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Various diffusion MRI (dMRI) measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different dMRI measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. Indeed, it quickly becomes intractable for existing analysis pipelines to process multiple measurements at each voxel and at each vertex forming a streamline, highlighting the need for new ways to visualise or analyse such high-dimensional data. In a sample of 36 typically developing children aged 8–18 years, we profiled various commonly used dMRI measures across 22 brain pathways. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in these dMRI measures. The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We then demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. In summary, our findings demonstrate that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.

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Treatment with VEL015 at doses up to 30 mg per day for 24 weeks was safe and well-tolerated in patients with AD. Diffusion MR measures appear to be the most sensitive biomarkers to assess disease progression over 24 weeks.

Using a novel longitudinal fixel-based analysis framework, we demonstrate that white matter fibre density and fibre cross-section increased within a 16-month scan rescan period in specific regions. The observed increases might reflect increasing axonal diameter or axon count. Pubertal stage or progression did not influence the rate of fibre development in the early stages of puberty. Future work should focus on quantifying these measures across a wider age range to capture the full spectrum of fibre development across the pubertal period.

Insufficient supply of selenium to antioxidant enzymes in the brain may contribute to Alzheimer's disease (AD) pathophysiology; therefore, oral supplementation may potentially slow neurodegeneration. We examined selenium and selenoproteins in serum and cerebrospinal fluid (CSF) from a dual-dose 24-week randomized controlled trial of sodium selenate in AD patients, to assess tolerability, and efficacy of selenate in modulating selenium concentration in the central nervous system (CNS). A pilot study of 40 AD cases was randomized to placebo, nutritional (0.32 mg sodium selenate, 3 times daily), or supranutritional (10 mg, 3 times daily) groups. We measured total selenium, selenoproteins, and inorganic selenium levels, in serum and CSF, and compared against cognitive outcomes. Supranutritional selenium supplementation was well tolerated and yielded a significant (p < 0.001) but variable (95% CI = 13.4-24.8 μg/L) increase in CSF selenium, distributed across selenoproteins and inorganic species. Reclassifying subjects as either responsive or non-responsive based on elevation in CSF selenium concentrations revealed that responsive group did not deteriorate in Mini-Mental Status Examination (MMSE) as non-responsive group (p = 0.03). Pooled analysis of all samples revealed that CSF selenium could predict change in MMSE performance (Spearman's rho = 0.403; p = 0.023). High-dose sodium selenate supplementation is well tolerated and can modulate CNS selenium concentration, although individual variation in selenium metabolism must be considered to optimize potential benefits in AD. The Vel002 study is listed on the Australian and New Zealand Clinical Trials Registry ( http://www.anzctr.org.au /), ID: ACTRN12611001200976.

Recent advances in diffusion magnetic resonance imaging (dMRI) analysis techniques have improved our understanding of fibre‐specific variations in white matter microstructure. Increasingly, studies are adopting multi‐shell dMRI acquisitions to improve the robustness of dMRI‐based inferences. However, the impact of b‐value choice on the estimation of dMRI measures such as apparent fibre density (AFD) derived from spherical deconvolution is not known. Here, we investigate the impact of b‐value sampling scheme on estimates of AFD. First, we performed simulations to assess the correspondence between AFD and simulated intra‐axonal signal fraction across multiple b‐value sampling schemes. We then studied the impact of sampling scheme on the relationship between AFD and age in a developmental population (n = 78) aged 8–18 (mean = 12.4, SD = 2.9 years) using hierarchical clustering and whole brain fixel‐based analyses. Multi‐shell dMRI data were collected at 3.0T using ultra‐strong gradients (300 mT/m), using 6 diffusion‐weighted shells ranging from b = 0 to 6,000 s/mm2. Simulations revealed that the correspondence between estimated AFD and simulated intra‐axonal signal fraction was improved with high b‐value shells due to increased suppression of the extra‐axonal signal. These results were supported by in vivo data, as sensitivity to developmental age‐relationships was improved with increasing b‐value (b = 6,000 s/mm2, median R2 = .34; b = 4,000 s/mm2, median R2 = .29; b = 2,400 s/mm2, median R2 = .21; b = 1,200 s/mm2, median R2 = .17) in a tract‐specific fashion. Overall, estimates of AFD and age‐related microstructural development were better characterised at high diffusion‐weightings due to improved correspondence with intra‐axonal properties.