  Diffusion-weighted magnetic resonance imaging (dMRI) offers unique
capabilities including noninvasive probing of brain's tissue microstructure and
structural connectivity. It is widely used for clinical assessment of brain
pathologies and for neuroscience research. Analyzing the dMRI data to extract
useful information for medical and scientific purposes can be challenging. The
dMRI measurements often suffer from strong noise and artifacts, there is
usually high inter-session and inter-scanner variability in the data, and
considerable inter-subject heterogeneity in brain structure. Moreover, the
relationship between measurements and the phenomena of interest can be highly
complex. Recent years have witnessed increasing use of machine learning methods
for dMRI analysis. This manuscript aims to assess these efforts, with a focus
on methods that have addressed data preprocessing and harmonization,
microstructure mapping, tractography, and white matter tract analysis. We study
the main findings, strengths, and weaknesses of the existing methods and
suggest topics for future research. We find that machine learning may be
exceptionally suited to tackle some of the difficult tasks in dMRI analysis.
However, for this to happen, several shortcomings of existing methods and
critical unresolved issues need to be addressed. These include deficient
evaluation practices, lack of rich training datasets and validation benchmarks,
as well as model generalizability, reliability, and explainability concerns.
