Abstract

This article focuses attention on the pressing need to think carefully and deeply about the current state of the art in using measurements of tissue microstructure derived from MRI to explain individual differences in brain function, electrophysiology and or cognitive function1. Although initial effort in the application of microstructural imaging was on voxel-based metrics derived from diffusion tensor magnetic resonance imaging (DT-MRI), such as fractional anisotropy (FA) and the mean diffusivity (MD), there is increasing realisation of the limitations of this approach both in terms of biological specificity and in terms of interpretability of any results that emerge. This has led to the development of alternative approaches that are (i) looking at topologies of networks derived from diffusion-MRI-based fibre-tracking approaches, (ii) adopting “advanced” diffusion MRI metrics that go beyond the tensor model, or (iii) looking at data from non-diffusion-based MRI contrasts, such as those based on magnetization transfer, multi-component relaxometry, or susceptibility-weighted imaging. With the increasing availability of methods to extract such metrics, and ease of access, it should be stressed that our application of such methods is outpacing our understanding of what aspect of biology the metrics are actually capturing. As such, there is a danger of operating in an unprincipled and unstructured fashion. This article argues that the “missing link” is a non-invasive neuroimaging metric that is not only well understood, but which also can reasonably be expected to explain variance in brain function from a biological perspective, rather than a metric that is used purely as a matter of convenience.

Highlights

  • The UK national rail networkThe United Kingdom is served by a “national rail network” that comprises mainline and local train services that link small villages to large cities

  • Initial effort in the application of microstructural imaging was on voxel-based metrics derived from diffusion tensor magnetic resonance imaging (DT-MRI), such as fractional anisotropy (FA) and the mean diffusivity (MD), there is increasing realisation of the limitations of this approach both in terms of biological specificity and in terms of interpretability of any results that emerge

  • This has led to the development of alternative approaches that are (i) looking at topologies of networks derived from diffusion-MRI-based fibre-tracking approaches, (ii) adopting “advanced” diffusion MRI metrics that go beyond the tensor model, or (iii) looking at data from non-diffusion-based MRI contrasts, such as those based on magnetization transfer, multi-component relaxometry, or susceptibility-weighted imaging

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Summary

SUMMARY

This article focuses attention on the pressing need to think carefully and deeply about the current state of the art in using measurements of tissue microstructure derived from MRI to explain individual differences in brain function, electrophysiology and or cognitive function. Initial effort in the application of microstructural imaging was on voxel-based metrics derived from diffusion tensor magnetic resonance imaging (DT-MRI), such as fractional anisotropy (FA) and the mean diffusivity (MD), there is increasing realisation of the limitations of this approach both in terms of biological specificity and in terms of interpretability of any results that emerge. This has led to the development of alternative approaches that are (i) looking at topologies of networks derived from diffusion-MRI-based fibre-tracking approaches, (ii) adopting “advanced” diffusion MRI metrics that go beyond the tensor model, or (iii) looking at data from non-diffusion-based MRI contrasts, such as those based on magnetization transfer, multi-component relaxometry, or susceptibility-weighted imaging.

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