Elsevier

NeuroImage

Volume 104, 1 January 2015, Pages 253-265
NeuroImage

The effects of SIFT on the reproducibility and biological accuracy of the structural connectome

https://doi.org/10.1016/j.neuroimage.2014.10.004Get rights and content

Highlights

  • Evaluated effects of SIFT method on quantitative tractography applications

  • Synthetic phantom demonstrates vital importance of SIFT.

  • SIFT reduces scan–rescan variability of structural connectome construction.

  • SIFT improves biological accuracy of structural connectome.

Abstract

Diffusion MRI streamlines tractography is increasingly being used to characterise and assess the structural connectome of the human brain. However, issues pertaining to quantification of structural connectivity using streamlines reconstructions are well-established in the field, and therefore the validity of any conclusions that may be drawn from these analyses remains ambiguous. We recently proposed a post-processing method entitled “SIFT: Spherical-deconvolution Informed Filtering of Tractograms” as a mechanism for reducing the biases in quantitative measures of connectivity introduced by the streamlines reconstruction method. Here, we demonstrate the advantage of this approach in the context of connectomics in three steps. Firstly, we carefully consider the model imposed by the SIFT method, and the implications this has for connectivity quantification. Secondly, we investigate the effects of SIFT on the reproducibility of structural connectome construction. Thirdly, we compare quantitative measures extracted from structural connectomes derived from streamlines tractography, with and without the application of SIFT, to published estimates drawn from post-mortem brain dissection. The combination of these sources of evidence demonstrates the important role the SIFT methodology has for the robust quantification of structural connectivity of the brain using diffusion MRI.

Introduction

Diffusion MRI streamlines tractography is increasingly being used as one of many image analysis tools in the rapidly-evolving field of connectomics (Hagmann, 2005, Sporns et al., 2005). In this framework, the grey matter of the brain is parcellated in some manner, and the connections reconstructed using streamlines tractography used to infer structural connectivity between the parcellated areas (Hagmann et al., 2008). This allows for the evaluation of the resulting ‘connectome’ matrix using a wide range of analysis tools made possible using graph theory to make inferences about the connectional architecture of the brain (Bullmore and Sporns, 2009, Rubinov and Sporns, 2010), or perturbations to this connectivity in pathology and disease (Bassett and Bullmore, 2009, Griffa et al., 2013).

Most studies to date have used the number of streamlines connecting each node pair as a measure of ‘connection density’. This is however contrary to a fundamental limitation of streamlines tractography approaches that is well-established in the field: streamline count is not a valid marker of axonal fibre count (Jones et al., 2013). Construction of the structural connectome (or indeed any other quantitative method) using streamline count alone is therefore inadvisable. The approaches employed in the literature for addressing this issue include the following:

  • Employing heuristics that make estimates regarding the nature of biases in the streamlines reconstruction process (e.g. increased streamline seeding in longer pathways) in order to explicitly correct for them (e.g. Hagmann et al., 2007, Colon-Perez et al., 2012). This assumes that the heuristics employed are a complete parameterization of the reconstruction biases present in the data, which is not guaranteed.

  • Applying a threshold to generate a binary connectivity matrix in an attempt to circumvent the non-quantitative nature of streamline-based connectivity. This ‘connected-or-not-connected’ interpretation of structural connectivity may enable various sophisticated graph-theoretic analysis methods, but is not reflective of the actual underlying structure of the brain, where a wide spectrum of connectivity strength exists between various regions (Markov et al., 2011).

  • Calculation of some quantitative parameter along the pathway connecting the nodes, rather than (or in addition to) the streamline counts (e.g. Hagmann et al., 2010, Lo et al., 2010, Pannek et al., 2013). This is comparable to using the streamlines connecting each node pair to define a mask in a voxel-based analysis, so may not be an appropriate metric for use in more complex graph-theoretic analyses due to the interpretation of the relevant quantitative parameter or the confound of crossing fibres.

We recently proposed the “Spherical-deconvolution Informed Filtering of Tractograms (SIFT)” algorithm as a mechanism for addressing these quantification issues (Smith et al., 2013). By imposing a model that maps a streamlines reconstruction back to the acquired diffusion image data, and modifying a reconstruction to improve its correspondence with the image data given this model, the number of streamlines connecting two regions of the brain becomes a proportional estimate of the total cross-sectional area of the white matter fibre pathway connecting those regions; this is inherently a highly biologically relevant measure of ‘structural connectivity’. We therefore advocate that such a processing step is essential to ensure that any conclusions drawn about the structural connectedness of the brain, or differences in structural connectivity between subjects, are biologically relevant and not due to systematic errors in reconstruction processes and analyses.

In this work, we interrogated the effects of the SIFT algorithm on the estimated structural connectome in three ways. Firstly, we use a simple synthetic phantom to highlight the importance of imposing such a model on a streamlines reconstruction before the connectome is generated. Secondly, we investigated the effects of SIFT on the reproducibility of the structural connectome. Thirdly, we compared the tractograms and connectomes with and without the application of SIFT to quantitative and qualitative estimates of white matter connectivity derived from published post mortem brain dissection experiments; given the lack of a true quantitative gold standard to assess whole-brain tracking results, these ex vivo results provide important (if imperfect) measures of white matter connectivity from a source other than diffusion MRI to which streamlines reconstructions may be compared.

Section snippets

Implications of the SIFT model

In the SIFT method, a simple model is imposed that maps a streamlines reconstruction back to the measured diffusion signal, as a means for reducing some of the major reconstruction biases of streamlines tractography. The model is described in detail in Smith et al. (2013), and is summarized briefly below:

  • The density of a discrete fibre population within any voxel in the image can be estimated (up to a global scaling factor) using the integral of the relevant lobe of the Fibre Orientation

Results

Fig. 3 shows examples of the four types of streamlines reconstructions, and the resulting connectome matrices, using data from the intra-scan case. For visualization purpose, the streamlines reconstructions are displayed using Track Density Imaging (TDI) (Calamante et al., 2010), which allows for visualisation of streamlines density throughout the example slice, providing a succinct visual representation of the differences introduced by varying the type of reconstruction in the relative

Discussion

We have previously proposed the SIFT method as a mechanism for reducing some of the reconstruction biases associated with diffusion MRI streamlines tractography, by imposing physical volumetric constraints that are otherwise ignored by the streamlines reconstruction process (Smith et al., 2013). By doing so, each reconstruction should more closely mimic the underlying biology by design. In this study, we have provided three principal sources of evidence to support the hypothesis that

Conclusion

In order for the analysis of a brain structural connectome to be valid and its results to be interpretable, it is essential that the derivation of the underlying connection densities between brain regions be robust and biologically meaningful. We have shown that due to the physical underpinnings of the SIFT method, its application results in estimates of structural connectivity that are both more reproducible, and more representative of the underlying biological white matter connectivity. This

Acknowledgments

We are grateful to the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council, Austin Health, and the Victorian Government's Operational Infrastructure Support Program for their support. This research was supported by a Victorian Life Sciences Computation Initiative (VLSCI) grant [VR0271] on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government, Australia.

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  • Cited by (0)

    1

    Current address: Centre for the Developing Brain, King's College London, London, United Kingdom.

    2

    Current address: Department of Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom.

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