Suyu Zhong

PANDA: a pipeline toolbox for analyzing brain diffusion images

Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in in-vivo studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named “Pipeline for Analyzing braiN Diffusion imAges” (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http://www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.

Convergence and divergence across construction methods for human brain white matter networks: An assessment based on individual differences

Using diffusion MRI, a number of studies have investigated the properties of whole‐brain white matter (WM) networks with differing network construction methods (node/edge definition). However, how the construction methods affect individual differences of WM networks and, particularly, if distinct methods can provide convergent or divergent patterns of individual differences remain largely unknown. Here, we applied 10 frequently used methods to construct whole‐brain WM networks in a healthy young adult population (57 subjects), which involves two node definitions (low‐resolution and high‐resolution) and five edge definitions (binary, FA weighted, fiber‐density weighted, length‐corrected fiber‐density weighted, and connectivity‐probability weighted). For these WM networks, individual differences were systematically analyzed in three network aspects: (1) a spatial pattern of WM connections, (2) a spatial pattern of nodal efficiency, and (3) network global and local efficiencies. Intriguingly, we found that some of the network construction methods converged in terms of individual difference patterns, but diverged with other methods. Furthermore, the convergence/divergence between methods differed among network properties that were adopted to assess individual differences. Particularly, high‐resolution WM networks with differing edge definitions showed convergent individual differences in the spatial pattern of both WM connections and nodal efficiency. For the network global and local efficiencies, low‐resolution and high‐resolution WM networks for most edge definitions consistently exhibited a highly convergent pattern in individual differences. Finally, the test–retest analysis revealed a decent temporal reproducibility for the patterns of between‐method convergence/divergence. Together, the results of the present study demonstrated a measure‐dependent effect of network construction methods on the individual difference of WM network properties. Hum Brain Mapp 36:1995–2013, 2015.

The White Matter Structural Network Underlying Human Tool Use and Tool Understanding

The ability to recognize, create, and use complex tools is a milestone in human evolution. Widely distributed brain regions in parietal, frontal, and temporal cortices have been implicated in using and understanding tools, but the roles of their anatomical connections in supporting tool use and tool conceptual behaviors are unclear. Using deterministic fiber tracking in healthy participants, we first examined how 14 cortical regions that are consistently activated by tool processing are connected by white matter (WM) tracts. The relationship between the integrity of each of the 33 obtained tracts and tool processing deficits across 86 brain-damaged patients was investigated. WM tract integrity was measured with both lesion percentage (structural imaging) and mean fractional anisotropy (FA) values (diffusion imaging). Behavioral abilities were assessed by a tool use task, a range of conceptual tasks, and control tasks. We found that three left hemisphere tracts connecting frontoparietal and intrafrontal areas overlapping with left superior longitudinal fasciculus are crucial for tool use such that larger lesion and lower mean FA values on these tracts were associated with more severe tool use deficits. These tracts and five additional left hemisphere tracts connecting frontal and temporal/parietal regions, mainly overlapping with left superior longitudinal fasciculus, inferior frontooccipital fasciculus, uncinate fasciculus, and anterior thalamic radiation, are crucial for tool concept processing. Largely consistent results were also obtained using voxel-based symptom mapping analyses. Our results revealed the WM structural networks that support the use and conceptual understanding of tools, providing evidence for the anatomical skeleton of the tool knowledge network.

The semantic anatomical network: Evidence from healthy and brain-damaged patient populations

Semantic processing is central to cognition and is supported by widely distributed gray matter (GM) regions and white matter (WM) tracts. The exact manner in which GM regions are anatomically connected to process semantics remains unknown. We mapped the semantic anatomical network (connectome) by conducting diffusion imaging tractography in 48 healthy participants across 90 GM “nodes,” and correlating the integrity of each obtained WM edge and semantic performance across 80 brain‐damaged patients. Fifty‐three WM edges were obtained whose lower integrity associated with semantic deficits and together with their linked GM nodes constitute a semantic WM network. Graph analyses of this network revealed three structurally segregated modules that point to distinct semantic processing components and identified network hubs and connectors that are central in the communication across the subnetworks. Together, our results provide an anatomical framework of human semantic network, advancing the understanding of the structural substrates supporting semantic processing. Hum Brain Mapp 36:3499–3515, 2015.

Deficiency of brain structural sub-network underlying post-ischaemic stroke apathy.

This study aimed to reveal the structural basis of post‐ischaemic stroke apathy, especially in relation to disruptions in structural connectivity.

Developmental Changes in Topological Asymmetry Between Hemispheric Brain White Matter Networks from Adolescence to Young Adulthood

Abstract Human brain asymmetries have been well described. Intriguingly, a number of asymmetries in brain phenotypes have been shown to change throughout the lifespan. Recent studies have revealed topological asymmetries between hemispheric white matter networks in the human brain. However, it remains unknown whether and how these topological asymmetries evolve from adolescence to young adulthood, a critical period that constitutes the second peak of human brain and cognitive development. To address this question, the present study included a large cohort of healthy adolescents and young adults. Diffusion and structural magnetic resonance imaging were acquired to construct hemispheric white matter networks, and graph‐theory was applied to quantify topological parameters of the hemispheric networks. In both adolescents and young adults, rightward asymmetry in both global and local network efficiencies was consistently observed between the 2 hemispheres, but the degree of the asymmetry was significantly decreased in young adults. At the nodal level, the young adults exhibited less rightward asymmetry of nodal efficiency mainly around the parasylvian area, posterior tempo‐parietal cortex, and fusiform gyrus. These developmental patterns of network asymmetry provide novel insight into the human brain structural development from adolescence to young adulthood and also likely relate to the maturation of language and social cognition that takes place during this period.

Discriminating the Difference between Remote and Close Association with Relation to White-Matter Structural Connectivity

Remote association is a core ability that influences creative output. In contrast to close association, remote association is commonly agreed to be connected with more original and unique concepts. However, although existing studies have discovered that creativity is closely related to the white-matter structure of the brain, there are no studies that examine the relevance between the connectivity efficiencies and creativity of the brain regions from the perspective of networks. Consequently, this study constructed a brain white matter network structure that consisted of cerebral tissues and nerve fibers and used graph theory to analyze the connection efficiencies among the network nodes, further illuminating the differences between remote and close association in relation to the connectivity of the brain network. Researchers analyzed correlations between the scores of 35 healthy adults with regard to remote and close associations and the connectivity efficiencies of the white-matter network of the brain. Controlling for gender, age, and verbal intelligence, the remote association positively correlated with the global efficiency and negatively correlated with the levels of small-world. A close association negatively correlated with the global efficiency. Notably, the node efficiency in the middle temporal gyrus (MTG) positively correlated with remote association and negatively correlated with close association. To summarize, remote and close associations work differently as patterns in the brain network. Remote association requires efficient and convenient mutual connections between different brain regions, while close association emphasizes the limited connections that exist in a local region. These results are consistent with previous results, which indicate that creativity is based on the efficient integration and connection between different regions of the brain and that temporal lobes are the key regions for discriminating remote and close associations.

Abnormal topological organization of the white matter network in Mandarin speakers with congenital amusia

Congenital amusia is a neurogenetic disorder that mainly affects the processing of musical pitch. Brain imaging evidence indicates that it is associated with abnormal structural and functional connections in the fronto-temporal region. However, a holistic understanding of the anatomical topology underlying amusia is still lacking. Here, we used probabilistic diffusion tensor imaging tractography and graph theory to examine whole brain white matter structural connectivity in 31 Mandarin-speaking amusics and 24 age- and IQ-matched controls. Amusics showed significantly reduced global connectivity, as indicated by the abnormally decreased clustering coefficient (Cp) and increased normalized shortest path length (λ) compared to the controls. Moreover, amusics exhibited enhanced nodal strength in the right inferior parietal lobule relative to controls. The co-existence of the lexical tone deficits was associated with even more deteriorated global network efficiency in amusics, as suggested by the significant correlation between the increments in normalized shortest path length (λ) and the insensitivity in lexical tone perception. Our study is the first to reveal reduced global connectivity efficiency in amusics as well as an increase in the global connectivity cost due to the co-existed lexical tone deficits. Taken together these results provide a holistic perspective on the anatomical substrates underlying congenital amusia.

Aberrant White Matter Networks Mediate Cognitive Impairment in Patients with Silent Lacunar Infarcts in Basal Ganglia Territory

Silent lacunar infarcts, which are present in over 20% of healthy elderly individuals, are associated with subtle deficits in cognitive functions. However, it remains largely unclear how these silent brain infarcts lead to cognitive deficits and even dementia. Here, we used diffusion tensor imaging tractography and graph theory to examine the topological organization of white matter networks in 27 patients with silent lacunar infarcts in the basal ganglia territory and 30 healthy controls. A whole-brain white matter network was constructed for each subject, where the graph nodes represented brain regions and the edges represented interregional white matter tracts. Compared with the controls, the patients exhibited a significant reduction in local efficiency and global efficiency. In addition, a total of eighteen brain regions showed significantly reduced nodal efficiency in patients. Intriguingly, nodal efficiency–behavior associations were significantly different between the two groups. The present findings provide new aspects into our understanding of silent infarcts that even small lesions in subcortical brain regions may affect large-scale cortical white matter network, as such may be the link between subcortical silent infarcts and the associated cognitive impairments. Our findings highlight the need for network-level neuroimaging assessment and more medical care for individuals with silent subcortical infarcts.

White-Matter Structural Connectivity Underlying Human Laughter-Related Traits Processing

Most research into the neural mechanisms of humor has not explicitly focused on the association between emotion and humor on the brain white matter networks mediating this connection. However, this connection is especially salient in gelotophobia (the fear of being laughed at), which is regarded as the presentation of humorlessness, and two related traits, gelotophilia (the enjoyment of being laughed at) and katagelasticism (the enjoyment of laughing at others). Here, we explored whether the topological properties of white matter networks can account for the individual differences in the laughter-related traits of 31 healthy adults. We observed a significant negative correlation between gelotophobia scores and the clustering coefficient, local efficiency and global efficiency, but a positive association between gelotophobia scores and path length in the brains white matter network. Moreover, the current study revealed that with increasing individual fear of being laughed at, the linking efficiencies in superior frontal gyrus, anterior cingulate cortex, parahippocampal gyrus, and middle temporal gyrus decreased. However, there were no significant correlations between either gelotophilia or katagelasticism scores or the topological properties of the brain white matter network. These findings suggest that the fear of being laughed at is directly related to the level of local and global information processing of the brain network, which might provide new insights into the neural mechanisms of the humor information processing.