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/).

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

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.

The economy of brain network organization

Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs. The next challenge is to use this knowledge to gain a better understanding of brain disease. Recent developments in the application of network science to conditions such as Alzheimer's disease, multiple sclerosis, traumatic brain injury and epilepsy have challenged the classical concept of neurological disorders being either 'local' or 'global', and have pointed to the overload and failure of hubs as a possible final common pathway in neurological disorders.

Modern network science of neurological disorders

Purpose of review
Recent developments in the statistical physics of complex networks have been translated to neuroimaging data in an effort to enhance our understanding of human brain structural and functional networks. This review focuses on studies using graph theoretical measures applied to structural MRI, diffusion MRI, functional MRI, electroencephalography and magnetoencephalography data.

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Human Brain Networks in Health and Disease

Inter-individual differences in the thickness or volume of a brain region often co-vary with inter-individual differences in other brain regions. Alexander-Bloch, Giedd and Bullmore discuss this phenomenon of structural co-variance, its underlying mechanisms and its potential value in the understanding of various brain disorders.

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Imaging structural co-variance between human brain regions.

Recent research on Alzheimer's disease (AD) has shown that the decline of cognitive and memory functions is accompanied by a disrupted neuronal connectivity characterized by white matter (WM) degeneration. However, changes in the topological organization of WM structural network in AD remain largely unknown. Here, we used diffusion tensor image tractography to construct the human brain WM networks of 25 AD patients and 30 age- and sex-matched healthy controls, followed by a graph theoretical analysis. We found that both AD patients and controls had a small-world topology in WM network, suggesting an optimal balance between structurally segregated and integrative organization. More important, the AD patients exhibited increased shortest path length and decreased global efficiency in WM network compared with controls, implying abnormal topological organization. Furthermore, we showed that the WM network contained highly connected hub regions that were predominately located in the precuneus, cingulate cortex, and dorsolateral prefrontal cortex, which was consistent with the previous diffusion-MRI studies. Specifically, AD patients were found to have reduced nodal efficiency predominantly located in the frontal regions. Finally, we showed that the alterations of various network properties were significantly correlated with the behavior performances. Together, the present study demonstrated for the first time that the Alzheimer's brain was associated with disrupted topological organization in the large-scale WM structural networks, thus providing the structural evidence for abnormalities of systematic integrity in this disease. This work could also have implications for understanding how the abnormalities of structural connectivity in AD underlie behavioral deficits in the patients.

https://www.jneurosci.org/content/jneuro/30/50/16876.full.pdf

Diffusion Tensor Tractography Reveals Abnormal Topological Organization in Structural Cortical Networks in Alzheimer's Disease

http://www.nlpr.ia.ac.cn/2012papers/gjkw/gk80.pdf

Abnormal topological organization of structural brain networks in schizophrenia.

Disrupted thalamo-cortical connectivity in schizophrenia: A morphometric correlation analysis

Large-scale preclinical Alzheimer’s disease study based on β-amyloid positron emission tomography (PET) has not been conducted in China. Establish a cohort on Alzheimer’s disease spectrum, especially the preclinical stages, and determine the factors influencing the acceptance of β-amyloid PET scan screening in China. Longitudinal. Shanghai, China. A total of 4386 participants were screened and 2451 participants who met enrollment criteria were eventually included in this report. The multidimensional data was collected, including comprehensive assessments, PET and magnetic resonance imaging scans, genetics, and plasma biomarkers. There were 571 participants in the normal cognition group, 625 participants in the subjective cognitive decline group, 155 participants in the objectively defined subtle cognitive decline group, 501 participants in the mild cognitive impairment group, 471 participants in Alzheimer’s disease group, and 128 participants with cognitive impairment from other known causes. Significant differences in demographics, florbetapir PET, APOE, and neuropsychological tests were found among the groups. Eight hundred and seventeen participants (33.3%) completed the florbetapir PET scanning. Non-demented individuals with higher age, lower education years, male, with a family history of dementia, and higher self-report depression prefer to undergo PET scans. Acceptance of PET scans did not correlate with objectively assessed cognitive impairment. The Chinese Preclinical Alzheimer’s Disease Study was designed to establish a large-scale cohort with comprehensive data collection. Our findings may help to understand the factors affecting the acceptance of β-amyloid PET in urban areas of China and help us address the low acceptance challenge.

Chun Yi Lo

Papers

Diffusion Tensor Tractography Reveals Abnormal Topological Organization in Structural Cortical Networks in Alzheimer's Disease

Abnormal topological organization of structural brain networks in schizophrenia.

Disrupted thalamo-cortical connectivity in schizophrenia: A morphometric correlation analysis

Chinese Preclinical Alzheimer’s Disease Study (C-PAS): Design and Challenge from PET Acceptance