Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review

Abstract

Stroke is a leading cause of disability worldwide. Motor impairments occur in most of the patients with stroke in the acute phase and contribute substantially to disability. Diffusion tensor imaging (DTI) biomarkers such as fractional anisotropy (FA) measured at an early phase after stroke have emerged as potential predictors of motor recovery. In this narrative review, we: (1) review key concepts of diffusion MRI (dMRI); (2) present an overview of state-of-art methodological aspects of data collection, analysis and reporting; and (3) critically review challenges of DTI in stroke as well as results of studies that investigated the correlation between DTI metrics within the corticospinal tract and motor outcomes at different stages after stroke. We reviewed studies published between January, 2008 and December, 2018, that reported correlations between DTI metrics collected within the first 24 h (hyperacute), 2-7 days (acute), and >7-90 days (early subacute) after stroke. Nineteen studies were included. Our review shows that there is no consensus about gold standards for DTI data collection or processing. We found great methodological differences across studies that evaluated DTI metrics within the corticospinal tract. Despite heterogeneity in stroke lesions and analysis approaches, the majority of studies reported significant correlations between DTI biomarkers and motor impairments. It remains to be determined whether DTI results could enhance the predictive value of motor disability models based on clinical and neurophysiological variables.

Keywords: corticospinal tract (CST); diffusion MRI (dMRI); diffusion tensor imaging (DTI); motor stroke; stroke recovery; white matter (WM).

Figure 1

Model of the tensor showing the eingenvalues. Diffusivities are depicted in FA representation (λ_ll—parallel or axial diffusivity—AD, λ_⊥perpendicular or radial diffusivity—RD).

Figure 2

DTI maps generated as output of tensor estimation. FA maps in two versions, the second depicted in RGB colors. Maps were generated in ExploreDTI.

Figure 3

Commonly used seeds, inclusion and exclusions masks for corticospinal tract (CST) DTI-based tractography: (A) probabilistic of corticospinal tract (CST) showing commonly chosen masks/ROIs. (B) deterministic, showing streamlines. The pons was an inclusion mask in this example. Extraction of metrics can also be performed from this ROI in the pons, in the internal capsule, the entire CST or other parts of the tract.

Figure 4

(A) Tensor in a region of crossing fibers, when two populations of fibers cross (in this particularly case, at 90 degrees), the tensor fails in the interpretation of the diffusion signal, suggesting low FA (approximately isotropic diffusion). (B) Crossing fibers at the centrum semiovale, the ‘ground truth’ signal within a voxel. Constrained Spherical Deconvolution (CSD) depicts two populations of fibers while DTI depicts a single population of fibers.