Background The purpose of this study was to validate a technique for measuring mean calcium density and to determine associations of cardiovascular risk factors with carotid calcium density. Methods and Results We performed a cross-sectional study in a random sample of 100 stroke-free participants from the population-based Rotterdam Study. The mean calcium density of the combined left and right carotid bifurcations was quantified with a threshold of 130 Hounsfield Units (HU) using a novel density technique. To validate the methodology, carotid calcium volumes acquired using the technique in the current study were compared with measurements computed using dedicated clinical software (semiautomatic technique based on a threshold of ≥130 HU). Next, we investigated the associations of participant demographics, total calcium volume, and known cardiovascular risk factors (hypertension, diabetes, hypercholesterolemia, obesity, and smoking status) with the newly derived mean carotid calcium density measurement using linear regression analyses. Calcium volumes obtained with the 2 methods showed a high agreement (intraclass correlation coefficient=0.99, P<0.001), underlining the validity of the density technique. The total calcium volume was statistically significantly associated with the mean calcium density (cardiovascular risk factors adjusted model (B: 0.48 [95% CI, 0.30-0.66], P<0.001). We also found an association between hypercholesterolemia and mean calcium density (0.46 [0.09-0.83], P=0.017). No other significant associations were found between participant demographics or cardiovascular risk factors and mean carotid calcium density. Conclusions We demonstrated the feasibility of a carotid calcium density measurement technique. The data warrant a subsequent longitudinal study to determine the association between carotid calcium density and the risk of cerebrovascular events.

Background and aims: Atherosclerotic plaque onset and progression are known to be affected by local biomechanical factors. While the role of wall shear stress (WSS) has been studied, the impact of another biomechanical factor, namely mechanical wall stress (MWS), remains poorly understood. In this study, we investigated the association of MWS, independently and combined with WSS, towards atherosclerosis in coronary arteries. Methods: Thirty-four human coronary arteries were analyzed using near-infrared spectroscopy intravascular ultrasound (NIRS-IVUS) and optical coherence tomography (OCT) at baseline and after 12 months. Baseline WSS and MWS were calculated using computational models, and wall thickness (ΔWT) and lipid-rich necrotic core size (ΔLRNC) change were measured in non-calcified coronary segments. The arteries were further divided into 1.5 mm/45° sectors and categorized as plaque-free or plaque sectors. For each category, associations between biomechanical factors (WSS & MWS) and changes in coronary wall (ΔWT & ΔLRNC) were studied using linear mixed models. Results: In plaque-free sectors, higher MWS (p < 0.001) was associated with greater vessel wall growth. Plaque sectors demonstrated wall thickness reduction over time, likely due to medical therapy, where higher levels of WSS and WMS, individually and combined, (p < 0.05) were associated with a greater reduction. Sectors with low MWS combined with high WSS demonstrated the highest LRNC increase (p < 0.01). Conclusions: In this study, we investigated the association of the (largely-overlooked) biomechanical factor MWS with coronary atherosclerosis, individually and combined with WSS. Our results demonstrated that both MWS and WSS significantly correlate with atherosclerotic plaque initiation and development.

Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data –such as cognitive tests, imaging, and genetic data– and the types of output they provide. We will focus on specific use cases for diagnosis, i.e., estimating the current “condition” of the patient, such as early detection and diagnosis of dementia, differential diagnosis of brain tumors, and decision making in stroke. Regarding prediction, i.e., estimation of the future “condition” of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer. Furthermore, based on these use cases, we will assess the current state-of-the-art methodology and highlight current efforts on benchmarking of these methods and the importance of open science therein. Finally, we assess the current clinical impact of computer-aided methods and discuss the required next steps to increase clinical impact.

In order to prevent long-term immunity-related complications after lung transplantation, close monitoring of immunosuppressant levels using therapeutic drug monitoring (TDM) is paramount. Novel electronic nose (eNose) technology may be a non-invasive alternative to the current invasive procedures for TDM. We investigated the diagnostic and categorization capacity of eNose breathprints for Tacrolimus trough blood plasma levels (TAC _trough) in lung transplant recipients (LTRs). We performed eNose measurements in stable LTR attending the outpatient clinic. We evaluated (1) the correlation between eNose measurements and TAC _trough, (2) the diagnostic capacity of eNose technology for TAC _trough, and (3) the accuracy of eNose technology for categorization of TAC _trough into three clinically relevant categories (low: <7 µg ml ⁻¹, medium: 7-10 µg ml ⁻¹, and high: >10 µg ml ⁻¹ ). A total of 186 measurements from 86 LTR were included. There was a weak but statistically significant correlation (r = 0.21, p = 0.004) between the eNose measurements and TAC _trough. The root mean squared error of prediction for the diagnostic capacity was 3.186 in the training and 3.131 in the validation set. The accuracy of categorization ranged between 45%-63% for the training set and 52%-69% in the validation set. There is a weak correlation between eNose breathprints and TAC _trough in LTR. However, the diagnostic as well as categorization capacity for TAC _trough using eNose breathprints is too inaccurate to be applicable in TDM.

Background: Accumulating evidence highlights the existence of distinct morphological subtypes of intracranial carotid arteriosclerosis. So far, little is known on the prevalence of these subtypes and subsequent stroke risk in the general population. We determined the prevalence of morphological subtypes of intracranial arteriosclerosis and assessed the risk of stroke associated with these subtypes. Methods: Between 2003 and 2006, 2391 stroke-free participants (mean age 69.6, 51.7% women) from the population-based Rotterdam Study underwent noncontrast computed tomography to visualize calcification in the intracranial carotid arteries as a proxy for intracranial arteriosclerosis. Calcification morphology was evaluated according to a validated grading scale and categorized into intimal, internal elastic lamina (IEL), or mixed subtype. Follow-up for stroke was complete until January 1, 2016. We used multivariable Cox regression to assess associations of each subtype with incident stroke. Results: The prevalence of calcification was 82% of which 39% had the intimal subtype, 48% IEL subtype, and 13% a mixed subtype. During a median follow-up of 10.4 years, 155 participants had a stroke. All 3 subtypes were associated with a higher risk of stroke (adjusted hazard ratio [95% CI] for intimal: 2.11 [1.07-4.13], IEL: 2.66 [1.39-5.11], and mixed subtype 2.57 [1.18-5.61]). The association of the IEL subtype with stroke was strongest among older participants. The association of the intimal subtype with stroke was noticeably stronger in women than in men. Conclusions: Calcification of the IEL was the most prevalent subtype of intracranial arteriosclerosis. All 3 subtypes were associated with an increased risk of stroke, with noticeable age and sex-specific differences.

Objective: Atherosclerotic plaque rupture in carotid arteries is a major source of cerebrovascular events. Calcifications are highly prevalent in carotid plaques, but their role in plaque rupture remains poorly understood. This work studied the morphometric features of calcifications in carotid plaques and their effect on the stress distribution in the fibrous plaque tissue at the calcification interface, as a potential source of plaque rupture and clinical events. Methods: A comprehensive morphometric analysis of 65 histology cross-sections from 16 carotid plaques was performed to identify the morphology (size and shape) and location of plaque calcifications, and the fibrous-tissue fiber organization around them. Calcification-specific finite element models were constructed to examine the fibrous plaque tissue stresses at the calcification interface. Statistical correlation analysis was performed to elucidate the impact of calcification morphology and fibrous tissue organization on interface stresses. Results: Hundred-seventy-one calcifications were identified on the histology cross-sections, which showed great variation in morphology. Four distinct patterns of fiber organization in the plaque tissue were observed around the calcification. They were termed as attached, pushed-aside, encircling and random patterns. The stress analyses showed that calcifications are correlated with high interface stresses, which might be comparable to or even above the plaque strength. The stress levels depended on the calcification morphology and fiber organization. Thicker calcification with a circumferential slender shape, located close to the lumen were correlated most prominently to high interface stresses. Conclusion: Depending on its morphology and the fiber organization around it, a calcification in an atherosclerotic plaque can act as a stress riser and cause high interface stresses. Significance: This study demonstrated the potential of calcifications in atherosclerotic plaques to cause elevated stresses in plaque tissue and provided a biomechanical explanation for the histopathological findings of calcification-associated plaque rupture.

Structural brain markers are studied extensively in the field of neurodegeneration, but are thought to occur rather late in the process. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain structure and function are also affected by ‘normal’ brain ageing. More information is needed on how functional connectivity relates to aging, particularly in the absence of overt neurodegenerative disease. We investigated the association of age with resting-state functional connectivity in 2878 non-demented persons between 50 and 95 years of age (54.1% women) from the population-based Rotterdam Study. We obtained nine well-known resting state networks using data-driven methodology. Within the anterior default mode network, ventral attention network, and sensorimotor network, functional connectivity was significantly lower with older age. In contrast, functional connectivity was higher with older age within the visual network. Between resting state networks, we found patterns of both increases and decreases in connectivity in approximate equal proportions. Our results reinforce the notion that the aging brain undergoes a reorganization process, and serves as a solid basis for exploring functional connectivity as a preclinical marker of neurodegenerative disease.

Early structural changes to the heart, including the chambers and the coronary arteries, provide important information on pre-clinical heart disease like cardiac failure. Currently, contrast-enhanced cardiac computed tomography angiography (CCTA) is the preferred modality for the visualization of the cardiac chambers and the coronaries. In clinical practice not every patient undergoes a CCTA scan; many patients receive only a non-contrast-enhanced calcium scoring CT scan (CTCS), which has less radiation dose and does not require the administration of contrast agent. Quantifying cardiac structures in such images is challenging, as they lack the contrast present in CCTA scans. Such quantification would however be relevant, as it enables population based studies with only a CTCS scan. The purpose of this work is therefore to investigate the feasibility of automatic segmentation and quantification of cardiac structures viz whole heart, left atrium, left ventricle, right atrium, right ventricle and aortic root from CTCS scans. A fully automatic multi- atlas-based segmentation approach is used to segment the cardiac structures. Results show that the segmentation overlap between the automatic method and that of the reference standard have a Dice similarity coefficient of 0.91 on average for the cardiac chambers. The mean surface-to-surface distance error over all the cardiac structures is 1.4±1.7 mm. The automatically obtained cardiac chamber volumes using the CTCS scans have an excellent correlation when compared to the volumes in corresponding CCTA scans, a Pearson correlation coefficient (R) of 0.95 is obtained. Our fully automatic method enables large-scale assessment of cardiac structures on non-contrastenhanced CT scans.

Background and Purpose - The presence of subclinical vascular brain disease, including white matter lesions and lacunar infarcts, substantially increases the risk of clinical stroke. White matter microstructural integrity is considered an earlier, potentially better, marker of the total burden of vascular brain disease. Its association with risk of stroke, a focal event, remains unknown. Methods - From the population-based Rotterdam Study, 4259 stroke-free participants (mean age: 63.6 years, 55.6% women) underwent brain magnetic resonance imaging, including diffusion magnetic resonance imaging, between 2006 and 2011. All participants were followed up for incident stroke until 2013. Cox proportional hazards models were used to associate markers of the microstructure of normal-appearing white matter with risk of stroke, adjusting for age, sex, white matter lesion volume, lacunar infarcts, and additionally for cardiovascular risk factors. Finally, we assessed the predictive value of white matter microstructural integrity for stroke beyond the Framingham Stroke Risk Profile. Results - During 18 476 person-years of follow-up, 58 people experienced a stroke. Both lower fractional anisotropy and higher MD increased risk of stroke, independent of age, sex, cardiovascular risk factors, white matter lesion volume, and lacunar infarcts (hazard ratio per SD increase in: fractional anisotropy: 0.75 [95% confidence interval, 0.57-0.98] and MD: 1.50 [95% confidence interval, 1.08-2.09]). MD improved stroke prediction beyond the Framingham Stroke Risk Profile (continuous net reclassification improvement: 0.52 [95% confidence interval, 0.24-0.81]). Conclusions - Future stroke is predicted not only by prevalent vascular lesions but also by subtle alterations in the microstructure of normal-appearing white matter. Inclusion of this effect in risk prediction models produces a significant advantage in stroke prediction compared with the existing Framingham Stroke Risk Profile.

Background: Coffee is one of the most widely consumed beverages worldwide and has been of considerable interest in research on cognition and dementia. Objective: To investigate the effect of coffee on preclinical brain MRI markers of dementia and cognitive performance. Methods: In 2,914 participants from the population-based Rotterdam Study (mean age: 59.3±7.2 years, 55 females), we assessed coffee consumption, performed brain MRI, and assessed cognition at baseline. To study cognitive change, cognitive assessment was repeated after 5 years of follow-up. Coffee consumption was analyzed continuously (per cup increase) and in categories (0-1,>1-3,>3 cups/day). Using logistic and linear regression, associations of coffee consumption with lacunar infarcts and brain tissue volumes on MRI, and cognitive performance (cross-sectional and longitudinal) were investigated, adjusting for relevant confounders. Results: We found that higher coffee consumption was associated with a lower prevalence of lacunar infarcts [odds ratio per cup increase: 0.88 (95 CI:0.79;0.98)], and smaller hippocampal volume [difference: -0.01 (95 CI:-0.02;0.00)]. Also, we found that the highest category of coffee consumption was associated with better performance on the Letter Digit Substitution Task [difference: 1.13(95 CI:0.39;1.88)], Word Fluency test [0.74(95 CI:0.04,1.45)], Stroop interference task [1.82(95 CI:0.23;3.41)], and worse performance on the 15-Word Learning test delayed recall [-0.38(95 CI:-0.74;-0.02)]. These associations were not found when cognition was analyzed longitudinally. Conclusion: We found complex associations between coffee consumption, brain structure, and cognition. Higher coffee consumption was cross-sectionally associated with a lower occurrence of lacunar infarcts and better executive function, but also with smaller hippocampal volume and worse memory function.