Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change

Abstract

Cerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period.98 patients (aged 43–89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ) describing the patients' brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials.Changes in brain structure described by DSEG θ were related to change in EF and IPS (p < 0.001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models (p = 0.002). Change in DSEG θ was also related to change in all other MRI markers (p < 0.017), suggesting it may be used as a surrogate marker of SVD damage across the cerebrum. Sample size estimates indicated that fewer patients would be required to detect treatment effects using DSEG θ compared to conventional MRI and DTI markers of SVD severity.DSEG θ is a powerful tool for characterising subtle brain change in SVD that has a negative impact on cognition and remains a significant predictor of cognitive change when other MRI markers of brain change are accounted for. DSEG provides an automatic segmentation of the whole cerebrum that is sensitive to a range of SVD related structural changes and successfully predicts cognitive change. Power analysis shows DSEG θ has potential as a monitoring tool in clinical trials. As such it may provide a marker of SVD severity from a single imaging modality (i.e. DTIs).

Predicting Dementia in Cerebral Small Vessel Disease Using an Automatic Diffusion Tensor Image Segmentation Technique

“…22,23 We have shown previously that it is possible to summarize information from DSEG into a single score (DSEG- θ ) that describes the microstructure of the whole cerebrum. 24 Furthermore, we found that change in DSEG- θ was related to change in conventional imaging markers of SVD, including WMH load, GM atrophy, lacunar infarcts, and cerebral microbleeds, in addition to DTI histogram parameters describing WM microstructure. 24 As such, DSEG- θ is an automated technique that may provide a suitable biomarker of SVD severity based on a single imaging parameter (DTI), rather than relying on information from several different imaging modalities that often require manual segmentation.…”

“…12 Due to some participants not having full coverage of the cerebellum, it was removed from all scans using an automated technique. 24…”

“…28 These measures may be visualized in a 2-dimensional Cartesian plane, 23 the ( p,q ) space, in which it is possible to identify diffusion properties of GM, WM tissue, and cerebrospinal fluid, as well as pathologically affected tissue. 22–24…”

Statins and Brain Health: Alzheimer’s Disease and Cerebrovascular Disease Biomarkers in Older Adults

“…DTI sequences were processed as described previously [36] and were analyzed for measures of white matter microstructural degeneration as another indicator of cerebrovascular disease [37, 38]. We focused on FA of the corpus callosum, as we recently found that this measure was one of the most sensitive markers for ascertaining structural brain changes related to systemic vascular health [39].…”

[P2–411]: Predicting Dementia in Cerebral Small‐vessel Disease Using an Automatic Diffusion Tensor Image Segmentation Technique

“…28 These measures may be visualized in a 2-dimensional Cartesian plane, 23 the (p,q) space, in which it is possible to identify diffusion properties of GM, WM tissue, and cerebrospinal fluid, as well as pathologically affected tissue. [22][23][24] DSEG is a fully automated DTI segmentation algorithm that separates (p,q) space into 16 discrete segments using a k-medians cluster analysis based on the magnitudes of the isotropic (p) and anisotropic (q) diffusion metrics for each voxel, given in mm2s -1 . 22 Each segment describes a unique diffusion profile representing tissue microstructural properties of each voxel assigned to that segment.…”