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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Relating Cerebral Blood Flow to Structural & Functional Metrics in Typically Developing Children

Sample slices from the multivariate atlas used as a basis for neuro-anatomical comparison.

Purpose: To evaluate the relationships between cerebral blood flow and other magnetic resonance (MR) imaging based measures such as fractional anisotropy, magnetic transfer ratio, cortical thickness and mean resting state BOLD signal in typically developing children.

Methods: Eighty-eight children aged 7-17 underwent pseudo-continuous arterial spin-labeled perfusion MRI (pCASL) [1] examinations along with anatomical (T1), diffusion tensor (DTI), magnetic transfer (MT) and BOLD resting state functional MRI (rs-fMRI) examinations. For each imaging modality, the ANTs [2] toolkit was used to create a modality-specific template from a subset (n=30) of the subjects. For non-scalar modalities, derived scalar images were used for template building. For pCASL the mean CBF image was used; for DTI the average diffusion weighted image was used; for rs-fMRI the mean BOLD image was used; and for MT the M0 image was used. Each modality-specific template was then registered to the T1 template to obtain a single multi-modality template (MMT). The T1 component of the MMT was then brain-masked, labeled, and three-tissue segmented using the Atropos segmentation tool [2]. For each subject, each modality was aligned to the corresponding component of the MMT for brain-masking and labeling. Intra-subject registrations were then performed to align all modalities to each subject’s T1 image. To provide a basis for comparison, a scalar metric was derived for each image modality. For pCASL the mean CBF was calculated; for T1 images, the cortical thickness was measured using the DiRECT method; fractional anisotropy was calculated from the DTI; the magnetization transfer ratio (MTR) was calculated from the MT images; and mean BOLD signal was calculated from the resting state fMRI data.

Results: Regularized canonical correlation analysis, as implemented in the sscan tool [2], was used to identify the relationship between CBF and each of the additional modalities. The analysis of each modality type is restricted to the most informative tissue type for that modality. For CBF, rs-fMRI and cortical thickness, only values in gray matter are examined, while only values in white matter are examined for FA and MTR.

Discussion: To the best of our knowledge, this is the first study to simultaneously compare CBF to cortical thickness, fractional anisotropy, magnetization transfer ratio and mean resting BOLD signal in a single population. In doing so, we hope to gain insight regarding the degree to which CBF provides statistically unique information in relation to these additional MR imaging modalities. Additionally, the development of the framework for analyzing these modalities provides a basis for future studies to explore the relationship between CBF and network based measures of both structural and functional connectivity.

Conclusion: The relationship between cortical thickness and Mean CBF (R²=0.4777) was the strongest of the metrics examined. In white matter, the MTR (R²=0.3126)  was stronger than FA (R²=0.1462). The mean BOLD (R²=0.1414) metric was the weakest.

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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.