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Fatigue and depression are common, debilitating, and intertwined symptoms in people with relapsing-remitting multiple sclerosis (pwRRMS). To elucidate the relationship between depression/fatigue and brain connectivity in pwRRMS we conducted a systematic review. Searched databases were PubMed, Web-of-Science and Scopus. Inclusion criteria were: participants with RRMS; published between 2001-2021; fatigue and depression assessments validated for MS; brain structural, functional (fMRI) or diffusion MRI (dMRI). 16 dMRI (13 fatigue, 4 depression) and 18 fMRI (16 fatigue, 4 depression) studies were included. ~50% of studies reported no correlation between MRI measures and fatigue/depression. Positive findings showed that abnormal cortico-limbic structural and functional connectivity was associated with depression, and fatigue was linked to connectivity measures in cortico-thalamic-basal-ganglial networks. Additionally, both depression and fatigue were related to altered cingulum structural connectivity, and functional connectivity involving thalamus, cerebellum, frontal lobe, and pre-/postcentral gyri. These findings suggest neuropathological effects in these regions may underlie fatigue and depression in pwRRMS, but the overall results were inconclusive. Further studies using optimised imaging protocols and validated depression and fatigue measures are required to clarify the substrate underlying these symptoms in pwRRMS.

Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO’s performance does not deteriorate as PVS volume increases. Second, the performance of all “vesselness” filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.

Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods is still unclear. Here, we propose an open-source computational model generating three-dimensional digital reference objects to evaluate enhancement performance in relation to PVS characteristics and spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). With it, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms all other filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO’s performance does not deteriorate as PVS volume increases. Second, the performance of all “vesselness” enhancement filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under the precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.

Objective: To determine the magnitude of potentially causal relationships among vascular risk factors (VRFs), large-artery atheromatous disease (LAD), and cerebral white matter hyperintensities (WMH) in 2 prospective cohorts. Methods: We assessed VRFs (history and measured variables), LAD (in carotid, coronary, and leg arteries), and WMH (on structural MRI, visual scores and volume) in: (a) community-dwelling older subjects of the Lothian Birth Cohort 1936, and (b) patients with recent nondisabling stroke. We analyzed correlations, developed structural equation models, and performed mediation analysis to test interrelationships among VRFs, LAD, and WMH. Results: In subjects of the Lothian Birth Cohort 1936 (n = 881, mean age 72.5 years [SD ±0.7 years], 49% with hypertension, 33% with moderate/severe WMH), VRFs explained 70% of the LAD variance but only 1.4% to 2% of WMH variance, of which hypertension explained the most. In stroke patients (n = 257, mean age 74 years [SD ±11.6 years], 61% hypertensive, 43% moderate/severe WMH), VRFs explained only 0.1% of WMH variance. There was no direct association between LAD and WMH in either sample. The results were the same for all WMH measures used. Conclusions: The small effect of VRFs and LAD on WMH suggests that WMH have a large “nonvascular,” nonatheromatous etiology. VRF modification, although important, may be limited in preventing WMH and their stroke and dementia consequences. Investigation of, and interventions against, other suspected small-vessel disease mechanisms should be addressed.

Background: White matter lesions (WML) are associated with hypertension. Blood pressure (BP) is transmitted to the brain via the carotid arteries, and autoregulation helps protect the brain. We investigated if BP via carotid waveform parameters were associated with WML. Methods: We obtained BP measurements from a cohort of community dwelling subjects at mean ages 70±1 and 73±1 years; brain MRI and carotid artery ultrasound at mean age 73±1 years. We calculated mean values of BP (systolic, diastolic, mean, variability and pulsatility), measured WML by volume, and Fazekas scale. We calculated internal carotid artery (ICA) mean blood flow velocity, pulsatility index (PI), and resistivity index (RI). We tested associations between BP and ICA flow parameters and WML using multiple linear regression, corrected for intracranial volume (ICV), age, gender, BMI, previous MI, diabetes, hypertension, smoking, hypercholesterolaemia, PVD and stroke. Results: Amongst 694 subjects, diastolic and mean BP decreased and hence BP pulsatility increased significantly between ages 70 and 73 years. Lower diastolic BP and higher BP pulsatility were associated with higher ICA PI (standardized β, age 70= -0.24, age 73= -0.19, both p Conclusions: The relatively weak association between BP and WML may be mediated via ICA pulsatility which is largely driven by falling diastolic BP. This questions the nature of the apparent link between BP and WML.

Quantitative tractography may provide insights into regional heterogeneity of changes in white matter structure in normal ageing. Here we examine how brain atrophy and white matter lesions affect correlations between tract shape, tract integrity and age in a range of frontal and non-frontal tracts in 90 non-demented subjects aged over 65 years using an enhanced version of probabilistic neighbourhood tractography. This novel method for automatic single seed point placement employs unsupervised learning and streamline selection to provide reliable and accurate tract segmentation, whilst also indicating how the shape of an individual tract compares to that of a predefined reference tract. There were significant negative correlations between tract shape similarity to reference tracts derived from a young brain white matter atlas and age in genu and splenium of corpus callosum. Controlling for intracranial and lateral ventricle volume, the latter of which increased significantly with age, attenuated these correlations by 40 and 84 % respectively, indicating that this age-related change in callosal tract topology is significantly mediated by global atrophy and ventricular enlargement. In accordance with the 'frontal ageing' hypothesis, there was a significant positive correlation between mean diffusivity () and age, and a significant negative correlation between fractional anisotropy (FA) and age in corpus callosum genu; correlations not seen in splenium. Significant positive correlations were also observed between and age in bilateral cingulum cingulate gyri, uncinate fasciculi and right corticospinal tract. This pattern of correlations was not, however, reproduced when those subjects with significant white matter lesion load were analyzed separately from those without. These data therefore suggest that brain atrophy and white matter lesions play a significant role in driving regional patterns of age-related changes in white matter tract shape and integrity.