Multi-shell Diffusion MRI Models for White Matter Characterization in Cerebral Small Vessel Disease

Abstract

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.

Diffusion models reveal white matter microstructural changes with ageing, pathology and cognition

“…Diffusion metrics are suggested to be most strongly associated with processing speed. 71 , 93 Therefore, we also tested these hypotheses in the supplemental material and found that both DTI and NODDI strongly predicted processing speed (Trail B and combined). As expected, commissural fibres had the greatest effect size.…”

Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease

“…For prospective multicentre studies, it is crucial to minimize differences in acquisition parameters such as b-values, but for retrospective studies with already acquired data, harmonization of scans with largely different b-values needs further investigation. As clinical protocols continuously improve and more complex sequences are implemented (e.g., multi-shell data), future work should also investigate whether RISH harmonization is suitable for such advanced dMRI applications in SVD ( De Luca et al, 2018 , Konieczny et al, 2020 , Rydhög et al, 2017 ). Moreover, since we focused our analyses on the dMRI metrics most commonly associated with SVD (FA, MD, PSMD), further analyses are required in order to generalize our conclusions to other metrics obtained from higher level analysis such as fiber tractography ( De Luca et al, 2020 ) and network theory in SVD ( Reijmer et al, 2015 ).…”

“…All these factors may affect the measured diffusion signal intensity and metrics derived from the data. In prospective multicentre studies, this variability can be controlled using standardized acquisitions and scanners from the same manufacturer ( Konieczny et al, 2020 ). However, when retrospectively combining data form different cohorts, differences in acquisition can be substantial.…”

Impact of white-mater mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease

“…related to axonal degeneration and ischemic demyelination), they lack speci city regarding the concrete nature of the underlying histological changes. More sophisticated microscopic diffusion models have been developed to better inform the true nature of these changes and may be explored in future studies of SVD patients [9,20]. In this regard, our results represent the rst step towards a consensus analysis pipeline to generate comparable DTI histogram metrics capable of predicting cognitive impairment in SVD patients; this pipeline could be potentially applicable also to other clinical conditions such as multiple sclerosis [17].…”

“…Conversely, Lawrence et al demonstrated that the MD peak height obtained over the NAWM voxels was strongly associated with processing speed impairment [16]. Nonetheless, in recent work, the MD peak width has been utilized as a reference measure to validate the predictive power of novel biomarkers due to its wellestablished value in detecting microscopic tissue changes in a broad range of pathological WM conditions [10,[17][18][19][20]; as a result, retrospective validation in existing DWI datasets may help to establish a consistent processing pipeline for future longitudinal studies Our study aimed to assess the impact of using different masks for computing DTI histogram-based metrics on their sensitivity as SVD biomarkers. For this purpose: 1) we extracted different DTI histogrambased metrics using two distinct masks: NAWM versus TBSS; 2) we evaluated their sensitivity to discriminate between patients and healthy controls; and 3) we performed a correlation analysis between a selection of DTI metrics that have been most frequently considered in SVD studies (i.e, FA median, FA peak height, MD peak height, and MD peak width) and the cognitive domains more typically impaired in SVD patients (i.e.…”