Diffusion tensor imaging for multilevel assessment of the visual pathway: possibilities for personalized outcome prediction in autoimmune disorders of the central nervous system

Abstract

The afferent visual pathway represents the most frequently affected white matter pathway in multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD). Diffusion tensor imaging (DTI) can reveal microstructural or non-overt brain tissue damage and quantify pathological processes. DTI facilitates the reconstruction of major white matter fiber tracts allowing for the assessment of structure-function and damage-dysfunction relationships. In this review, we outline DTI studies investigating the afferent visual pathway in idiopathic optic neuritis (ON), NMOSD, and MS. Since MS damage patterns are believed to depend on multiple factors, i.e., ON (anterior visual pathway damage), inflammatory lesions (posterior visual pathway damage), and global diffuse inflammatory and neurodegenerative processes, comprehensive knowledge on different contributing factors using DTI in vivo may advance our understanding of MS disease pathology. Combination of DTI measures and visual outcome parameters yields the potential to improve routine clinical diagnostic procedures and may further the accuracy of individual prognosis with regard to visual function and personalized disease outcome. However, due to the inherent limitations of DTI acquisition and post-processing techniques and the so far heterogeneous and equivocal data of previous studies, evaluation of the true potential of DTI as a possible biomarker for afferent visual pathway dysfunction is still substantially limited. Further research efforts with larger longitudinal studies and standardized DTI acquisition and post-processing validation criteria are needed to overcome current DTI limitations. DTI evaluation at different levels of the visual pathway has the potential to provide markers for individual damage evaluation in the future. As an imaging biomarker, DTI may support individual outcome prediction during personalized treatment algorithms in MS and other neuroinflammatory diseases, hereby leveraging the concept of predictive, preventive, and personalized medicine in the field of clinical neuroimmunology.

Keywords: DTI; Diffusion tensor imaging; Multiple sclerosis; Neuromyelitis optica spectrum disorders; Optic neuritis; Predictive preventive personalized medicine; Visual pathway.

Fig. 1

Selection of visual pathway anatomical structures and assessment methods. Important anatomical structures are displayed: retina, optic nerve, optic chiasm, and optic tract are parts of the anterior visual pathway and lateral geniculate nucleus (LGN), optic radiation, and primary visual cortex are parts of the posterior visual pathway. Optic neuritis within the optic nerve, OR lesions within the optic radiation, and V1 atrophy of the visual cortex are displayed as typical damage patterns in neuroinflammatory diseases, e.g., multiple sclerosis. Whereas HCVA and LCVA may assess the overall functionality of the visual system, other methods provide information on different visual system parts, i.e., OCT of the retina, DTI-based tractography of the optic radiation and functional MRI of the visual cortex. MfVEP evaluates latency delays along the entire pathway from optic nerve to V1 area. This figure was made by use of InkScape (https://inkscape.org/en/). OR lesion optic radiation lesion, V1 atrophy primary visual cortex atrophy, DTI diffusion tensor imaging, OCT optical coherence tomography, mfVEP multifocal visual evoked potentials, HCVA high-contrast visual acuity, LCVA low-contrast visual acuity, LGN lateral geniculate nucleus

Fig. 2

Tensor ellipsoid with the main axes. Each axis has its eigenvector (orientation of the axis) and its eigenvalue (length of the axis). The 1st eigenvector is mainly used for tractography purposes; the eigenvalues are the basis for all DTI parameters (eg. fractional anisotropy)

Fig. 3

Diffusion-weighted image, eigenvector map, and fractional anisotropy map. a Healthy control diffusion-weighted image used as raw image for further DTI post-processing. b Eigenvector map with encoding of eigenvector directions by RGB code (red = left-right orientation; green = anterior-posterior orientation; blue = inferior-superior orientation) with red arrows showing highly aligned fibers of the optic radiation and c fractional anisotropy (FA) map with high values (white) in regions of highly aligned fiber tracts and low values (dark gray) in areas of diffuse fiber orientation (yellow arrows showing highly aligned fibers of the optic radiation corresponding to red arrows in b)

Fig. 4

TBSS analysis of patients’ fiber tracts in patients with NMOSD compared to healthy controls. a TBSS mean FA skeleton of patients and healthy controls. b–d FA differences between NMOSD patients and healthy controls at p values uncorrected for multiple comparisons p < 0.05 (b) and p < 0.01 (c), and at a p < 0.05 fwe corrected for multiple comparisons (d). Red-yellow = reduction in NMOSD patients/blue-light blue = increase in NMOSD patients. For visualization purposes, significant skeleton tracts are thickened (tbss_fill script of fsl). b–d Are images reproduced from Pache F, Zimmermann H, Finke C, et al. (2016) Brain parenchymal damage in neuromyelitis optica spectrum disorder—a multimodal MRI study. Eur Radiol 26: 4413–4422. [84]

Fig. 5

Probabilistic tractography of the optic radiation. a Optic radiation fibers are calculated using probabilistic tractography (constrained spherical deconvolution [122] using MRtrix 0.2.12; Brain Research Institute, Melbourne, Australia) in a patient with neuromyelitis optica spectrum disorder (NMOSD). Optic radiation in b is visualized in axial, sagittal, and coronal plane (c) using DTI Quench (Vistalab Software, Stanford University)