Elsevier

NeuroImage

Volume 73, June 2013, Pages 239-254
NeuroImage

Comments and Controversies
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI

https://doi.org/10.1016/j.neuroimage.2012.06.081Get rights and content

Abstract

Diffusion-weighted MRI (DW-MRI) has been increasingly used in imaging neuroscience over the last decade. An early form of this technique, diffusion tensor imaging (DTI) was rapidly implemented by major MRI scanner companies as a scanner selling point. Due to the ease of use of such implementations, and the plausibility of some of their results, DTI was leapt on by imaging neuroscientists who saw it as a powerful and unique new tool for exploring the structural connectivity of human brain. However, DTI is a rather approximate technique, and its results have frequently been given implausible interpretations that have escaped proper critique and have appeared misleadingly in journals of high reputation. In order to encourage the use of improved DW-MRI methods, which have a better chance of characterizing the actual fiber structure of white matter, and to warn against the misuse and misinterpretation of DTI, we review the physics of DW-MRI, indicate currently preferred methodology, and explain the limits of interpretation of its results. We conclude with a list of ‘Do's and Don'ts’ which define good practice in this expanding area of imaging neuroscience.

Introduction

Diffusion weighted MRI (DW-MRI) (Behrens and Johansen-Berg, 2009, Jones, 2010a, Le Bihan and Breton, 1985, Le Bihan et al., 1986) is currently the only method capable of mapping the fiber1 architecture of tissue (e.g., nervous tissue, muscle) in vivo and, as such, it has triggered tremendous hopes and expectations. As the technique has matured, an increasing number of software packages have been developed that allow such data to be analyzed in a push-button manner — sometimes to such an extent that the end-user need not know anything about the underlying physics, and yet are still able to derive a p-value which can be interpreted according to the hypotheses being tested. There are, however, a substantial number of pitfalls associated with these methods (see, e.g., Jones, 2010b, Jones, 2010c, Jones and Cercignani, 2010, Le Bihan et al., 2006), which can lead to biased or, in some cases, completely fallacious conclusions being drawn. What is not in question, however, is that DW-MRI carries invaluable in vivo information about tissue microstructure, but in order to extract this information in the most efficient and unbiased way, it is important to make the right choices for the acquisition and analysis of these data, and, even more importantly, for the interpretation of the results.

It is in this context that this article focuses on three issues. The first of these is: What exactly are we measuring with DW-MRI, i.e., what is the immediate meaning of the data we get from the technique? The second is: What questions are we trying to answer on the basis of DW-MRI? The link between these two issues is the basis for the third issue, interpretation. Our main focus is on measurements within white matter of the live human brain, although many of the issues discussed here are equally relevant to pre-clinical studies in animal models, and of tissues other than brain.

The target audience of this article is the typical ‘end user’ who has access to diffusion-weighted MR sequences, provided by the MR scanner manufacturer, and uses ‘push-button’ software packages to analyze their data to look for group differences or structure–function correlations. It is our opinion that, without basic insight into the fundamental principles of the method and, most importantly, its limitations and pitfalls, misunderstandings, misconceptions and misinterpretation will be perpetuated. Our aim is to provide this grounding to the aforementioned target audience.

Section snippets

What does diffusion-weighted MR imaging actually measure?

Diffusion-weighted MRI measures just one thing — the dephasing of spins of protons in the presence of a spatially-varying magnetic field (‘gradient’). The mechanism of interest here is the phase change resulting from components of incoherent displacement of spins along the axis of the applied field gradient, which changes their Larmor frequency. The longer the protons are allowed to diffuse (the ‘diffusion time’, Δ) and the higher the mean squared displacement per unit time of the molecules

What questions do we ask of the DW-MRI data?

There are, of course, myriad questions that are asked of DW-MRI data — but these can be grouped into classes.

One important class is concerned with the trajectory of fiber pathways and their interpretation in terms of anatomical connectivity, and includes questions of the form: “Which gray matter regions are inter-connected by white matter fibers?”; “Where do these fibers pass?”; and “How strong are these connections?”4

Interpretation

The interpretation of DW-MRI data is essentially a model based procedure, even if no formal, mathematically described model is invoked, i.e., the measured data are combined with a number of assumptions about the underlying processes and structures. These model assumptions always represent a simplification of reality, i.e., they neglect certain aspects of the true generative mechanism of the data. For the choice of the model, three aspects are important: (a) the quality and quantity of the

Conclusions

As we stated at the outset, the only thing that that we can say with any certainty in diffusion MRI is that we measure a signal change when a motion-sensitizing gradient is applied along a given axis. Inferring anything else is dependent on the quality of the model and the quality of the data. There are many mechanisms by which the diffusion weighted signal can be modulated. This includes but is not limited to, the myelination, the axon density, the axon diameter, the permeability of the

The do's

  • a.

    Carefully consider the question(s) to be asked of the data and consider whether the data acquisition/analysis allows you to answer these questions.

    As most of the recommendations given below (small voxels, many directions, high diffusion weighting, high SNR) are in mutual competition, the user has to decide where to invest the precious acquisition time. For simple questions such as unspecific white matter differences between two groups, there are minimal demands on the data acquisition and

The don'ts

  • a.

    Don't assume that the principal eigenvector of a diffusion tensor is a good indication of the actual fiber orientations in all voxels.

    Although in a limited set of places (where the bundle-to-voxel size is favorable and all fibers are highly parallel in the voxel), the principal eigenvector may do a good job, it is unsafe to use this simple model throughout the whole brain.

  • b.

    Don't assume that tractography using a single diffusion tensor will be adequate for all fiber trajectories in the brain.

    Most

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