Background and Purpose: In cerebral small vessel disease, cerebral blood flow and autoregulation are impaired and therefore excessive blood pressure reduction could possibly accelerate white matter damage and worsen outcome. The trial determined, in severe symptomatic cerebral small vessel disease, whether intensive blood pressure lowering resulted in progression of white matter damage assessed using diffusion tensor imaging. Methods: Randomized, parallel, multicenter controlled, blinded-outcomes clinical trial. One hundred eleven participants with magnetic resonance imaging confirmed symptomatic lacunar infarct and confluent white matter hyperintensities and were recruited and randomized to standard (systolic=130–140 mmHg) (N=56) or intensive (systolic<125 mmHg) (N=55) blood pressure targets. The primary end point was change in diffusion tensor imaging white matter mean diffusivity peak height between baseline and 24 months. Secondary end points were other magnetic resonance imaging markers and cognition. Results: Patients were mean 68 years and 60% male. Mean (SD) blood pressure reduced by −15.3 (15.4) and −23.1 (22.04) mm Hg in the standard/intensive groups, respectively ( P <0.001). There was no difference between treatment groups for the primary end point: standard, adjusted mean (SE)=12.5×10 −3 (0.2×10 −3 ); intensive, 12.5×10 −3 (0.2×10 −3 ), P =0.92. In the whole population over 24 months follow-up, there was a significant deterioration in white matter microstructure but no detectable decrease in cognition. Conclusions: Intensive blood pressure lowering in severe cerebral small vessel disease was not associated with progression of white matter damage on diffusion tensor imaging or magnetic resonance imaging. In a multicentre study setting over 2 years, multimodal diffusion tensor imaging-magnetic resonance imaging was more sensitive to detecting change than cognitive testing. REGISTRATION: URL: https://www.isrctn.com ; Unique identifier: ISRCTN37694103.

Introduction: There are few randomized clinical trials in vascular cognitive impairment (VCI). This trial tested the hypothesis that the PDE5 inhibitor tadalafil, a widely used vasodilator, increases cerebral blood flow (CBF) in older people with symptomatic small vessel disease, the main cause of VCI. Methods:In a double-blind, placebo-controlled, cross-over trial, participants received tadalafil (20 mg) and placebo on two visits ≥7 days apart (randomized to order of treatment). The primary endpoint, change in subcortical CBF, was measured by arterial spin labelling.Results: Tadalafil increased CBF non-significantly in all subcortical areas (N = 55, age: 66.8 (8.6) years) with greatest treatment effect within white matter hyperintensities (+9.8%, P = .0960). There were incidental treatment effects on systolic and diastolic blood pressure (-7.8, -4.9 mmHg; P < .001). No serious adverse events were observed.

Quasi-diffusion imaging (QDI) is a novel quantitative diffusion magnetic resonance imaging (dMRI) technique that enables high quality tissue microstructural imaging in a clinically feasible acquisition time. QDI is derived from a special case of the continuous time random walk (CTRW) model of diffusion dynamics and assumes water diffusion is locally Gaussian within tissue microstructure. By assuming a Gaussian scaling relationship between temporal () and spatial () fractional exponents, the dMRI signal attenuation is expressed according to a diffusion coefficient, (in mm2 s−1), and a fractional exponent, . Here we investigate the mathematical properties of the QDI signal and its interpretation within the quasi-diffusion model. Firstly, the QDI equation is derived and its power law behaviour described. Secondly, we derive a probability distribution of underlying Fickian diffusion coefficients via the inverse Laplace transform. We then describe the functional form of the quasi-diffusion propagator, and apply this to dMRI of the human brain to perform mean apparent propagator imaging. QDI is currently unique in tissue microstructural imaging as it provides a simple form for the inverse Laplace transform and diffusion propagator directly from its representation of the dMRI signal. This study shows the potential of QDI as a promising new model-based dMRI technique with significant scope for further development.

Alzheimer’s disease (AD) has a long pre-clinical period, and so there is a crucial need for early detection, including of Mild Cognitive Impairment (MCI). Computational analysis of connected speech using Natural Language Processing and machine learning has been found to indicate disease and could be utilized as a rapid, scalable test for early diagnosis. However, there has been a focus on the Cookie Theft picture description task, which has been criticized. Fifty participants were recruited – 25 healthy controls (HC), 25 mild AD or MCI (AD+MCI) – and these completed five connected speech tasks: picture description, a conversational map reading task, recall of an overlearned narrative, procedural recall and narration of a wordless picture book. A high-dimensional set of linguistic features were automatically extracted from each transcript and used to train Support Vector Machines to classify groups. Performance varied, with accuracy for HC vs. AD+MCI classification ranging from 62% using picture book narration to 78% using overlearned narrative features. This study shows that, importantly, the conditions of the speech task have an impact on the discourse produced, which influences accuracy in detection of AD beyond the length of the sample. Further, we report the features important for classification using different tasks, showing that a focus on the Cookie Theft picture description task may narrow the understanding of how early AD pathology impacts speech.

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