networks by using the single tensor model, the basic model to quantify DTI indices and tractography. However, the conventional DTI technique does not take into account contamination by the cerebrospinal fluid (CSF), which has been known to affect the estimated DTI measures and tractography in the single tensor model. Previous studies have shown that the Fluid-Attenuated Inversion Recovery (FLAIR) technique can suppress the contribution of the CSF to the DW-MRI signal. We acquired DTI datasets from twenty-two subjects using both FLAIR-DTI and conventional DTI (non-FLAIR-DTI) techniques, constructed brain anatomical networks using deterministic tractography, and compared the topological properties of the anatomical networks derived from the two types of DTI techniques. Although the brain anatomical networks derived from both types of DTI datasets showed small-world properties, we found that the brain anatomical networks derived from the FLAIR-DTI showed significantly increased global and local network efficiency compared with those derived from the conventional DTI. The increases in the network regional topological properties derived from the FLAIR-DTI technique were observed in CSF-filled regions, including the postcentral gyrus, periventricular regions, inferior frontal and temporal gyri, and regions in the visual cortex. Because brain anatomical networks derived from conventional DTI datasets with tractography have been widely used in many studies, our findings may have important implications for studying human brain anatomical networks derived from DW-MRI data and tractography.

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 (Alzheimers 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 Alzheimers 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

HighlightsThe intrinsic functional network was constructed for verb‐action and noun‐object processing.The verb/action‐ and noun/object‐activated areas were intrinsically divided into segregated modules.The word/conceptual‐class specific networks’ integrity associated with behavior in patients. Abstract 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.

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.

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 Alzheimers 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.