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A symmetry-based method to infer structural brain networks from probabilistic tractography data.

Constantine Dovrolis1*, Kamal Shadi1, Helen S. Mayberg2 and David A. Gutman3
  • 1 Georgia Tech, School of Computer Science, United States
  • 2 Emory University - School of Medicine, Department of Psychiatry and Neurology, United States
  • 3 Emory University - School of Medicine, Department of Biomedical Informatics, United States

Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP) have provided brain research with an abundance of structural connectivity data. A major challenge in this research effort is that the inferred brain networks, as well as their topological properties, are often sensitive to the parameters of the tractography process. In probabilistic tractography, the most critical of those parameters is the connectivity threshold τ that determines whether the tractography-generated streamlines from a given seed voxel to a target ROI occur with sufficiently large probability to indicate the presence of an actual connection. This work focuses on the following problem: how to infer the structural network between a given set of grey matter ROIs in a reliable way that does not require an arbitrary choice of the connectivity threshold? The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA), exploits a fundamental limitation of diffusion MRI imaging and of the tractography process: diffusion MRI can estimate the orientation of fibers in each voxel but it cannot infer the polarity (afferent versus efferent) of those fibers. Similarly, a tractography algorithm can combine those per-voxel orientations to “stitch together” expected connections but it does not provide any information about the direction of those connections. Given this limitation, MANIA expects that the presence of an actual connection from voxel X to voxel Y (in that direction) will be detected by the tractography process as a symmetric connection between X and Y. Similarly, if there is no connection between X and Y, the tractography process should not detect a connection in either direction. Based on this principle, MANIA formulates the network inference problem as an optimization over the range of connectivity threshold values: it selects the value of τ that minimizes the asymmetry of the resulting network. The network asymmetry is normalized relative to the asymmetry that would be expected due to chance alone in a random network of the same density. MANIA is based on the premise that there is an ideal value (or range of values) of the connectivity threshold that can correctly classify every directed pair of ROIs as either “connection exists” or “connection does not exist”. When such a threshold exists, it will result in a completely symmetric network (because a perfectly accurate tractography-based network cannot be asymmetric). On the other hand, if such an ideal threshold does not exist (for instance, it may be that two connected ROIs are too far from each other and tractography cannot “see” their connection, or that it is impossible for streamlines to cross the white matter/grey matter boundary of a certain ROI in one direction but not in the opposite), then MANIA aims to at least minimize the normalized asymmetry metric, even if the resulting network will not be completely symmetric. MANIA can work in tandem with probabilistic tractography methods, such as FSL’s probtrackx, PiCo, and fDF-PROBA. It can be also combined with deterministic tractography methods, such as FACT, but only if a large number of streamlines (in the thousands) are generated from randomly placed seeds within each voxel. We evaluate the accuracy of MANIA based on synthetically generated data in which the ground-truth network is known. We also compare MANIA with an ideal threshold-based method in which the optimal connectivity threshold is assumed to be known. Further, we show how to associate a confidence level with each edge, and how to apply MANIA in a group of subjects (based on a "rank aggregation" algorithm). As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects to infer the structural network between 18 corticolimbic ROIs that are implicated with neuropsychiatric disorders such as major depressive disorder (MDD) or post traumatic stress disorder (PTSD). We measured the “centrality” of each node in the rank-aggregated network, based on four centrality metrics (degree, closeness, betweenness, PageRank). Different centrality metrics focus on different notions of importance. The betweenness centrality of a node X focuses on the number of shortest paths between any pair of nodes that go through X. BA25 (subcallosal cingulate) is the most important node from this perspective because it serves as the “unique bridge” between six ROIs of the limbic system and 9 cortical ROIs. BA25 is also the most central node in terms of its average distance to all other nodes (closeness centrality). Similarly, we measured the edge centrality of all connected node pairs. In terms of edge betweenness centrality, the connection between BA25 and the Nucleus Accumbens (Acb) is by far the most central in this network. It is interesting to note that this edge includes the segment of white matter that is the target of Deep Brain Stimulation (DBS) therapies for the treatment of Major Depressive Disorder. In fact, the DBS target is typically the point at which the fibers between (BA25-Acb), (BA25-BA32) and (BA25-BA24) intersect.

References

Bassett, D. S., Brown, J. A., Deshpande, V., Carlson, J. M., Grafton, S. T. (2011). Conserved and variable architecture of human white matter connectivity. NeuroImage, 54(2), 1262-1279.

Bastiani, M., Shah, N. J., Goebel, R., Roebroeck, A. (2012). Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm. NeuroImage, 62(3), 1732-1749.

Cheng, H., Wang, Y., Sheng, J., Sporns, O., Kronenberger, W. G., Mathews, V. P., et al. (2012). Optimization of seed density in DTI tractography for structural networks. Journal of neuroscience methods, 203(1), 264-272.

de Reus, M. A., van den Heuvel, M. P. (2013a). Estimating false positives and negatives in brain networks. NeuroImage, 70, 402-409.

de Reus, M. A., Van den Heuvel, M. P. (2013b). The parcellation-based connectome: limitations and extensions. NeuroImage, 80, 397-404.

Jones, D. K., Knösche, T. R., Turner, R. (2013). White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. NeuroImage, 73, 239-254.

Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., et al. (2005). Deep brain stimulation for treatment-resistant depression. Neuron, 45(5), 651-660.

Morris, D. M., Embleton, K. V., Parker, G. J. (2008). Probabilistic fibre tracking: differentiation of connections from chance events. NeuroImage, 42(4), 1329-1339.

Sporns, O. (2012). Discovering the human connectome: MIT press.

Thomas, C., Frank, Q. Y., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., et al. (2014). Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proceedings of the National Academy of Sciences, 111(46), 16574-16579.

Keywords: diffusion MRI, tractography, Network analysis, Depressive Disorder, Major, connectomics

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Investigator presentations

Topic: Neuroimaging

Citation: Dovrolis C, Shadi K, Mayberg HS and Gutman DA (2016). A symmetry-based method to infer structural brain networks from probabilistic tractography data.. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00001

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Received: 26 May 2016; Published Online: 18 Jul 2016.

* Correspondence: Prof. Constantine Dovrolis, Georgia Tech, School of Computer Science, Atlanta, GA, 30332, United States, constantine@gatech.edu