Zhijun Yao

Bio: Zhijun Yao is an academic researcher from Lanzhou University. The author has contributed to research in topics: Medicine & Functional magnetic resonance imaging. The author has an hindex of 15, co-authored 55 publications receiving 934 citations. Previous affiliations of Zhijun Yao include Nanjing Medical University.

Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease

Abstract: Recently, many researchers have used graph theory to study the aberrant brain structures in Alzheimer's disease (AD) and have made great progress. However, the characteristics of the cortical network in Mild Cognitive Impairment (MCI) are still largely unexplored. In this study, the gray matter volumes obtained from magnetic resonance imaging (MRI) for all brain regions except the cerebellum were parcellated into 90 areas using the automated anatomical labeling (AAL) template to construct cortical networks for 98 normal controls (NCs), 113 MCIs and 91 ADs. The measurements of the network properties were calculated for each of the three groups respectively. We found that all three cortical networks exhibited small-world properties and those strong interhemispheric correlations existed between bilaterally homologous regions. Among the three cortical networks, we found the greatest clustering coefficient and the longest absolute path length in AD, which might indicate that the organization of the cortical network was the least optimal in AD. The small-world measures of the MCI network exhibited intermediate values. This finding is logical given that MCI is considered to be the transitional stage between normal aging and AD. Out of all the between-group differences in the clustering coefficient and absolute path length, only the differences between the AD and normal control groups were statistically significant. Compared with the normal controls, the MCI and AD groups retained their hub regions in the frontal lobe but showed a loss of hub regions in the temporal lobe. In addition, altered interregional correlations were detected in the parahippocampus gyrus, medial temporal lobe, cingulum, fusiform, medial frontal lobe, and orbital frontal gyrus in groups with MCI and AD. Similar to previous studies of functional connectivity, we also revealed increased interregional correlations within the local brain lobes and disrupted long distance interregional correlations in groups with MCI and AD.

A review of structural and functional brain networks: small world and atlas

Abstract: Brain networks can be divided into two categories: structural and functional networks. Many studies of neuroscience have reported that the complex brain networks are characterized by small-world or scale-free properties. The identification of nodes is the key factor in studying the properties of networks on the macro-, micro- or mesoscale in both structural and functional networks. In the study of brain networks, nodes are always determined by atlases. Therefore, the selection of atlases is critical, and appropriate atlases are helpful to combine the analyses of structural and functional networks. Currently, some problems still exist in the establishment or usage of atlases, which are often caused by the segmentation or the parcellation of the brain. We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent. In the process of building atlases, the influences of single subjects and groups should be balanced. In this article, we focused on the effects of atlases on the analysis of brain networks and the improved divisions based on the tractography or connectivity in the parcellation of atlases.

A longitudinal study of atrophy in amnestic mild cognitive impairment and normal aging revealed by cortical thickness.

Abstract: In recent years, amnestic mild cognitive impairment (aMCI) has attracted significant attention as an indicator of high risk for Alzheimer's disease. An understanding of the pathology of aMCI may benefit the development of effective clinical treatments for dementia. In this work, we measured the cortical thickness of 109 aMCI subjects and 99 normal controls (NC) twice over two years. The longitudinal changes and the cross-sectional differences between the two types of participants were explored using the vertex thickness values. The thickness of the cortex in aMCI was found significantly reduced in both longitudinal and between-group comparisons, mainly in the temporal lobe, superolateral parietal lobe and some regions of the frontal cortices. Compared to NC, the aMCI showed a significantly high atrophy rate in the left lateral temporal lobe and left parahippocampal gyrus over two years. Additionally, a significant positive correlation between brain atrophy and the decline of Mini-Mental State Examination (MMSE) scores was also found in the left superior and left middle temporal gyrus in aMCI. These findings demonstrated specific longitudinal spatial patterns of cortical atrophy in aMCI and NC. The higher atrophy rate in aMCI might be responsible for the accelerated functional decline in the aMCI progression process.

Morphological changes in subregions of hippocampus and amygdala in major depressive disorder patients

Abstract: Despite many neuroimaging studies in the past years, the neuroanatomical substrates of major depressive disorder (MDD) subcortical structures are still not well understood. Since hippocampus and amygdala are the two vital subcortical structures that most susceptible to MDD, finding the evidence of morphological changes in their subregions may bring some new insights for MDD research. Combining structural magnetic resonance imaging (MRI) with novel morphometry analysis methods, we recruited 25 MDD patients and 28 healthy controls (HC), and investigated their volume and morphological differences in hippocampus and amygdala. Relative to volumetric method, our methods detected more significant global morphological atrophies (p<0.05). More precisely, subiculum and cornu ammonis (CA) 1 subregions of bilateral hippocampus, lateral (LA) and basolateral ventromedial (BLVM) of left amygdala and LA, BLVM, central (CE), amygdalostriatal transition area (ASTR), anterior cortical (ACO) and anterior amygdaloid area (AAA) of right amygdala were demonstrated prone to atrophy. Correlation analyses between each subject's surface eigenvalues and Hamilton Depression Scale (HAMD) were then performed. Correlation results showed that atrophy areas in hippocampus and amygdala have slight tendencies of expanding into other subregions with the development of MDD. Finally, we performed group morphometric analysis and drew the atrophy and expansion areas between MDD-Medicated group (only 19 medicated subjects in MDD group were included) and HC group, found some preliminary evidence about subregional morphological resilience of hippocampus and amygdala. These findings revealed new pathophysiologic patterns in the subregions of hippocampus and amygdala, which can help with subsequent smaller-scale MDD research.

BrainNet Viewer: a network visualization tool for human brain connectomics.

Abstract: The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

The economy of brain network organization

Abstract: On the basis of data from brain network science, Bullmore and Sporns propose that brain organization is shaped by an economical trade-off between minimizing wiring cost and maximizing the efficiency of information transfer between neuronal populations and discuss this idea in the context of psychiatric and neurological disorders. The brain is expensive, incurring high material and metabolic costs for its size — relative to the size of the body — and many aspects of brain network organization can be mostly explained by a parsimonious drive to minimize these costs. However, brain networks or connectomes also have high topological efficiency, robustness, modularity and a 'rich club' of connector hubs. Many of these and other advantageous topological properties will probably entail a wiring-cost premium. We propose that brain organization is shaped by an economic trade-off between minimizing costs and allowing the emergence of adaptively valuable topological patterns of anatomical or functional connectivity between multiple neuronal populations. This process of negotiating, and re-negotiating, trade-offs between wiring cost and topological value continues over long (decades) and short (millisecond) timescales as brain networks evolve, grow and adapt to changing cognitive demands. An economical analysis of neuropsychiatric disorders highlights the vulnerability of the more costly elements of brain networks to pathological attack or abnormal development.