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White matter hyperintensity (WMH) shape is associated with long-term dementia risk in community-dwelling older adults, however, the underlying structural correlates of this association are unknown. We therefore aimed to investigate the association between baseline WMH shape and cerebrovascular disease progression over time in community-dwelling older adults. The association of WMH shape and cerebrovascular disease markers was investigated using linear and logistic regression models in the Age, Gene/Environment Susceptibility-Reykjavik (AGES) study (n = 2297; average time to follow-up: 5.2 years). A more irregular shape of periventricular/confluent WMH at baseline was associated with a larger increase in WMH volume, and with occurrence of new subcortical infarcts, new microbleeds, new enlarged perivascular spaces, and new cerebellar infarcts at the 5.2-year follow-up (all p < 0.05). Furthermore, less elongated and more irregularly shaped deep WMHs were associated with a larger increase in WMH volume, and new cortical infarcts at follow-up (p < 0.05). A less elongated shape of deep WMH was associated with new microbleeds at follow-up (p < 0.05). Our findings show that WMH shape may be indicative of the type of cerebrovascular disease marker progression. This underlines the significance of WMH shape to aid in the assessment of cerebrovascular disease progression., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: M.J.P. van Osch reports to be an unpaid member of a clinical trial steering committee of Alnylam.

Background and Objectives: Individual brain MRI markers only show at best a modest association with long-term occurrence of dementia. Therefore, it is challenging to accurately identify individuals at increased risk for dementia. We aimed to identify different brain MRI phenotypes by hierarchical clustering analysis based on combined neurovascular and neurodegenerative brain MRI markers and to determine the long-term dementia risk within the brain MRI phenotype subgroups., Methods: Hierarchical clustering analysis based on 32 combined neurovascular and neurodegenerative brain MRI markers in community-dwelling individuals of the Age-Gene/Environment Susceptibility Reykjavik Study was applied to identify brain MRI phenotypes. A Cox proportional hazards regression model was used to determine the long-term risk for dementia per subgroup., Results: We included 3,056 participants and identified 15 subgroups with distinct brain MRI phenotypes. The phenotypes ranged from limited burden, mostly irregular white matter hyperintensity (WMH) shape and cerebral atrophy, mostly irregularly WMHs and microbleeds, mostly cortical infarcts and atrophy, mostly irregularly shaped WMH and cerebral atrophy to multiburden subgroups. Each subgroup showed different long-term risks for dementia (min-max range hazard ratios [HRs] 1.01-6.18; mean time to follow-up 9.9 ± 2.6 years); especially the brain MRI phenotype with mainly WMHs and atrophy showed a large increased risk (HR 6.18, 95% CI 3.37-11.32)., Discussion: Distinct brain MRI phenotypes can be identified in community-dwelling older adults. Our results indicate that distinct brain MRI phenotypes are related to varying long-term risks of developing dementia. Brain MRI phenotypes may in the future assist in an improved understanding of the structural correlates of dementia predisposition.

Introduction: We aimed to investigate the association between white matter hyperintensity (WMH) shape and volume and the long-term dementia risk in community-dwelling older adults., Methods: Three thousand seventy-seven participants (mean age: 75.6 ± 5.2 years) of the Age Gene/Environment Susceptibility (AGES)-Reykjavik study underwent baseline 1.5T brain magnetic resonance imaging and were followed up for dementia (mean follow-up: 9.9 ± 2.6 years)., Results: More irregular shape of periventricular/confluent WMH (lower solidity (hazard ratio (95% confidence interval) 1.34 (1.17 to 1.52), p < .001) and convexity 1.38 (1.28 to 1.49), p < .001); higher concavity index 1.43 (1.32 to 1.54), p < .001) and fractal dimension 1.45 (1.32 to 1.58), p < .001)), higher total WMH volume (1.68 (1.54 to 1.87), p < .001), higher periventricular/confluent WMH volume (1.71 (1.55 to 1.89), p < .001), and higher deep WMH volume (1.17 (1.08 to 1.27), p < .001) were associated with an increased long-term dementia risk., Discussion: WMH shape markers may in the future be useful in determining patient prognosis and may aid in patient selection for future preventive treatments in community-dwelling older adults., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

T 2 -hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006-0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Currently, little is known about the spatial distribution of white matter hyperintensities (WMH) in the brain of patients with Systemic Lupus erythematosus (SLE). Previous lesion markers, such as number and volume, ignore the strategic location of WMH. The goal of this work was to develop a fully-automated method to identify predominant patterns of WMH across WM tracts based on cluster analysis. A total of 221 SLE patients with and without neuropsychiatric symptoms from two different sites were included in this study. WMH segmentations and lesion locations were acquired automatically. Cluster analysis was performed on the WMH distribution in 20 WM tracts. Our pipeline identified five distinct clusters with predominant involvement of the forceps major, forceps minor, as well as right and left anterior thalamic radiations and the right inferior fronto-occipital fasciculus. The patterns of the affected WM tracts were consistent over the SLE subtypes and sites. Our approach revealed distinct and robust tract-based WMH patterns within SLE patients. This method could provide a basis, to link the location of WMH with clinical symptoms. Furthermore, it could be used for other diseases characterized by presence of WMH to investigate both the clinical relevance of WMH and underlying pathomechanism in the brain., (© 2022. The Author(s).)

White matter hyperintensity (WMH) shape has been associated with the severity of the underlying brain pathology, suggesting it is a potential neuroimaging marker of WMH impact on brain function. In 563 patients with vascular disease (58 ± 10 years), we examined the relationship between WMH volume, shape, and cognitive functioning. WMH volume and shape were automatically determined on 1.5T brain MRI data. Standardized linear regression analyses estimated the association between WMH volume and shape (concavity index, solidity, convexity, fractal dimension, and eccentricity) and memory and executive functioning, adjusted for age, sex, educational level, and reading ability. Larger WMH volumes were associated with lower executive functioning Z-scores (b (95%-CI): -0.09 (-0.17;-0.01)). Increased shape complexity of periventricular/confluent WMH associated with lower executive functioning (concavity index +1SD: -0.13 (-0.20;-0.06); solidity -1SD: -0.09 (-0.17;-0.02)) and lower memory function (fractal dimension +1SD: -0.10 (-0.18;-0.02)). Of note, the association between concavity index and executive functioning was independent of WMH volume (-0.12 (-0.19;-0.04)). Our results suggest that WMH shape contains additional information about WMH burden, not otherwise captured by WMH volume., (Copyright © 2022. Published by Elsevier Inc.)

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estimation for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmentations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL- and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray matter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interest., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: Advanced white matter hyperintensity (WMH) markers on brain MRI may help reveal underlying mechanisms and aid in the diagnosis of different phenotypes of SLE patients experiencing neuropsychiatric (NP) manifestations., Methods: In this prospective cohort study, we included a clinically well-defined cohort of 155 patients consisting of 38 patients with NPSLE (26 inflammatory and 12 ischaemic phenotype) and 117 non-NPSLE patients. Differences in 3 T MRI WMH markers (volume, type and shape) were compared between patients with NPSLE and non-NPSLE and between patients with inflammatory and ischaemic NPSLE by linear and logistic regression analyses corrected for age, sex and intracranial volume., Results: Compared with non-NPSLE [92% female; mean age 42 (13) years], patients with NPSLE [87% female; mean age 40 (14) years] showed a higher total WMH volume [B (95%-CI)]: 0.46 (0.0 7 ↔ 0.86); P = 0.021], a higher periventricular/confluent WMH volume [0.46 (0.0 6 ↔ 0.86); P = 0.024], a higher occurrence of periventricular with deep WMH type [0.32 (0.1 3 ↔ 0.77); P = 0.011], a higher number of deep WMH lesions [3.06 (1.2 1 ↔ 4.90); P = 0.001] and a more complex WMH shape [convexity: ‒0.07 (‒0.12 ↔ ‒0.02); P = 0.011, concavity index: 0.05 (0.0 1 ↔ 0.08); P = 0.007]. WMH shape was more complex in inflammatory NPSLE patients [89% female; mean age 39 (15) years] compared with patients with the ischaemic phenotype [83% female; mean age 41 (11) years] [concavity index: 0.08 (0.0 1 ↔ 0.15); P = 0.034]., Conclusion: We demonstrated that patients with NPSLE showed a higher periventricular/confluent WMH volume and more complex shape of WMH compared with non-NPSLE patients. This finding was particularly significant in inflammatory NPLSE patients, suggesting different or more severe underlying pathophysiological abnormalities., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

The underlying mechanisms of the association between cardiovascular risk factors and a higher white matter hyperintensity (WMH) burden are unknown. We investigated the association between cardiovascular risk factors and advanced WMH markers in 155 non-demented older adults (mean age: 71 ± 5 years). The association between cardiovascular risk factors and quantitative MRI-based WMH shape and volume markers were examined using linear regression analysis. Presence of hypertension was associated with a more irregular shape of periventricular/confluent WMH (convexity (B (95 % CI)): -0.12 (-0.22--0.03); concavity index: 0.06 (0.02-0.11)), but not with total WMH volume (0.22 (-0.15-0.59)). Presence of diabetes was associated with deep WMH volume (0.89 (0.15-1.63)). Body mass index or hyperlipidemia showed no association with WMH markers. In conclusion, different cardiovascular risk factors seem to be related to a distinct pattern of WMH shape markers in non-demented older adults. These findings may suggest that different underlying cardiovascular pathological mechanisms lead to different WMH MRI phenotypes, which may be valuable for early detection of individuals at risk for stroke and dementia., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: To compare cognitive function between patients with different phenotypes of neuropsychiatric systemic lupus erythematosus (NPSLE) and assess its association with brain and white matter hyperintensity (WMH) volumes., Methods: Patients attending the Leiden University Medical Centre NPSLE clinic between 2007 and 2015 without large brain infarcts were included (n=151; 42±13 years, 91% women). In a multidisciplinary consensus meeting, neuropsychiatric symptoms were attributed to systemic lupus erythematosus (SLE) (NPSLE, inflammatory (n=24) or ischaemic (n=12)) or to minor/non-NPSLE (n=115). Multiple regression analyses were performed to compare cognitive function between NPSLE phenotypes and to assess associations between brain and WMH volumes and cognitive function cross-sectionally., Results: Global cognitive function was impaired in 5%, learning and memory (LM) in 46%, executive function and complex attention (EFCA) in 39% and psychomotor speed (PS) in 46% of all patients. Patients with inflammatory NPSLE showed the most cognitive impairment in all domains (p≤0.05).Higher WMH volume associated with lower PS in the total group (B: -0.14 (95% CI -0.32 to -0.02)); especially in inflammatory NPSLE (B: -0.36 (95% CI -0.60 to -0.12). In the total group, lower total brain volume and grey matter volume associated with lower cognitive functioning in all domains (all: 0.00/0.01 (0.00;0.01)) and lower white matter volume associated with lower LM, EFCA and PS (all: 0.00/0.01 (0.00;0.01))., Conclusion: We demonstrated that an association between brain and WMH volumes and cognitive function is present in patients with SLE, but differs between (NP)SLE phenotypes. WMHs associated with PS especially in inflammatory NPSLE, which suggests a different, potentially more severe underlying pathophysiological mechanism of cognitive impairment in this phenotype., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To evaluate longitudinal variations in diffusion tensor imaging (DTI) metrics of different white matter (WM) tracts of newly diagnosed SLE patients, and to assess whether DTI changes relate to changes in clinical characteristics over time., Methods: A total of 17 newly diagnosed SLE patients (19-55 years) were assessed within 24 months from diagnosis with brain MRI (1.5 T Philips Achieva) at baseline, and after at least 12 months. Fractional anisotropy, mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity values were calculated in several normal-appearing WM tracts. Longitudinal variations in DTI metrics were analysed by repeated measures analysis of variance. DTI changes were separately assessed for 21 WM tracts. Associations between longitudinal alterations of DTI metrics and clinical variables (SLEDAI-2K, complement levels, glucocorticoid dosage) were evaluated using adjusted Spearman correlation analysis., Results: Mean MD and RD values from the normal-appearing WM significantly increased over time (P = 0.019 and P = 0.021, respectively). A significant increase in RD (P = 0.005) and MD (P = 0.012) was found in the left posterior limb of the internal capsule; RD significantly increased in the left retro-lenticular part of the internal capsule (P = 0.013), and fractional anisotropy significantly decreased in the left corticospinal tract (P = 0.029). No significant correlation was found between the longitudinal change in DTI metrics and the change in clinical measures., Conclusion: Increase in diffusivity, reflecting a compromised WM tissue microstructure, starts in initial phases of the SLE disease course, even in the absence of overt neuropsychiatric (NP) symptoms. These results indicate the importance of monitoring NP involvement in SLE, even shortly after diagnosis., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Objective: To determine whether white matter hyperintensity (WMH) markers on MRI are associated with long-term risk of mortality and ischemic stroke., Methods: We included consecutive patients with manifest arterial disease enrolled in the Second Manifestations of Arterial Disease-Magnetic Resonance (SMART-MR) study. We obtained WMH markers (volume, type, and shape) from brain MRI scans performed at baseline using an automated algorithm. During follow-up, occurrence of death and ischemic stroke was recorded. Using Cox regression, we investigated associations of WMH markers with risk of mortality and ischemic stroke, adjusting for demographics, cardiovascular risk factors, and cerebrovascular disease., Results: We included 999 patients (59 ± 10 years; 79% male) with a median follow-up of 12.5 years (range 0.2-16.0 years). A greater periventricular or confluent WMH volume was independently associated with a greater risk of vascular death (hazard ratio [HR] 1.29, 95% confidence interval [CI] 1.13-1.47) for a 1-unit increase in natural log-transformed WMH volume and ischemic stroke (HR 1.53, 95% CI 1.26-1.86). A confluent WMH type was independently associated with a greater risk of vascular (HR 1.89, 95% CI 1.15-3.11) and nonvascular death (HR 1.65, 95% CI 1.01-2.73) and ischemic stroke (HR 2.83, 95% CI 1.36-5.87). A more irregular shape of periventricular or confluent WMH, as expressed by an increase in concavity index, was independently associated with a greater risk of vascular (HR 1.20, 95% CI 1.05-1.38 per SD increase) and nonvascular death (HR 1.21, 95% CI 1.03-1.42) and ischemic stroke (HR 1.28, 95% CI 1.05-1.55)., Conclusions: WMH volume, type, and shape are associated with long-term risk of mortality and ischemic stroke in patients with manifest arterial disease., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

BACKGROUND AND PURPOSE: Hyperglycemia can lead to an increased rate of apoptosis of microglial cells and to damaged neurons. The relation between hyperglycemia and cerebrovascular markers on MRI is unknown. Our aim was to study the association between intraoperative hyperglycemia and cerebrovascular markers. METHODS: In this further analysis of a subgroup investigation of the BIOCOG study, 65 older non-demented patients (median 72 years) were studied who underwent elective surgery of ≥ 60 minutes. Intraoperative blood glucose maximum was determined retrospectively in each patient. In these patients, preoperatively and at 3 months follow-up a MRI scan was performed and white matter hyperintensity (WMH) volume and shape, infarcts, and perfusion parameters were determined. Multivariable logistic regression analyses were performed to determine associations between preoperative cerebrovascular markers and occurrence of intraoperative hyperglycemia. Linear regression analyses were performed to assess the relation between intraoperative hyperglycemia and pre- to postoperative changes in WMH volume. Associations between intraoperative hyperglycemia and postoperative WMH volume at 3 months follow-up were also assessed by linear regression analyses. RESULTS: Eighteen patients showed intraoperative hyperglycemia (glucose maximum ≥ 150 mg/dL). A preoperative more smooth shape of periventricular and confluent WMH was related to the occurrence of intraoperative hyperglycemia [convexity: OR 33.318 (95 % CI (1.002 - 1107.950); p = 0.050]. Other preoperative cerebrovascular markers were not related to the occurrence of intraoperative hyperglycemia. Intraoperative hyperglycemia showed no relation with pre- to postoperative changes in WMH volume nor with postoperative WMH volume at 3 months follow-up. CONCLUSIONS: We found that a preoperative more smooth shape of periventricular and confluent WMH was related to the occurrence of intraoperative hyperglycemia. These findings may suggest that a similar underlying mechanism leads to a certain pattern of vascular brain abnormalities and an increased risk of hyperglycemia., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Background: Emerging evidence shows sex differences in manifestations of vascular brain injury in memory clinic patients. We hypothesize that this is explained by sex differences in cardiovascular function., Objective: To assess the relation between sex and manifestations of vascular brain injury in patients with cognitive complaints, in interaction with cardiovascular function., Methods: 160 outpatient clinic patients (68.8±8.5 years, 38% female) with cognitive complaints and vascular brain injury from the Heart-Brain Connection study underwent a standardized work-up, including heart-brain MRI. We calculated sex differences in vascular brain injury (lacunar infarcts, non-lacunar infarcts, white matter hyperintensities [WMHs], and microbleeds) and cardiovascular function (arterial stiffness, cardiac index, left ventricular [LV] mass index, LV mass-to-volume ratio and cerebral blood flow). In separate regression models, we analyzed the interaction effect between sex and cardiovascular function markers on manifestations of vascular brain injury with interaction terms (sex*cardiovascular function marker)., Results: Males had more infarcts, whereas females tended to have larger WMH-volumes. Males had higher LV mass indexes and LV mass-to-volume ratios and lower CBF values compared to females. Yet, we found no interaction effect between sex and individual cardiovascular function markers in relation to the different manifestations of vascular brain injury (p-values interaction terms > 0.05)., Conclusion: Manifestations of vascular brain injury in patients with cognitive complaints differed by sex. There was no interaction between sex and cardiovascular function, warranting further studies to explain the observed sex differences in injury patterns.

Lacunes and white matter hyperintensities (WMHs) are features of cerebral small vessel disease (CSVD) that are associated with poor functional outcomes. However, how the two are related remains unclear. In this study, we examined the association between lacunes and several WMH features in patients with a history of vascular disease. A total of 999 patients (mean age 59 ± 10 years) with a 1.5 T brain magnetic resonance imaging (MRI) scan were included from the SMART-MR study. Lacunes were scored visually and WMH features (volume, subtype and shape) were automatically determined. Analyses consisted of linear and Poisson regression adjusted for age, sex, and total intracranial volume (ICV). Patients with lacunes (n = 188; 19%) had greater total (B = 1.03, 95% CI: 0.86 to 1.21), periventricular/confluent (B = 1.08, 95% CI: 0.89 to 1.27), and deep (B = 0.71, 95% CI: 0.44 to 0.97) natural log-transformed WMH volumes than patients without lacunes. Patients with lacunes had an increased risk of confluent type WMHs (RR = 2.41, 95% CI: 1.98 to 2.92) and deep WMHs (RR = 1.41, 95% CI: 1.22 to 1.62) and had a more irregular shape of confluent WMHs than patients without lacunes, independent of total WMH volume. In conclusion, we found that lacunes on MRI were associated with WMH features that correspond to more severe small vessel changes, mortality, and poor functional outcomes.

White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease, that is increasingly studied with large, pooled multicenter datasets. This data pooling increases statistical power, but poses challenges for automated WMH segmentation. Although there is extensive literature on the evaluation of automated WMH segmentation methods, such evaluations in a multicenter setting are lacking. We performed WMH segmentations in sixty patients scanned on six different magnetic resonance imaging (MRI) scanners (10 patients per scanner) using five freely available and fully-automated WMH segmentation methods (Cascade, kNN-TTP, Lesion-TOADS, LST-LGA and LST-LPA). Different MRI scanner vendors and field strengths were included. We compared these automated WMH segmentations with manual WMH segmentations as a reference. Performance of each method both within and across scanners was assessed using spatial and volumetric correspondence with the reference segmentations by Dice's similarity coefficient (DSC) and intra-class correlation coefficient (ICC) respectively. We found the best performance, both within and across scanners, for kNN-TTP, followed by LST-LPA and LST-LGA, with worse performance for Lesion-TOADS and Cascade. Our findings can serve as a guide for choosing a method and also highlight the importance to further improve and evaluate consistency of methods in a multicenter setting.

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.

Cerebral small vessel disease is a heterogeneous disease in which various underlying etiologies can lead to different types of white matter hyperintensities (WMH). WMH shape features might aid in distinguishing these different types. In this proof of principle study in patients with type 2 diabetes mellitus (T2DM), we present a novel approach to assess WMH using shape features. Our algorithm determines WMH volume and different WMH shape and location features on 3T MRI scans. These features were compared between patients with T2DM (n = 60) and a matched control group (n = 54). Although a more traditional marker (WMH volume) was not significantly different between groups (natural log transformed Beta (95% CI): 0.07 (-0.11↔0.24)), patients with T2DM showed a larger number of non-punctuate WMH (median (10 th -90 th percentile), patients: 40 lesions per person (16-86); controls: 26 (5-58)) and a different shape (eccentricity) of punctuate deep WMH (Beta (95% CI): 0.40 (0.23↔0.58)) compared to controls. In conclusion, our algorithm identified WMH features that are not part of traditional WMH assessment, but showed to be distinguishing features between patients with T2DM and controls. Future studies could address these features to further unravel the etiology and functional impact of WMH.

Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts. The method uses a multi-scale convolutional neural network with a T 1 -weighted image, a T 2 -weighted fluid attenuated inversion recovery (FLAIR) image and a T 1 -weighted inversion recovery (IR) image as input. The method automatically segments white matter (WM), cortical grey matter (cGM), basal ganglia and thalami (BGT), cerebellum (CB), brain stem (BS), lateral ventricular cerebrospinal fluid (lvCSF), peripheral cerebrospinal fluid (pCSF), and WMH. Our method was evaluated quantitatively with images publicly available from the MRBrainS13 challenge ( n  = 20), quantitatively and qualitatively in relatively healthy older subjects ( n  = 96), and qualitatively in patients from a memory clinic ( n  = 110). The method can accurately segment WMH (Overall Dice coefficient in the MRBrainS13 data of 0.67) without compromising performance for tissue segmentations (Overall Dice coefficients in the MRBrainS13 data of 0.87 for WM, 0.85 for cGM, 0.82 for BGT, 0.93 for CB, 0.92 for BS, 0.93 for lvCSF, 0.76 for pCSF). Furthermore, the automatic WMH volumes showed a high correlation with manual WMH volumes (Spearman's ρ  = 0.83 for relatively healthy older subjects). In both cohorts, our method produced reliable segmentations (as determined by a human observer) in most images (relatively healthy/memory clinic: tissues 88%/77% reliable, WMH 85%/84% reliable) despite various degrees of brain abnormalities and motion artefacts. In conclusion, this study shows that a convolutional neural network-based segmentation method can accurately segment brain tissues and WMH in MR images of older patients with varying degrees of brain abnormalities and motion artefacts.