Microstructure-Informed Tractography
Simona Schiavi1,2
1Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy, 2Department of Computer Science, University of Verona, Verona, Italy

Synopsis

Microstructure-informed tractography is a relatively new area of research that aims at combining tractography with tissue microstructural information in the pursuit of more quantitative and biologically oriented estimation of brain connectivity. This lecture introduces key concepts and motivations behind microstructure informed tractography and motivates why they are relevant in the context of quantifying structural connectivity. Following this lecture, researchers and clinicians who are interested in structural connectivity will learn how to build more veridical and quantitative connectomes using diffusion MRI and multimodal acquisitions.

Target audience

Researchers and clinicians who are interested in understanding how to improve the accuracy and interpretability of structural connectivity estimates by combining tractography with tissue microstructural models.

Objective

The audience will learn to be cautious of the limitations of standard tractography and connectome reconstruction. Moreover, this lecture will introduce the connection density biases1 and the false positives2,3 issues as well as which are the existing methods to reduce them.

Purpose

The purpose is to learn how to use tractography in combination with microstructural information coming from dMRI and other multimodal techniques as well as to be cautious of the important methodological choices to make.

Methods

Microstructure-informed tractography is a relatively new area of research that aims at combining tractography with tissue microstructural information in the pursuit of a more quantitative and biologically oriented estimation of brain connectivity4. Indeed, while streamline tractography provides estimates of structural connection trajectories that are consistent with the underlying fiber orientations, it does not provide any guarantees regarding consistency between the number of such reconstructed connections with the density of those underlying fibers (i.e., the actual number of axons in a white matter region)5,6. This limitation is often expressed as the mantra “streamline count is not quantitative”. The class of algorithms that tries to solve this issue are often referred to as global approaches and can be divided into two main categories based on the strategy used to perform the reconstruction: bottom-up (or generative) and top-down (or discriminative). Bottom-up methods reconstruct the streamlines starting from their fundamental constituent parts, i.e., small segments, which represent short portions of fiber tracts and for which it is possible to determine the corresponding signal contributions to the acquired dMR image using generative models. Streamlines are formed by altering the position/shape of these fragments and encouraging them to form continuous and smooth trajectories and to adapt to the underlying signal7–11. Top-down methods, instead, start from an already constructed whole-brain tractogram and split each streamline into segments inside different voxels trying to determine their contribution to the acquired dMR signal. The problem is solved globally so the contribution of all the segments of the same streamline, scaled by their length in each voxel, must remain constant along the entire trajectory. At this point, the streamlines not compatible with the signal are discarded from the tractogram (filtering), and the contribution to the dMR signal is assigned to each remaining trajectory to scale their value for structural connectivity estimation (streamline contributions)12–21. Moreover, very recent developments of these techniques have been proposed to additionally inject anatomical priors19,20 and exploit multimodal acquisitions22,23 to further boost the specificity of the reconstructions without affecting the sensitivity and provide more accurate estimates of connectivity.

Acknowledgements

Prof. Alessandro Daducci at the University of Verona for helping in the preparation of this course.

References

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23. Barakovic M, Tax CMW, Rudrapatna U, et al. Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation. Neuroimage. 2021;227:117617. doi:https://doi.org/10.1016/j.neuroimage.2020.117617

Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)