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BACKGROUND AND OBJECTIVES: Diffusion tensor imaging (DTI) networks integrate damage from a variety of pathological processes in cerebral small vessel disease (SVD) and may be a sensitive marker to detect treatment effects. We determined whether brain network analysis could detect treatment effects in the PRESERVE trial dataset, in which intensive versus standard blood pressure (BP) lowering was compared. The primary endpoint of DTI had not shown treatment differences. METHODS: Subjects with lacunar stroke were randomised to standard (systolic 130-140 mmHg), or intensive (systolic

Contains fulltext : 170472.pdf (Publisher’s version ) (Open Access) Cerebral small vessel disease (SVD) is an important cause of vascular cognitive impairment. Recent studies have demonstrated that structural connectivity of brain networks in SVD is disrupted. However, little is known about the extent and location of the reduced connectivity in SVD. Here they investigate the rich club organisation-a set of highly connected and interconnected regions-and investigate whether there is preferential rich club disruption in SVD. Diffusion tensor imaging (DTI) and cognitive assessment were performed in a discovery sample of SVD patients (n = 115) and healthy control subjects (n = 50). Results were replicated in an independent dataset (49 SVD with confluent WMH cases and 108 SVD controls) with SVD patients having a similar SVD phenotype to that of the discovery cases. Rich club organisation was examined in structural networks derived from DTI followed by deterministic tractography. Structural networks in SVD patients were less dense with lower network strength and efficiency. Reduced connectivity was found in SVD, which was preferentially located in the connectivity between the rich club nodes rather than in the feeder and peripheral connections, a finding confirmed in both datasets. In discovery dataset, lower rich club connectivity was associated with lower scores on psychomotor speed (beta = 0.29, P < 0.001) and executive functions (beta = 0.20, P = 0.009). These results suggest that SVD is characterized by abnormal connectivity between rich club hubs in SVD and provide evidence that abnormal rich club organisation might contribute to the development of cognitive impairment in SVD. Hum Brain Mapp 38:1751-1766, 2017. (c) 2017 Wiley Periodicals, Inc.

Purpose To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. Materials and methods We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T2-weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. Results Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p 2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1H MRS characteristics. Conclusions 1H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours., Highlights • Non-Gaussian multimodal MRI characteristics of high and low grade glioma tissue. • Bayesian inference of multimodal MRI derives whole brain tumour tissue-type maps. • Automated segmentation of normal and tumour tissue volumes. • Visualisation of glioma heterogeneity, infiltration, necrosis and vasogenic oedema.

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.\ud 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.\ud Changes in brain structure described by DSEG θ were related to change in EF and IPS (p