ObjectiveTo test the hypothesis that multi-shell diffusion models improve the characterization of microstructural alterations in cerebral small vessel disease (SVD), we assessed associations with processing speed performance, longitudinal change and reproducibility of diffusion metrics.MethodsWe included 50 sporadic and 59 genetically defined SVD patients (CADASIL) with cognitive testing and standardized 3T MRI, including multi-shell diffusion imaging. We applied the simple diffusion tensor imaging (DTI) model and 2 advanced models: diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI). Linear regression and multivariable random forest regression (including conventional SVD markers) were used to determine associations between diffusion metrics and processing speed performance. The detection of short-term disease progression was assessed by linear mixed models in 49 sporadic SVD patients with longitudinal high-frequency imaging (in total 459 MRIs). Inter-site reproducibility was determined in 10 CADASIL patients scanned back-to-back on 2 different 3T MRI scanners.ResultsMetrics from DKI showed the strongest associations with processing speed performance (R2 up to 21%) and the largest added benefit on top of conventional SVD imaging markers in sporadic SVD and CADASIL patients with lower SVD burden. Several metrics from DTI and DKI performed similarly in detecting disease progression. Reproducibility was excellent (intraclass correlation coefficient >0.93) for DTI and DKI metrics. NODDI metrics were less reproducible.ConclusionMulti-shell diffusion imaging and DKI improve the detection and characterization of cognitively relevant microstructural white matter alterations in SVD. Excellent reproducibility of diffusion metrics endorses their use as SVD markers in research and clinical care. Our publicly available inter-site dataset facilitates future studies.Classification of evidenceThis study provides Class I evidence that in patients with SVD, diffusion MRI metrics are associated with processing speed performance.

Retinal assessments have been discussed as biomarkers for brain atrophy. However, available studies did not investigate all retinal layers due to older technology, reported inconsistent results, or were based on small sample sizes. We included 2872 eligible participants of the Rhineland Study with data on spectral domain–optical coherence tomography (SD–OCT) and brain magnetic resonance imaging (MRI). We used multiple linear regression to examine relationships between retinal measurements and volumetric brain measures as well as fractional anisotropy (FA) as measure of microstructural integrity of white matter (WM) for different brain regions. Mean (SD) age was 53.8 ± 13.2 years (range 30–94) and 57% were women. Volumes of the inner retina were associated with total brain and grey matter (GM) volume, and even stronger with WM volume and FA. In contrast, the outer retina was mainly associated with GM volume, while both, inner and outer retina, were associated with hippocampus volume. While we extend previously reported associations between the inner retina and brain measures, we found additional associations of the outer retina with parts of the brain. This indicates that easily accessible retinal SD-OCT assessments may serve as biomarkers for clinical monitoring of neurodegenerative diseases and merit further research.

Purpose In diffusion MRI, dropout refers to a strong attenuation of the measured signal that is caused by bulk motion during the diffusion encoding. When left uncorrected, dropout will be erroneously interpreted as high diffusivity in the affected direction. We present a method to automatically detect dropout, and to replace the affected measurements with imputed values. Methods Signal dropout is detected by deriving an outlier score from a simple harmonic oscillator‐based reconstruction and estimation (SHORE) fit of all measurements. The outlier score is defined to detect measurements that are substantially lower than predicted by SHORE in a relative sense, while being less sensitive to measurement noise in cases of weak baseline signal. A second SHORE fit is based on detected inliers only, and its predictions are used to replace outliers. Results Our method is shown to reliably detect and accurately impute dropout in simulated data, and to achieve plausible results in corrupted in vivo dMRI measurements. Computational effort is much lower than with previously proposed alternatives. Conclusions Deriving a suitable outlier score from SHORE results in a fast and accurate method for detection and imputation of dropout in diffusion MRI. It requires measurements with multiple b values (such as multi‐shell or DSI), but is independent from the models used for analysis (such as DKI, NODDI, deconvolution, etc.).

Visual impairment and retinal neurodegeneration are intrinsically connected and both have been associated with cognitive impairment and brain atrophy, but the underlying mechanisms remain unclear. To investigate whether transneuronal degeneration is implicated, we systematically assessed the relation between visual function and retinal, visual pathway, hippocampal and brain degeneration. We analyzed baseline data from 3316 eligible Rhineland Study participants with visual acuity (VA), optical coherence tomography (OCT), and magnetic resonance imaging (MRI) data available. Regional volumes, cortical volume, and fractional anisotropy (FA) were derived from T1‐weighted and diffusion‐weighted 3 T MRI scans. Statistical analyses were performed using multivariable linear regression and structural equation modeling. VA and ganglion cell layer (GCL) thinning were both associated with global brain atrophy (SD effect size [95% CI] −0.090 [−0.118 to −0.062] and 0.066 [0.053–0.080], respectively), and hippocampal atrophy (−0.029 [−0.055 to −0.003] and 0.114 [0.087–0.141], respectively). The effect of VA on whole brain and hippocampal volume was partly mediated by retinal neurodegeneration. Similarly, the effect of retinal neurodegeneration on brain and hippocampal atrophy was mediated through intermediate visual tracts, accounting for 5.2%–23.9% of the effect. Visual impairment and retinal neurodegeneration were robustly associated with worse brain atrophy, FA, and hippocampal atrophy, partly mediated through disintegration of intermediate visual tracts. Our findings support the use of OCT‐derived retinal measures as markers of neurodegeneration, and indicate that both general and transneuronal neurodegeneration along the visual pathway, partly reflecting visual impairment, account for the association between retinal neurodegeneration and brain atrophy.