Rationale and Objectives: While calcification is a highly prevalent component in atherosclerotic extracranial carotid arteries and is known to impact plaque stability, the link between carotid calcification and ischemic events is yet to be identified. We aimed to investigate the associations of geometric features of carotid calcifications, and their temporal changes, with ischemic events. Materials and Methods: We retrospectively analyzed 128 mildly stenotic carotid arteries (Plaque At Risk study) from 64 patients with recent ischemic event, using multi-detector computed tomography angiography data at baseline and after 2 years. The 3D artery and calcification geometries were reconstructed with a semi-automatic pipeline, and an in-depth calcification morphometric assessment was performed. We examined the distribution of the calcification morphometrics and their temporal changes and investigated their associations with ischemic events at the time of inclusion, using generalized linear mixed models. Results: At baseline, compared to contralateral asymptomatic arteries, symptomatic carotids had more calcification bodies (mean [95%CI]: 1.9 [1.4–2.6] vs. 1.6 [1.2–2.2]). These calcifications were smaller (mean area [95%CI]: 3.7 mm² [2.9–5.1] vs. 4.5 mm² [3.5–5.8]) and narrower (mean width [95%CI]: 2.7 mm [2.3–3.4] vs. 3.1 mm [2.5–3.6]). At 2-year follow-up, adjusting for baseline measurements, these calcifications were smaller (mean width [95%CI]: 2.9 mm [2.5–3.5] vs. 3.3 mm [2.7–3.7]) and longer (mean [95%CI]: 8.6 mm [7.1–10.5] vs. 7.5 mm [6.3–9.5]) compared to asymptomatic side. Conclusion: Symptomatic carotid arteries presented more and smaller calcifications with a tendency to grow more in the longitudinal artery direction, providing insights into the role of carotid calcifications in ischemic events.

Introduction: Ischemic stroke incidence varies significantly with respect to sex and cardiovascular risk factors (CVRFs), a relationship that it is not well understood. Calcification in carotid atherosclerosis is known to impact plaque stability, potentially linked to ischemic stroke. The objective was to assess the in-depth calcification morphometrics within extracranial carotid atherosclerosis, their temporal changes, and associations with sex and CVRFs. Methods: Carotid arteries (n = 144) with confirmed atherosclerosis and mild-to-moderate stenosis from 72 symptomatic patients (Plaque-At-Risk study) with recent ischemic event due to ischemia in the territory of a carotid artery were imaged using multidetector computed tomography angiography (MDCTA) at baseline and after 2 years. The lumen, vessel wall, and calcifications were segmented semiautomatically, and the carotid geometries were 3D reconstructed. A comprehensive morphometric assessment of carotid calcifications was performed on the baseline and followup scans. We investigated distributions of these metrics and their associations with sex and CVRFs using generalized linear mixed models. Results: Our findings suggest that women have larger (4.5 mm2 [95% CI: 3.2-6.2] vs. 3.2 mm2 [95% CI: 2.4-4.2]) calcifications, located closer to the lumen (0.6 mm [95% CI: 0.4-0.8] vs. 0.9 mm [95% CI: 0.7-1.2]) in contrast to men at baseline and follow-up, adjusted for baseline measurements. At the baseline, nonsmokers had larger (5.3 mm2 [95% CI: 3.7-7.5] vs. 3.2 mm2 [95% CI: 2.3-4.4]) and longer (5.7 mm [95% CI: 4.1-7.3] vs. 2.4 mm [95% CI: 1.6-3.6]) calcifications than the current smokers. Diabetic patients had thicker (1.1 mm [95% CI: 0.8-1.3] vs. 0.8 mm [95% CI: 0.7-0.9]) carotid calcifications at baseline. Conclusion: Our in-depth analyses exposed several geometric features of carotid calcifications associated with sex and CVRFs and provided further insight into the pathophysiology of carotid atherosclerosis.

Background and objective: Although the association of wall shear stress (WSS) with coronary artery disease has been well studied, that of mechanical wall stress (MWS) is mainly overlooked. In this study, we performed in-silico artery-specific modeling to investigate the involvement of both MWS and WSS in coronary artery disease. Methods: Fifteen coronary arteries from five adult familial hypercholesterolemic pigs were imaged by coronary computed tomography angiography, intravascular ultrasound, and optical coherence tomography at three time points (3, 9, and 12 months). Local WSS and MWS in 3 mm/45° sectors were determined using artery-specific computational models. The relationship of WSS and MWS with wall thickness change (ΔWT) over time was statistically analyzed using Generalized Linear Mixed models. Results: A positive ΔWT was measured in all sectors, where plaque sectors presented a greater ΔWT rate compared to plaque-free sectors. In plaque-free sectors, low WSS was associated with a higher ΔWT rate (p < 0.001). In plaque sectors, high MWS was associated with a higher ΔWT rate (p < 0.05), where ΔWT rate was, although slightly, even greater in the plaque sectors with lipid-rich necrotic core (p < 0.05). Conclusions: Our results from in-silico coronary-specific models suggest that WSS and MWS may play a dominant role at different stages of coronary artery disease. WSS may be more critical in the early stages of plaque formation while MWS might have greater significance in the progression of existing plaques.

Background and aims: Local biomechanical factors are known to influence atherosclerosis in extracranial carotid arteries. While the role of some flow-driven biomechanical factors has been investigated, the influence of pressure-driven mechanical wall stress (MWS) has received limited attention. In this study, the association of the pressure-driven and flow-driven biomechanical factors with carotid atherosclerosis was examined. Methods: Carotid arteries (n = 150) with mild-to-moderate stenosis from 75 symptomatic patients (Plaque-At-Risk study) were imaged using multi-detector computed tomography angiography (MDCTA) at the time of inclusion and after 2 years. Structural changes in carotid wall and calcifications were quantified from MDCTA data while the local baseline biomechanical factors in the carotids were determined using fluid-structure interaction (FSI) computational models. The associations of the local pressure-driven and flow-driven biomechanical factors with the carotid wall and calcification changes were studied using Generalized Linear Mixed models. Results: Over two years, plaque sectors, with calcified and non-calcified sectors combined, exhibited minimal change in wall thickness, likely due to medical treatment. High MWS was associated (p < 0.001) with a reduction in plaque thickness. In calcified plaque sectors, high MWS and low oscillatory shear index (OSI) were associated (p < 0.001) with greater calcification thickness increase. The distance between the lumen and calcification decreased over time, especially in the sectors exposed to high time-averaged wall shear stress (TAWSS) and high MWS. Conclusions: Our results suggest that the pressure-driven local MWS and flow-driven OSI and TAWSS significantly correlate with the development of calcified and non-calcified plaques in carotid arteries. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01208025.

Background: The accurate automated extraction of brain vessel centerlines from Computed tomographic angiography (CTA) images plays an important role in diagnosing and treating cerebrovascular diseases such as stroke. Despite its significance, this task is complicated by the complex cerebrovascular structure and heterogeneous imaging quality. Purpose: This study aims to develop and validate a segmentation-assisted framework designed to improve the accuracy and efficiency of brain vessel centerline extraction from CTA images. We streamline the process of lumen segmentation generation without additional annotation effort from physicians, enhancing the effectiveness of centerline extraction. Methods: The framework integrates four modules: (1) pre-processing techniques that register CTA images with a CT atlas and divide these images into input patches, (2) lumen segmentation generation from annotated vessel centerlines using graph cuts and robust kernel regression, (3) a dual-branch topology-aware UNet (DTUNet) that optimizes the use of the annotated vessel centerlines and the generated lumen segmentation via a topology-aware loss (TAL) and its dual-branch structure, and (4) post-processing methods that skeletonize and refine the lumen segmentation predicted by the DTUNet. Results: An in-house dataset derived from a subset of the MR CLEAN Registry is used to evaluate the proposed framework. The dataset comprises 10 intracranial CTA images, and 40 cube CTA sub-images with a resolution of (Formula presented.) voxels. Via five-fold cross-validation on this dataset, we demonstrate that the proposed framework consistently outperforms state-of-the-art methods in terms of average symmetric centerline distance (ASCD) and overlap (OV). Specifically, it achieves an ASCD of 0.84, an (Formula presented.) of 0.839, and an (Formula presented.) of 0.885 for intracranial CTA images, and obtains an ASCD of 1.26, an (Formula presented.) of 0.779, and an (Formula presented.) of 0.824 for cube CTA sub-images. Subgroup analyses further suggest that the proposed framework holds promise in clinical applications for stroke diagnosis and treatment. Conclusions: By automating the process of lumen segmentation generation and optimizing the network design of vessel centerline extraction, DTUnet achieves high performance without introducing additional annotation demands. This solution promises to be beneficial in various clinical applications in cerebrovascular disease management.

Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery–vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide range of clinical applications. The widely adopted U-Net applied on static DSA frames often struggles with disentangling vessels from subtraction artifacts. Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA. To address these limitations, we propose to simultaneously leverage spatial vasculature and temporal cerebral flow characteristics to segment arteries and veins in DSA. The proposed network, coined CAVE, encodes a 2D+time DSA series using spatial modules, aggregates all the features using temporal modules, and decodes it into 2D segmentation maps. On a large multi-center clinical dataset, CAVE achieves a vessel segmentation Dice of 0.84 (±0.04) and an artery–vein segmentation Dice of 0.79 (±0.06). CAVE surpasses traditional Frangi-based k-means clustering (P < 0.001) and U-Net (P < 0.001) by a significant margin, demonstrating the advantages of harvesting spatio-temporal features. This study represents the first investigation into automatic artery–vein segmentation in DSA using deep learning. The code is publicly available at https://github.com/RuishengSu/CAVE_DSA.

The role of wall shear stress (WSS) in atherosclerotic plaque development is evident, but the relation between WSS and plaque composition in advanced atherosclerosis, potentially resulting in plaque destabilization, is a topic of discussion. Using our previously developed image registration pipeline, we investigated the relation between two WSS metrics, time-averaged WSS (TAWSS) and the oscillatory shear index (OSI), and the local histologically determined plaque composition in a set of advanced human carotid plaques. Our dataset of 11 carotid endarterectomy samples yielded 87 histological cross-sections, which yielded 511 radial bins for analysis. Both TAWSS and OSI values were subdivided into patient-specific low, mid, and high tertiles. This cross-sectional study shows that necrotic core (NC) size and macrophage area are significantly larger in areas exposed to high TAWSS or low OSI. Local TAWSS and OSI tertile values were generally inversely related, as described in the literature, but other combinations were also found. Investigating the relation between plaque vulnerability features and different combinations of TAWSS and OSI tertile values revealed a significantly larger cap thickness in areas exposed to both low TAWSS and low OSI. In conclusion, our study confirmed previous findings, correlating high TAWSS to larger macrophage areas and necrotic core sizes. In addition, our study demonstrated new relations, correlating low OSI to larger macrophage areas, and a combination of low TAWSS and low OSI to larger cap thickness.

Intracranial vessel perforation is a peri-procedural complication during endovascular therapy (EVT). Prompt recognition is important as its occurrence is strongly associated with unfavorable treatment outcomes. However, perforations can be hard to detect because they are rare, can be subtle, and the interventionalist is working under time pressure and focused on treatment of vessel occlusions. Automatic detection holds potential to improve rapid identification of intracranial vessel perforation. In this work, we present the first study on automated perforation detection and localization on X-ray digital subtraction angiography (DSA) image series. We adapt several state-of-the-art single-frame detectors and further propose temporal modules to learn the progressive dynamics of contrast extravasation. Application-tailored loss function and post-processing techniques are designed. We train and validate various automated methods using two national multi-center datasets (i.e., MR CLEAN Registry and MR CLEAN-NoIV Trial), and one international multi-trial dataset (i.e., the HERMES collaboration). With ten-fold cross-validation, the proposed methods achieve an area under the curve (AUC) of the receiver operating characteristic of 0.93 in terms of series level perforation classification. Perforation localization precision and recall reach 0.83 and 0.70 respectively. Furthermore, we demonstrate that the proposed automatic solutions perform at similar level as an expert radiologist.

Treatment of acute ischemic stroke has been recently improved with the introduction of endovascular mechanical thrombectomy, a minimally invasive procedure able to remove a clot using aspiration devices and/or stent-retrievers. Despite the promising and encouraging results, improvements to the procedure and to the stent design are the focus of the recent efforts. Computational studies can pave the road to these improvements, providing their ability to describe and accurately reproduce a real procedure. A patient with ischemic stroke due to intracranial large vessel occlusion was selected and after the creation of the cerebral vasculature from computed tomography images and a histologic analysis to determine the clot composition, the entire thrombectomy procedure was virtually replicated. As in the real situation, the computational replica showed that two attempts were necessary to remove the clot, as a result of the position of the stent retriever with respect to the clot. Furthermore, the results indicated that clot fragmentation did not occur as the deformations were mainly in a compressive state without the possibility for clot cracks to propagate. The accurate representation of the procedure can be used as an important step for operative optimization planning and future improvements of stent designs.

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter-and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.

Background and Purpose: Mechanical properties of thromboemboli play an important role in the efficacy of endovascular thrombectomy (EVT) for acute ischemic stroke. However, very limited data on mechanical properties of human stroke thrombi are available. We aimed to mechanically characterize thrombi retrieved with EVT, and to assess the relationship between thrombus composition and thrombus stiffness. Methods: Forty-one thrombi from 19 patients with acute stroke who underwent EVT between July and October 2019 were mechanically analyzed, directly after EVT. We performed unconfined compression experiments and determined tangent modulus at 75% strain (E^t75) as a measure for thrombus stiffness. Thrombi were histologically analyzed for fibrin/platelets, erythrocytes, leukocytes, and platelets, and we assessed the relationship between histological components and E^t75with univariable and multivariable linear mixed regression. Results: Median E^t75was 560 (interquartile range, 393-1161) kPa. In the multivariable analysis, fibrin/platelets were associated with increased E^t75(aβ, 9 [95% CI, 5 to 13]) kPa, erythrocytes were associated with decreased E^t75%(aβ,-9 [95% CI,-5 to-13]) kPa. We found no association between leukocytes and E^t75. High platelet values were strongly associated with increased E^t75(aβ, 56 [95% CI, 38-73]). Conclusions: Fibrin/platelet content of thrombi retrieved with EVT for acute ischemic stroke is strongly associated with increased thrombus stiffness. For thrombi with high platelet values, there was a very strong relationship with thrombus stiffness. Our data provide a basis for future research on the development of next-generation EVT devices tailored to thrombus composition.

Thrombus composition and mechanical properties significantly impact the ease and outcomes of thrombectomy procedures in patients with acute ischemic stroke. A wide variation exists in the composition of thrombi between patients. If a relationship can be determined between the composition of a thrombus and its mechanical behaviour, as well as between the composition of a thrombus and its radiological imaging characteristics, then there is the potential to personalise thrombectomy treatment based on each individual thrombus. This review aims to give an overview of the current literature addressing this issue. Here, we present a scoping review detailing associations between thrombus composition, mechanical behaviour and radiological imaging characteristics. We conducted two searches 1) on the association between thrombus composition and the mechanical behaviour of the tissue and 2) on the association between radiological imaging characteristics and thrombus composition in the acute stroke setting. The review suggests that higher fibrin and lower red blood cell (RBC) content contribute to stiffer thrombi independent of the loading mode. Further, platelet-contracted thrombi are stiffer than non-contracted compositional counterparts. Fibrin content contributes to the elastic portion of viscoelastic behaviour while RBC content contributes to the viscous portion. It is possible to identify fibrin-rich or RBC-rich thrombi with computed tomography and magnetic resonance imaging vessel signs. Standardisation is required to quantify the association between thrombus density on non-contrast computed tomography and the RBC content. The characterisation of the thrombus fibrin network has not been addressed so far in radiological imaging but may be essential for the prediction of device-tissue interactions and distal thrombus embolization. The association between platelet-driven clot contraction and radiological imaging characteristics has not been explicitly investigated. However, evidence suggests that perviousness may be a marker of clot contraction.

Genetic factors play a major role in frontotemporal dementia (FTD). The majority of FTD cannot be genetically explained yet and it is likely that there are still FTD risk loci to be discovered. Common variants have been identified with genome-wide association studies (GWAS), but these studies have not systematically searched for rare variants. To identify rare and new common variant FTD risk loci and provide more insight into the heritability of C9ORF72-related FTD, we performed a GWAS consisting of 354 FTD patients (including and excluding N = 28 pathological repeat carriers) and 4209 control subjects. The Haplotype Reference Consortium was used as reference panel, allowing for the imputation of rare genetic variants. Two rare genetic variants nearby C9ORF72 were strongly associated with FTD in the discovery (rs147211831: OR = 4.8, P = 9.2 × 10⁻⁹, rs117204439: OR = 4.9, P = 6.0 × 10⁻⁹) and replication analysis (P < 1.1 × 10⁻³). These variants also significantly associated with amyotrophic lateral sclerosis in a publicly available dataset. Using haplotype analyses in 1200 individuals, we showed that these variants tag a sub-haplotype of the founder haplotype of the repeat expansion that was previously found to be present in virtually all pathological C9ORF72 G₄C₂ repeat lengths. This new risk haplotype was 10 times more likely to contain a C9ORF72 pathological repeat length compared to founder haplotypes without one of the two risk variants (~22% versus ~2%; P = 7.70 × 10⁻⁵⁸). In haplotypes without a pathologic expansion, the founder risk haplotype had a higher number of repeats (median = 12 repeats) compared to the founder haplotype without the risk variants (median = 8 repeats) (P = 2.05 × 10⁻²⁶⁰). In conclusion, the identified risk haplotype, which is carried by ~4% of all individuals, is a major risk factor for pathological repeat lengths of C9ORF72 G₄C₂. These findings strongly indicate that longer C9ORF72 repeats are unstable and more likely to convert to germline pathological C9ORF72 repeat expansions.

The collateral score is an important biomarker in decision making for endovascular treatment (EVT) of patients with ischemic stroke. The existing collateral grading systems are based on visual inspection and prone to subjective interpretation and interobserver variation. The purpose of our work is the development of an automatic collateral scoring method. In this work, we present a method that is inspired by human collateral scoring. Firstly, we define an anatomical region by atlas-based registration and extract vessel structures using a deep convolutional neural network. From this, high-level features based on the ratios of vessel length and volume of the occluded and the contralateral side are defined. Multi-class classification models are used to map the feature space to a four-grade collateral score and a quantitative score. The dataset used for training, validation and testing is from a registry of images acquired in clinical routine at multiple medical centers. The model performance is tested on 269 subjects, achieving an accuracy of 0.8. The dichotomized collateral score accuracy is 0.9. The error is comparable to the interobserver variation, the results are comparable to the performance of two radiologists with 10 to 30 years of experience.

OBJECTIVE: The disconnectivity hypothesis postulates that partial loss of connecting white matter fibers between brain regions contributes to the development of dementia. Using diffusion MRI to quantify global and tract-specific white matter microstructural integrity, we tested this hypothesis in a longitudinal population-based study. METHODS: Global and tract-specific fractional anisotropy (FA) and mean diffusivity (MD) were obtained in 4,415 people without dementia (mean age 63.9 years, 55.0% women) from the prospective population-based Rotterdam Study with brain MRI between 2005 and 2011. We modeled the association of these diffusion measures with risk of dementia (follow-up until 2016) and with changes on repeated cognitive assessment after on average 5.4 years, adjusting for age, sex, education, macrostructural MRI markers, depressive symptoms, cardiovascular risk factors, and APOE genotype. RESULTS: During a median follow-up of 6.8 years, 101 participants had incident dementia, of whom 83 had clinical Alzheimer disease (AD). Lower global values of FA and higher values of MD were associated with an increased risk of dementia (adjusted hazard ratio [95% confidence interval (CI)] per SD increase for MD 1.79 [1.44-2.23] and FA 0.65 [0.52-0.80]). Similarly, lower global values of FA and higher values of MD related to more cognitive decline in people without dementia (difference in global cognition per SD increase in MD [95% CI] was -0.04 [-0.07 to -0.01]). Associations were most profound in the projection, association, and limbic system tracts. CONCLUSIONS: Structural disconnectivity is associated with an increased risk of dementia and more pronounced cognitive decline in the general population.

Background: It is increasingly recognized that the complex functions of human cognition are not accurately represented by arbitrarily-defined anatomical brain regions. Given the considerable functional specialization within such regions, more fine-grained studies of brain structure could capture such localized associations. However, such analyses/studies in a large community-dwelling population are lacking. Objective: To perform a fine-mapping of cognitive ability to cortical and subcortical grey matter on magnetic resonance imaging (MRI). Methods: In 3,813 stroke-free and non-demented persons from the Rotterdam Study (mean age 69.1 (±8.8) years; 55.8% women) with cognitive assessments and brain MRI, we performed voxel-based morphometry and subcortical shape analysis on global cognition and separate tests that tapped into memory, information processing speed, fine motor speed, and executive function domains. Results: We found that the different cognitive tests significantly associated with grey matter density in differential but also overlapping brain regions, primarily in the left hemisphere. Clusters of significantly associated voxels with global cognition were located within multiple anatomic regions: left amygdala, hippocampus, parietal lobule, superior temporal gyrus, insula and posterior temporal lobe. Subcortical shape analysis revealed associations primarily within the head and tail of the caudate nucleus, putamen, ventral part of the thalamus, and nucleus accumbens, more equally distributed among the left and right hemisphere. Within the caudate nucleus both positive (head) as well as negative (tail) associations were observed with global cognition. Conclusions: In a large population-based sample, we mapped cognitive performance to cortical and subcortical grey matter density using a hypothesis-free approach with high-dimensional neuroimaging. Leveraging the power of our large sample size, we confirmed well-known associations as well as identified novel brain regions related to cognition.

Objective: To investigate the association of brain volumes, white matter lesion (WML) volumes, and lacunes, with cognitive decline in a population-based cohort of nondemented persons. Methods: Within the Rotterdam Study, 3624 participants underwent brain magnetic resonance imaging. Cognition was evaluated at baseline (2005 to 2009) and at the follow-up visit (2011 to 2013). We used a test battery that tapped into domains of executive function, information processing speed, motor speed, and memory. The volumetric measures assessed were total brain volume, lobar (gray matter and white matter) volumes, and hippocampal volumes. We also studied the association of WML volumes and lacunes with cognitive decline using linear regression models. Results: Total brain volume was associated with decline in global cognition, information processing, and motor speed (P<0.001) in analyses controlled for demographic and vascular factors. Specifically, smaller frontal and parietal lobes were associated with decline in information processing and motor speed, and smaller temporal and parietal lobes were associated with decline in general cognition and motor speed (P<0.001 for all tests). Total WML volume was associated with decline in executive function. Lobar WML volume, hippocampal volume, and lacunes were not associated with cognitive decline. Conclusions: Lower brain volume is associated with subsequent cognitive decline. Although lower total brain volume was significantly associated with decline in global cognition, specific lobar volumes were associated with decline in certain cognitive domains.

Objectives: Diminished function of the posterior cingulate cortex (PCC) is a typical finding in early Alzheimer’s disease (AD). It is hypothesized that in early stage AD, PCC functioning relates to or reflects hippocampal dysfunction or atrophy. The aim of this study was to examine the relationship between hippocampus function, volume and structural connectivity, and PCC activation during an episodic memory task-related fMRI study in mild cognitive impairment (MCI). Method: MCI patients (n = 27) underwent episodic memory task-related fMRI, 3D-T1w MRI, 2D T2-FLAIR MRI and diffusion tensor imaging. Stepwise linear regression analysis was performed to examine the relationship between PCC activation and hippocampal activation, hippocampal volume and diffusion measures within the cingulum along the hippocampus. Results: We found a significant relationship between PCC and hippocampus activation during successful episodic memory encoding and correct recognition in MCI patients. We found no relationship between the PCC and structural hippocampal predictors. Conclusions: Our results indicate a relationship between PCC and hippocampus activation during episodic memory engagement in MCI. This may suggest that during episodic memory, functional network deterioration is the most important predictor of PCC functioning in MCI. Key Points: • PCC functioning during episodic memory relates to hippocampal functioning in MCI. • PCC functioning during episodic memory does not relate to hippocampal structure in MCI. • Functional network changes are an important predictor of PCC functioning in MCI.

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.