Background: Clinical diagnosis and monitoring of Parkinson's disease (PD) remain challenging because of the lack of an established biomarker. Neuromelanin‐magnetic resonance imaging (NM‐MRI) is an emerging biomarker of nigral depigmentation indexing the loss of melanized neurons but has unknown prospective diagnostic and tracking performance in multicenter settings.Objectives: The aim was to investigate the diagnostic accuracy of NM‐MRI in early PD in a multiprotocol setting and to determine and compare serial NM‐MRI changes in PD and controls.Methods: In this longitudinal case–control 3 T MRI study, 148 patients and 97 controls were included from six UK clinical centers, of whom 140 underwent a second scan after 1.5 to 3 years. An automated template‐based analysis was applied for subregional substantia nigra NM‐MRI contrast and volume assessment. A point estimate of the period of prediagnostic depigmentation was computed.Results: All NM metrics performed well to discriminate patients from controls, with receiver operating characteristic showing 85% accuracy for ventral NM contrast and 83% for volume. Generalizability using a priori volume cutoff was good (79% accuracy). Serial MRI demonstrated accelerated NM loss in patients compared to controls. Ventral NM contrast loss was point estimated to start 5 to 6 years before clinical diagnosis. Ventral nigral depigmentation was greater in the most affected side, more severe cases, and nigral NM volume change correlated with change in motor severity.Conclusions: We demonstrate that NM‐MRI provides clinically useful diagnostic information in early PD across protocols, platforms, and sites. It provides methods and estimated depigmentation rates that highlight the potential to detect preclinical PD and track progression for biomarker‐enabled clinical trials. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Purpose: To measure accurately the upper cervical cord cross‐sectional area (CSA) in patients with relapsing remitting multiple sclerosis (RRMS), and normal control subjects, to address the paradox that longitudinal reduction in CSA has been detected in RRMS while reduction compared to controls has not. We hypothesized that a lack of statistical power and/or measurement sensitivity due to partial volume averaging in previous studies contributed to this paradox.Materials and Methods: Using a technique that corrects for partial volume averaging, we measured the CSA in 35 normal controls and 35 RRMS patients. We used the total intracranial volume (TICV) to normalize the CSA and therefore reduce the normal variance and improve the statistical power.Results: The mean TICV did not differ between groups. Statistical power analysis indicated that a 5% reduction in CSA in the patients could be detected with an estimated power of 0.74 before normalization and 0.9 after. The mean CSA in the patients was not reduced compared to controls after (P = 0.928) or before (P = 0.881) normalization.Conclusion: Using a sensitive analysis method, and apparently appropriate statistical power, we did not detect reduced CSA in RRMS patients. We hypothesize that this may be due to inflammation. J. Magn. Reson. Imaging 2007;26:61–65.

Meta-analysis of summary results from published neuroimaging studies independently testing a common hypothesis is performed using coordinate based meta-analysis (CBMA), which tests for consistent activation (in the case of functional MRI studies) of the same anatomical regions. Using just the reported coordinates it is also possible to meta-analyse coactivated regions to reveal a network-like structure of coordinate clusters (network nodes) distributed at the coactivated locations and a measure of the coactivation strength (network edges), which is determined by the presence/absence of reported activation. Here a new coordinate-based method to estimate a network of coactivations is detailed, which utilises the Z score accompanying each reported. Coordinate based meta-analysis of networks (CBMAN) assumes that if the activation pattern reported by independent studies is truly consistent, then the relative magnitude of these Z scores might also be consistent. It is hypothesised that this is detectable as Z score covariance between coactivated regions provided the within study variances are small. Advantages of using the Z scores instead of coordinates to measure coactivation strength are that censoring by the significance thresholds can be considered, and that using a continuous measure rather than a dichotomous one can increase statistical power. CBMAN uses maximum likelihood estimation to fit multivariate normal distributions to the standardised Z scores, and the covariances are considered as edges of a network of coactivated clusters (nodes). Here it is validated by numerical simulation and demonstrated on real data used previously to demonstrate CBMA. Software to perform CBMAN is freely available.

Type 1 and type 2 diabetes mellitus have an impact on the microstructural environment and cognitive functions of the brain due to its microvascular/macrovascular complications. Conventional Magnetic Resonance Imaging (MRI) techniques can allow detection of brain volume reduction in people with diabetes. However, conventional MRI is insufficiently sensitive to quantify microstructural changes. Diffusion Tensor Imaging (DTI) has been used as a sensitive MRI-based technique for quantifying and assessing brain microstructural abnormalities in patients with diabetes. This systematic review aims to summarise the original research literature using DTI to quantify microstructural alterations in diabetes and the relation of such changes to cognitive status and metabolic profile. A total of thirty-eight published studies that demonstrate the impact of diabetes mellitus on brain microstructure using DTI are included, and these demonstrate that both type 1 diabetes mellitus and type 2 diabetes mellitus may affect cognitive abilities due to the alterations in brain microstructures.

Purpose To resolve and regularize orientation estimates for two crossing fibers from images acquired with conventional diffusion tensor imaging (DTI) sampling schemes. Materials and Methods Partial volume causes artifacts in DTI. Given that routine use of high angular resolution diffusion imaging (HARDI) is still tentative, a regularized two-tensor model to resolve fiber crossings from conventional DTI datasets is presented. To overcome the problems of fitting multiple tensors, a model that exploits the planar diffusion profile in regions with fiber crossings is utilized. A regularization scheme is applied to reduce noise artifacts, which can be significant due to the relatively low number of acquired images. A set of basis directions is used to convert the two tensor model to many models of lower dimensionality. Relaxation labeling is utilized to select from amongst these models those that preserve continuity of orientations across neighbors. Revised fractional anisotropy (FA) and mean diffusivity (MD) values are computed. Results Spatial regularization improves the orientation estimates of the two-tensor model in simulations and in human data and estimates agree well with a priori anatomical knowledge. Conclusion Orientational, anisotropy, and diffusivity information can be resolved in regions of two fiber crossings using full brain coverage scans acquired in less than six minutes. J. Magn. Reson. Imaging 2008;28:199–209.