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Objective This work investigates network organisation of brain structural connectivity in systemic lupus erythematosus (SLE) relative to healthy controls and its putative association with lesion distribution and disease indicators. Methods White matter hyperintensity (WMH) segmentation and connectomics were performed in 47 patients with SLE and 47 healthy age-matched controls from structural and diffusion MRI data. Network nodes were divided into hierarchical tiers based on numbers of connections. Results were compared between patients and controls to assess for differences in brain network organisation. Voxel-based analyses of the spatial distribution of WMH in relation to network measures and SLE disease indicators were conducted. Results Despite inter-individual differences in brain network organization observed across the study sample, the connectome networks of SLE patients had larger proportion of connections in the peripheral nodes. SLE patients had statistically larger numbers of links in their networks with generally larger fractional anisotropy weights (i.e. a measure of white matter integrity) and less tendency to aggregate than those of healthy controls. The voxels exhibiting connectomic differences were coincident with WMH clusters, particularly the left hemisphere’s intersection between the anterior limb of the internal and external capsules. Moreover, these voxels also associated more strongly with disease indicators. Conclusion Our results indicate network differences reflective of compensatory reorganization of the neural circuits, reflecting adaptive or extended neuroplasticity in SLE.

Raised signal in cerebrospinal fluid (CSF) on fluid-attenuated inversion recovery (FLAIR) may indicate raised CSF protein or debris and is seen in inferior frontal sulci on routine MRI. To explore its clinical relevance, we assessed the association of inferior frontal sulcal hyperintensities (IFSH) on FLAIR with demographics, risk factors, and small vessel disease markers in three cohorts (healthy volunteers, n=44; mild stroke patients, n=105; older community-dwelling participants from Lothian birth cohort 1936, n=101). We collected detailed clinical data, scanned all subjects on the same 3T MRI scanner and 3-dimensional FLAIR sequence and developed a scale to rate IFSH. In adjusted analyses, the IFSH score increased with age (per 10-year increase; OR 1.69; 95% CI, 1.42-2.02), and perivascular spaces score in centrum semiovale in stroke patients (OR 1.73; 95% CI, 1.13-2.69). Since glymphatic CSF clearance declines with age and drains partially via the cribriform plate to the nasal lymphatics, IFSH on 3T MRI may be a non-invasive biomarker of altered CSF clearance and justifies further research in larger, more diverse samples.

Purpose: Dynamic contrast-enhanced (DCE) -MRI with Patlak model analysis is increasingly used to quantify low-level blood-brain barrier (BBB) leakage in studies of pathophysiology. We aimed to investigate systematic errors due to physiological, experimental, and modeling factors influencing quantification of the permeability-surface area product PS and blood plasma volume v p, and to propose modifications to reduce the errors so that subtle differences in BBB permeability can be accurately measured. Methods: Simulations were performed to predict the effects of potential sources of systematic error on conventional PS and v p quantification: restricted BBB water exchange, reduced cerebral blood flow, arterial input function (AIF) delay and (Formula presented.) error. The impact of targeted modifications to the acquisition and processing were evaluated, including: assumption of fast versus no BBB water exchange, bolus versus slow injection of contrast agent, exclusion of early data from model fitting and (Formula presented.) correction. The optimal protocol was applied in a cohort of recent mild ischaemic stroke patients. Results: Simulation results demonstrated substantial systematic errors due to the factors investigated (absolute PS error ≤ 4.48 × 10 −4 min −1). However, these were reduced (≤0.56 × 10 −4 min −1) by applying modifications to the acquisition and processing pipeline. Processing modifications also had substantial effects on in-vivo normal-appearing white matter PS estimation (absolute change ≤ 0.45 × 10 −4 min −1). Conclusion: Measuring subtle BBB leakage with DCE-MRI presents unique challenges and is affected by several confounds that should be considered when acquiring or interpreting such data. The evaluated modifications should improve accuracy in studies of neurodegenerative diseases involving subtle BBB breakdown.

Objective To investigate brain structural connectivity in relation to cognitive abilities and systemic damage in systemic lupus erythematosus (SLE). Methods Structural and diffusion MRI data were acquired from 47 patients with SLE. Brains were segmented into 85 cortical and subcortical regions and combined with whole brain tractography to generate structural connectomes using graph theory. Global cognitive abilities were assessed using a composite variable g, derived from the first principal component of three common clinical screening tests of neurological function. SLE damage ( LD) was measured using a composite of a validated SLE damage score and disease duration. Relationships between network connectivity metrics, cognitive ability and systemic damage were investigated. Hub nodes were identified. Multiple linear regression, adjusting for covariates, was employed to model the outcomes g and LD as a function of network metrics. Results The network measures of density (standardised ß = 0.266, p = 0.025) and strength (standardised ß = 0.317, p = 0.022) were independently related to cognitive abilities. Strength (standardised ß = –0.330, p = 0.048), mean shortest path length (standardised ß = 0.401, p = 0.020), global efficiency (standardised ß = –0.355, p = 0.041) and clustering coefficient (standardised ß = –0.378, p = 0.030) were independently related to systemic damage. Network metrics were not related to current disease activity. Conclusion Better cognitive abilities and more SLE damage are related to brain topological network properties in this sample of SLE patients, even those without neuropsychiatric involvement and after correcting for important covariates. These data show that connectomics might be useful for understanding and monitoring cognitive function and white matter damage in SLE.

Objective To examine the cross-sectional associations between dietary patterns and cognitive and neuroimaging indices of brain health concurrently in the same sample of healthy older adults. Methods Dietary patterns were derived from a 130-item food frequency questionnaire for 511 individuals in the Lothian Birth Cohort 1936 (mean age 79.3 ± 0.6 years). Composite scores for global cognitive function, visuospatial ability, processing speed, memory, and verbal ability were assessed. Brain volumes and white matter microstructure were assessed in participants (n = 358) who also underwent structural magnetic resonance imaging. Results A Mediterranean-style dietary pattern and a processed dietary pattern were identified using principal component analysis of food frequency questionnaire items. In fully-adjusted linear regression models, adherence to the Mediterranean-style pattern was associated with better verbal ability (β = 0.121, P = 0.002). Associations with global cognitive function (β = 0.094, P = 0.043), visuospatial ability (β = 0.113, P = 0.019), and memory (β = 0.105, P = 0.029) did not survive correction for multiple comparisons. Associations between the processed pattern and lower cognitive scores were attenuated by around 50% following adjustment for prior (childhood) cognitive ability; only an association with verbal ability remained (β = −0.130, P = 0.001). Neither dietary pattern was associated with brain volumes or white matter microstructure. Specific Mediterranean diet features—green leafy vegetables and a low intake of red meat—were associated with better cognitive functioning. Conclusions These observational findings suggest that adherence to a Mediterranean-style diet is associated with better cognitive functioning, but not better brain structural integrity, in older adults.

Background Cerebral small vessel disease is a major cause of dementia and stroke, visible on brain magnetic resonance imaging. Recent data suggest that small vessel disease lesions may be dynamic, damage extends into normal-appearing brain and microvascular dysfunctions include abnormal blood–brain barrier leakage, vasoreactivity and pulsatility, but much remains unknown regarding underlying pathophysiology, symptoms, clinical features and risk factors of small vessel disease. Patients and Methods: The Mild Stroke Study 3 is a prospective observational cohort study to identify risk factors for and clinical implications of small vessel disease progression and regression among up to 300 adults with non-disabling stroke. We perform detailed serial clinical, cognitive, lifestyle, physiological, retinal and brain magnetic resonance imaging assessments over one year; we assess cerebrovascular reactivity, blood flow, pulsatility and blood–brain barrier leakage on magnetic resonance imaging at baseline; we follow up to four years by post and phone. The study is registered ISRCTN 12113543. Summary Factors which influence direction and rate of change of small vessel disease lesions are poorly understood. We investigate the role of small vessel dysfunction using advanced serial neuroimaging in a deeply phenotyped cohort to increase understanding of the natural history of small vessel disease, identify those at highest risk of early disease progression or regression and uncover novel targets for small vessel disease prevention and therapy.

International audience; Recombinant interferon alpha is used to treat several immunological, oncological, and infectious diseases,1 and various serious side-effects have been associated with its therapeutic use. We want to bring attention to the development of neuromyelitis optica spectrum disorder (NMOSD) [...]

The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have confounded attempts to model it constructively. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three null models-random graphs, random geometric graphs, and edge-randomised connectomes. This presents important new insights into the structure of the human brain, indicating a rich variety of connectivity patterns within hierarchically equivalent nodes. That random models fail to show such behaviour suggests that the generative mechanisms of brain structure may even insist on such dissimilarity. This also provides the strongest evidence to date in support of the hierarchical complexity paradigm of complex brain networks- both ordered and random systems are inherently more predictable. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the third and second tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (tier 3) involves regions believed to bridge high-order cognitive (tier 1) and low-order sensorimotor processing (tier 2), revealing a strikingly large diversity of connectivity patterns elicited in the integration of these processes.

We present a complete study of limited one-time sampling irregularity map (LOTS-IM), a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), including its application and evaluation for quantitative assessment of white matter hyperintensities (WMH) of presumed vascular origin and assessing multiple sclerosis (MS) lesion progression. LOTS-IM is unique compared to similar other methods because it yields irregularity map (IM) which represents WMH as irregularity values, not probability values, and retains the original MRI’s texture information. We tested and compared the usage of IM for WMH segmentation on T2-FLAIR MRI with various methods, including the well established unsupervised WMH segmentation Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), conventional supervised machine learning schemes andstate-of-the-artsupervised deep neural networks. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep neural networks algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). The high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

Objective The objective of this study was to investigate fatigue and cognitive impairments in systemic lupus erythematous (SLE) in relation to diffuse white matter microstructural brain damage. Methods Diffusion tensor MRI, used to generate biomarkers of brain white matter microstructural integrity, was obtained in patients with SLE and age-matched controls. Fatigue and cognitive function were assessed and related to SLE activity, clinical data and plasma biomarkers of inflammation and endothelial dysfunction. Results Fifty-one patients with SLE (mean age 48.8 ± 14.3 years) were included. Mean diffusivity (MD) was significantly higher in all white matter fibre tracts in SLE patients versus age-matched healthy controls ( p Conclusion Patients with SLE have more microstructural brain white matter damage for age than the general population, but this does not explain increased fatigue or lower cognition in SLE. The association between raised IL-6 and worse current cognitive function in SLE should be explored in larger datasets.

Highlights • We assessed Perivascular Spaces (PVS) computationally in the centrum semiovale. • We measured total PVS volume and count, and individual PVS length, width, size. • Computational PVS measures correlated positively with PVS ratings. • PVS were associated with hypertension stroke and white matter hyperintensities., Background and Purpose Perivascular Spaces (PVS), also known as Virchow-Robin spaces, seen on structural brain MRI, are important fluid drainage conduits and are associated with small vessel disease (SVD). Computational quantification of visible PVS may enable efficient analyses in large datasets and increase sensitivity to detect associations with brain disorders. We assessed the associations of computationally-derived PVS parameters with vascular factors and white matter hyperintensities (WMH), a marker of SVD. Participants Community dwelling individuals (n = 700) from the Lothian Birth Cohort 1936 who had multimodal brain MRI at age 72.6 years (SD = 0.7). Methods We assessed PVS computationally in the centrum semiovale and deep corona radiata on T2-weighted images. The computationally calculated measures were the total PVS volume and count per subject, and the mean individual PVS length, width and size, per subject. We assessed WMH by volume and visual Fazekas scores. We compared PVS visual rating to PVS computational metrics, and tested associations between each PVS measure and vascular risk factors (hypertension, diabetes, cholesterol), vascular history (cardiovascular disease and stroke), and WMH burden, using generalized linear models, which we compared using coefficients, confidence intervals and model fit. Results In 533 subjects, the computational PVS measures correlated positively with visual PVS ratings (PVS count r = 0.59; PVS volume r = 0.61; PVS mean length r = 0.55; PVS mean width r = 0.52; PVS mean size r = 0.47). PVS size and width were associated with hypertension (OR 1.22, 95% CI [1.03 to 1.46] and 1.20, 95% CI [1.01 to 1.43], respectively), and stroke (OR 1.34, 95% CI [1.08 to 1.65] and 1.36, 95% CI [1.08 to 1.71], respectively). We found no association between other PVS measures and diabetes, hypercholesterolemia or cardiovascular disease history. Computational PVS volume, length, width and size were more strongly associated with WMH (PVS mean size versus WMH Fazekas score β = 0.66, 95% CI [0.59 to 0.74] and versus WMH volume β = 0.43, 95% CI [0.38 to 0.48]) than computational PVS count (WMH Fazekas score β = 0.21, 95% CI [0.11 to 0.3]; WMH volume β = 0.14, 95% CI [0.09 to 0.19]) or visual score. Individual PVS size showed the strongest association with WMH. Conclusions Computational measures reflecting individual PVS size, length and width were more strongly associated with WMH, stroke and hypertension than computational count or visual PVS score. Multidimensional computational PVS metrics may increase sensitivity to detect associations of PVS with risk exposures, brain lesions and neurological disease, provide greater anatomic detail and accelerate understanding of disorders of brain fluid and waste clearance., Graphical abstract Image, graphical abstract

We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperintensities (WMH) of presumed vascular origin, and multiple sclerosis (MS) lesions and their progression. LOTS-IM generates an irregularity map (IM) that represents all voxels as irregularity values with respect to the ones considered "normal". Unlike probability values, IM represents both regular and irregular regions in the brain based on the original MRI's texture information. We evaluated and compared the use of IM for WMH and MS lesions segmentation on T2-FLAIR MRI with the state-of-the-art unsupervised lesions' segmentation method, Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), with several well established conventional supervised machine learning schemes and with state-of-the-art supervised deep learning methods for WMH segmentation. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA on WMH segmentation, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep learning algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). LOTS-IM also performed well on MS lesions segmentation, performing similar to LST-LGA. On the other hand, the high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

Background and Purpose— Some rheumatic diseases are associated with stroke. Less is known about associations with stroke subtypes or stroke risk by age. We quantified the association between stroke, its subtypes, and rheumatic diseases and identified when stroke risk is greatest. Methods— Searches of EMBASE (from 1980) and MEDLINE (from inception) to end 2014 and manual search of reference lists for studies of stroke and stroke subtypes in rheumatic diseases as well as studies measuring cerebrovascular disease from magnetic resonance imaging. Results— Prior published meta-analyses and new pooled analyses of any stroke in rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, gout, and psoriasis show an excess risk of stroke over the general population with odds ratio (OR) ranging from 1.51 (95% confidence interval: 1.39–1.62) to 2.13 (1.53–2.98). New meta-analyses of stroke subtypes in rheumatoid arthritis [ischemic: OR, 1.64 (1.32–2.05); hemorrhagic: OR, 1.68 (1.11–2.53)] and systemic lupus erythematosus [ischemic: OR, 2.11 (1.66–2.67); hemorrhagic: OR, 1.82 (1.07–3.09)] show an excess risk of stroke over the general population. Stroke risk across rheumatic diseases is highest in those aged 65 years: OR, 1.14 (0.94–1.38); difference P Conclusions— Risk of any stroke is higher in most rheumatic diseases than in the general population, particularly

Supplemental Digital Content is available in the text., Background and Purpose— Perivascular spaces (PVS) around venules may help drain interstitial fluid from the brain. We examined relationships between suspected venules and PVS visible on brain magnetic resonance imaging. Methods— We developed a visual venular quantification method to examine the spatial relationship between venules and PVS. We recruited patients with lacunar stroke or minor nondisabling ischemic stroke and performed brain magnetic resonance imaging and retinal imaging. We quantified venules on gradient echo or susceptibility-weighted imaging and PVS on T2-weighted magnetic resonance imaging in the centrum semiovale and then determined overlap between venules and PVS. We assessed associations between venular count and patient demographic characteristics, vascular risk factors, small vessel disease features, retinal vessels, and venous sinus pulsatility. Results— Among 67 patients (69% men, 69.0±9.8 years), only 4.6% (range, 0%–18%) of venules overlapped with PVS. Total venular count increased with total centrum semiovale PVS count in 55 patients after accounting for venule-PVS overlap (β=0.468 [95% CI, 0.187–0.750]) and transverse sinus pulsatility (β=0.547 [95% CI, 0.309–0.786]) and adjusting for age, sex, and systolic blood pressure. Conclusions— Despite increases in both visible PVS and suspected venules, we found minimal spatial overlap between them in patients with sporadic small vessel disease, suggesting that most magnetic resonance imaging-visible centrum semiovale PVS are periarteriolar rather than perivenular.