Xuhong Liao

GRETNA: a graph theoretical network analysis toolbox for imaging connectomics.

Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website.1

Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain

Abstract Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small‐worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting‐state functional MRI and voxel‐based graph theory analysis to systematically investigate the topological organization of whole‐brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high‐order default‐mode and executive‐control regions. The hub and rich‐club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth.

Small-World Human Brain Networks : Perspectives and Challenges

Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.

Test–Retest Reliability of Graph Metrics in High‐resolution Functional Connectomics: A Resting‐State Functional MRI Study

The combination of resting‐state functional MRI (R‐fMRI) technique and graph theoretical approaches has emerged as a promising tool for characterizing the topological organization of brain networks, that is, functional connectomics. In particular, the construction and analysis of high‐resolution brain connectomics at a voxel scale are important because they do not require prior regional parcellations and provide finer spatial information about brain connectivity. However, the test–retest reliability of voxel‐based functional connectomics remains largely unclear.

Individual differences and time-varying features of modular brain architecture

ABSTRACT Recent studies have suggested that human brain functional networks are topologically organized into functionally specialized but inter‐connected modules to facilitate efficient information processing and highly flexible cognitive function. However, these studies have mainly focused on group‐level network modularity analyses using “static” functional connectivity approaches. How these extraordinary modular brain structures vary across individuals and spontaneously reconfigure over time remain largely unknown. Here, we employed multiband resting‐state functional MRI data (N=105) from the Human Connectome Project and a graph‐based modularity analysis to systematically investigate individual variability and dynamic properties in modular brain networks. We showed that the modular structures of brain networks dramatically vary across individuals, with higher modular variability primarily in the association cortex (e.g., fronto‐parietal and attention systems) and lower variability in the primary systems. Moreover, brain regions spontaneously changed their module affiliations on a temporal scale of seconds, which cannot be simply attributable to head motion and sampling error. Interestingly, the spatial pattern of intra‐subject dynamic modular variability largely overlapped with that of inter‐subject modular variability, both of which were highly reproducible across repeated scanning sessions. Finally, the regions with remarkable individual/temporal modular variability were closely associated with network connectors and the number of cognitive components, suggesting a potential contribution to information integration and flexible cognitive function. Collectively, our findings highlight individual modular variability and the notable dynamic characteristics in large‐scale brain networks, which enhance our understanding of the neural substrates underlying individual differences in a variety of cognition and behaviors. HighlightsIntrinsic modular structure of the human brain remarkably varied across individuals.Higher inter‐subject modular variability primarily located in the association cortex.The association cortex showed large variability in module affiliations over time.Regions with higher modular variability tend to have higher cognitive flexibility.

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.

Intrinsic Brain Hub Connectivity Underlies Individual Differences in Spatial Working Memory

Abstract Spatial working memory (SWM) is an important component of working memory and plays an essential role in driving high‐level cognitive abilities. Recent studies have demonstrated that individual SWM is associated with global brain communication. However, whether specific network nodal connectivity, such as brain hub connectivity, is involved in individual SWM performances remains largely unknown. Here, we collected resting‐state fMRI (R‐fMRI) data from a large group of 130 young healthy participants and evaluated their SWM performances. A voxel‐wise whole‐brain network analysis approach was employed to study the relationship between the nodal functional connectivity strength (FCS) and the SWM behavioral scores and to further estimate the participation of brain hubs in individual SWM. We showed significant associations between nodal FCS and SWM performance primarily in the default mode, visual, dorsal attention, and fronto‐parietal systems. Moreover, over 41% of these nodal regions were identified as brain network hubs, and these hubs’ FCS values contributed to 57% of the variance of the individual SWM performances that all SWM‐related regions could explain. Collectively, our findings highlight the cognitive significance of the brain network hubs in SWM, which furthers our understanding of how intrinsic brain network architectures underlie individual differences in SWM processing.

Identifying topological motif patterns of human brain functional networks

Recent imaging connectome studies demonstrated that the human functional brain network follows an efficient small‐world topology with cohesive functional modules and highly connected hubs. However, the functional motif patterns that represent the underlying information flow remain largely unknown. Here, we investigated motif patterns within directed human functional brain networks, which were derived from resting‐state functional magnetic resonance imaging data with controlled confounding hemodynamic latencies. We found several significantly recurring motifs within the network, including the two‐node reciprocal motif and five classes of three‐node motifs. These recurring motifs were distributed in distinct patterns to support intra‐ and inter‐module functional connectivity, which also promoted integration and segregation in network organization. Moreover, the significant participation of several functional hubs in the recurring motifs exhibited their critical role in global integration. Collectively, our findings highlight the basic architecture governing brain network organization and provide insight into the information flow mechanism underlying intrinsic brain activities. Hum Brain Mapp 38:2734–2750, 2017.

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.

APOE Genotype Effects on Intrinsic Brain Network Connectivity in Patients with Amnestic Mild Cognitive Impairment

Whether and how the apolipoprotein E (APOE) ε4 genotype specifically modulates brain network connectivity in patients with amnestic mild cognitive impairment (aMCI) remain largely unknown. Here, we employed resting-state (‘task-free’) functional MRI and network centrality approaches to investigate local (degree centrality, DC) and global (eigenvector centrality, EC) functional integrity in the whole-brain connectome in 156 older adults, including 66 aMCI patients (27 ε4-carriers and 39 non-carriers) and 90 healthy controls (45 ε4-carriers and 45 non-carriers). We observed diagnosis-by-genotype interactions on DC in the left superior/middle frontal gyrus, right middle temporal gyrus and cerebellum, with higher values in the ε4-carriers than non-carriers in the aMCI group. We further observed diagnosis-by-genotype interactions on EC, with higher values in the right middle temporal gyrus but lower values in the medial parts of default-mode network in the ε4-carriers than non-carriers in the aMCI group. Notably, these genotype differences in DC or EC were absent in the control group. Finally, the network connectivity DC values were negatively correlated with cognitive performance in the aMCI ε4-carriers. Our findings suggest that the APOE genotype selectively modulates the functional integration of brain networks in patients with aMCI, thus providing important insight into the gene-connectome interaction in this disease.