A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments

In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a generative model. A reference morphology is deformed along specific trajectories to match subject specific morphologies. It is used to define two imaging progression markers: 1) a morphological age and 2) a disease score. These markers can be computed locally in any brain region. The approach is evaluated on brain structural magnetic resonance images (MRI) from the ADNI database. The generative model is first estimated on a control population, then, for each subject, the markers are computed for each acquisition. The longitudinal evolution of these markers is then studied in relation with the clinical diagnosis of the subjects and used to generate possible morphological evolution. In the model, the morphological changes associated with normal aging are mainly found around the ventricles, while the Alzheimer's disease specific changes are more located in the temporal lobe and the hippocampal area. The statistical analysis of these markers highlights differences between clinical conditions even though the inter-subject variability is quiet high. In this context, the model can be used to generate plausible morphological trajectories associated with the disease. Our method gives two interpretable scalar imaging biomarkers assessing the effects of aging and disease on brain morphology at the individual and population level. These markers confirm an acceleration of apparent aging for Alzheimer's subjects and can help discriminate clinical conditions even in prodromal stages. More generally, the joint modeling of normal and pathological evolutions shows promising results to describe age-related brain diseases over long time scales.Comment: NeuroImage, Elsevier, In pres

Proteomic mapping of atrial and ventricular heart tissue in patients with aortic valve stenosis

Aortic valve stenosis (AVS) is one of the most common valve diseases in the world. However, detailed biological understanding of the myocardial changes in AVS hearts on the proteome level is still lacking. Proteomic studies using high-resolution mass spectrometry of formalin-fixed and paraffin-embedded (FFPE) human myocardial tissue of AVS-patients are very rare due to methodical issues. To overcome these issues this study used high resolution mass spectrometry in combination with a stem cell- derived cardiac specific protein quantification-standard to profile the proteomes of 17 atrial and 29 left ventricular myocardial FFPE human myocardial tissue samples from AVS-patients. In our proteomic analysis we quantified a median of 1980 (range 1495–2281) proteins in every single sample and identified significant upregulation of 239 proteins in atrial and 54 proteins in ventricular myocardium. We compared the proteins with published data. Well studied proteins reflect disease-related changes in AVS, such as cardiac hypertrophy, development of fibrosis, impairment of mitochondria and downregulated blood supply. In summary, we provide both a workflow for quantitative proteomics of human FFPE heart tissue and a comprehensive proteomic resource for AVS induced changes in the human myocardium

The Genetic Makeup of the Electrocardiogram

The electrocardiogram (ECG) is one of the most useful non-invasive diagnostic tests for a wide array of cardiac disorders. Traditional approaches to analyzing ECGs focus on individual segments. Here, we performed comprehensive deep phenotyping of 77,190 ECGs in the UK Biobank across the complete cycle of cardiac conduction, resulting in 500 spatial-temporal datapoints, across 10 million genetic variants. In addition to characterizing polygenic risk scores for the traditional ECG segments, we identified over 300 genetic loci that are statistically associated with the high-dimensional representation of the ECG. We established the genetic ECG signature for dilated cardiomyopathy, associated the BAG3, HSPB7/CLCNKA, PRKCA, TMEM43, and OBSCN loci with disease risk and confirmed this association in an independent cohort. In total, our work demonstrates that a high-dimensional analysis of the entire ECG provides unique opportunities for studying cardiac biology and disease and furthering drug development. A record of this paper's transparent peer review process is included in the Supplemental Information

Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis

Background: Cardiovascular magnetic resonance (CMR) is increasingly used for risk stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers and their respective thresholds remains undefined. Objectives: Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality. Methods: Patients with severe AS undergoing AVR (n = 440, derivation; n = 359, validation cohort) were prospectively enrolled across 13 international sites (median 3.8 years’ follow-up). CMR was performed shortly before surgical or transcatheter AVR. A random survival forest model was built using 29 variables (13 CMR) with post-AVR death as the outcome. Results: There were 52 deaths in the derivation cohort and 51 deaths in the validation cohort. The 4 most predictive CMR markers were extracellular volume fraction, late gadolinium enhancement, indexed left ventricular end-diastolic volume (LVEDVi), and right ventricular ejection fraction. Across the whole cohort and in asymptomatic patients, risk-adjusted predicted mortality increased strongly once extracellular volume fraction exceeded 27%, while late gadolinium enhancement >2% showed persistent high risk. Increased mortality was also observed with both large (LVEDVi >80 mL/m2) and small (LVEDVi ≤55 mL/m2) ventricles, and with high (>80%) and low (≤50%) right ventricular ejection fraction. The predictability was improved when these 4 markers were added to clinical factors (3-year C-index: 0.778 vs 0.739). The prognostic thresholds and risk stratification by CMR variables were reproduced in the validation cohort. Conclusions: Machine learning identified myocardial fibrosis and biventricular remodeling markers as the top predictors of survival in AS and highlighted their nonlinear association with mortality. These markers may have potential in optimizing the decision of AVR

Large-scale neuroanatomical study uncovers 198 gene associations in mouse brain morphogenesis.

Brain morphogenesis is an important process contributing to higher-order cognition, however our knowledge about its biological basis is largely incomplete. Here we analyze 118 neuroanatomical parameters in 1,566 mutant mouse lines and identify 198 genes whose disruptions yield NeuroAnatomical Phenotypes (NAPs), mostly affecting structures implicated in brain connectivity. Groups of functionally similar NAP genes participate in pathways involving the cytoskeleton, the cell cycle and the synapse, display distinct fetal and postnatal brain expression dynamics and importantly, their disruption can yield convergent phenotypic patterns. 17% of human unique orthologues of mouse NAP genes are known loci for cognitive dysfunction. The remaining 83% constitute a vast pool of genes newly implicated in brain architecture, providing the largest study of mouse NAP genes and pathways. This offers a complementary resource to human genetic studies and predict that many more genes could be involved in mammalian brain morphogenesis

Applying Deep Learning To Identify Imaging Biomarkers To Predict Cardiac Outcomes In Cancer Patients

Cancer patients are a unique population with increased mortality from cardiovascular disease, however only half of high-risk patients are medically optimized. Physicians ascertain cardiovascular risk from several risk predictors using demographic information, family history, and imaging data. The Agatston score, a measure of total calcium burden in coronary arteries on CT scans, is the current best predictor for major adverse cardiac events (MACE). Yet, the score is limited as it does not provide information on atherosclerotic plaque characteristics or distribution. In this study, we use deep learning techniques to develop an imaging-based biomarker that can robustly predict MACE in lung cancer patients. We selected participants with screen-detected lung cancer from the National Lung Screening Trial (NLST) and used cardiovascular mortality as our primary outcome. We applied automated segmentation algorithms to low-dose chest CT scans from NLST participants to segment cardiac substructures. Following segmentation, we extracted radiomic features from selected cardiac structures. We then used this dataset to train a regression model to predict cardiovascular death. We used a pre-trained nnU-Net model to successfully segment large cardiac structures on CT scans. These automated large cardiac structures had features that were predictive of MACE. We then successfully extract radiomic features from our areas of interest and use this high-dimensional dataset to train a regression model to predict MACE. We demonstrated that automated segmentation algorithms can result in low-cost non-invasive predictive biomarkers for MACE. We were able to demonstrate that radiomic feature extraction from segmented substructures can be used to develop a high-dimensional biomarker. We hope that such a scoring system can help physicians adequately determine cardiovascular risk and intervene, resulting in better patient outcomes

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival