Reproducibility of graph metrics of human brain structural networks

Recent interest in human brain connectivity has led to the application of graph theoretical analysis to human brain structural networks, in particular white matter connectivity inferred from diffusion imaging and fiber tractography. While these methods have been used to study a variety of patient populations, there has been less examination of the reproducibility of these methods. A number of tractography algorithms exist and many of these are known to be sensitive to user-selected parameters. The methods used to derive a connectivity matrix from fiber tractography output may also influence the resulting graph metrics. Here we examine how these algorithm and parameter choices influence the reproducibility of proposed graph metrics on a publicly available test-retest dataset consisting of 21 healthy adults. The dice coefficient is used to examine topological similarity of constant density subgraphs both within and between subjects. Seven graph metrics are examined here: mean clustering coefficient, characteristic path length, largest connected component size, assortativity, global efficiency, local efficiency, and rich club coefficient. The reproducibility of these network summary measures is examined using the intraclass correlation coefficient (ICC). Graph curves are created by treating the graph metrics as functions of a parameter such as graph density. Functional data analysis techniques are used to examine differences in graph measures that result from the choice of fiber tracking algorithm. The graph metrics consistently showed good levels of reproducibility as measured with ICC, with the exception of some instability at low graph density levels. The global and local efficiency measures were the most robust to the choice of fiber tracking algorithm.

• J. T. Duda, P. A. Cook, and J. C. Gee, “Reproducibility of graph metrics of human brain structural networks.,” Front Neuroinform, vol. 8, p. 46, 2014. 

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Structural and functional connectivity have network-wide influences upon cognitive performance

In this paper functional subnetworks in the brain were examined using MRI to measure both structural connectivity and functional connectivity. Additionally, the influence on behavior of both types of connectivity examined to determine the degree to which each provides unique information as well as how this information may be used to identify the parts of a network that are most influential on behavioral performance. Functional connectivity involves co-activation of brain regions during performance of a task while brain recruitment is monitored with fMRI. Structural connectivity is related to the long tract white matter projections that may integrate recruited brain regions biologically. Here we demonstrate how structural and functional connectivity may be used to examine small, functionally defined subnetworks in the brain during performance of a common language task. Functionally defined cortical regions are used along with a population-averaged diffusion tensor atlas to identify the white matter pathways that provide the basis for biological connectivity. A centerline-based method is used to provide a geometric model that facilitates the equidimensional comparison of functional and structural connectivity within a network. Behavioral data are used to identify the relative contributions of function and structure, and the degree to which each provides unique insight into behavior.

Duda, Jeffrey T., “Characterizing Connectivity In Brain Networks Using Magnetic Resonance Imaging” (2010). Publicly accessible Penn Dissertations. Paper 191.

Structural connectivity disruptions after traumatic brain injury

In each hemisphere of the brain, the thalamus and three cortical subregions in the prefrontal cortex were identified and used along with diffusion tensor based fiber tractography to model the white matter fiber bundles that connect the thalamus to each cortical region.

Traumatic brain injury (TBI) is one of the most common causes of long-term disability. Each year, approximately 1.5 million people sustain TBI in the United States alone, causing billions of dollars of economic cost. Among the survivors, many individuals are left with significant long-term cognitive and motor disabilities. However, efforts to identify the neuropathologic correlates of these deficits have gained only limited success to date. The use of more sensitive and reliable in vivo neuroimaging protocols may facilitate the identification of specific brain-behavior relationships in the TBI population. Here we present a study that explores novel methodologies for examining neuroimaging data to gain further insight into TBI.

Two different types of Magnetic Resonance Images (MRI) are used: diffusion tensor (DT) images quantify connectivity patterns in the brain while the T1 modality provides high-resolution images of tissue interfaces. Our objective is to use both modalities to build subject-specific, quantitative models of fiber connections in order to discover effects specific to a neural system. We first use a population-specific average T1 and DT template to label the thalamus and cortical regions of interest. We then build an expected connection model (illustrated above) within this template space that is transferred to subject space in order to provide a prior restriction on probabilistic tracking performed in subject space. This allows for the comparison of properties such as fractional anisotropy (FA) within a common framework along fiber pathways.

Students t-test results after FDR correction at p<0.02 indicate that the left hemisphere connection between thalamus and Brodmann area 10 is affected by TBI. Arrows indicate regions where TBI survivors show reduced FA compared to controls. A sagittal slice from the T1 component of the template is shown for anatomical reference.

J. T. Duda, B. B. Avants, J. Kim, H. Zhang, S. Patel, J. Whyte, and J. C. Gee, “Multivariate Analysis of Thalamo-Cortical Connectivity Loss in TBI,” in Proc. Computer Vision and Pattern Recognition, Ninth IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), Anchorage, AK, 2008.