Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets

The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, neuroimaging datasets do not provide a sufficient representation of heart features for the modeling of cardiovascular factors in brain disorders. To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data. Our approach is based on a variational framework for the joint inference of an imputation model of cardiac information from the available features, along with a Gaussian Process emulator that can faithfully reproduce personalised cardiovascular dynamics. Experimental results on UK Biobank show that our model allows accurate imputation of missing cardiac features in datasets containing minimal heart information, e.g. systolic and diastolic blood pressures only, while jointly estimating the emulated parameters of the lumped model. This allows a novel exploration of the heart-brain joint relationship through simulation of realistic cardiac dynamics corresponding to different conditions of brain anatomy

Coeur & Cerveau. Lien entre les pathologies cardiovasculaires et la neurodégénérescence par une approche combinée biophysique et statistique

Clinical studies have identified several cardiovascular risk factors associated to dementia and cardiac pathologies, but their pathological interaction remains poorly understood. Classically, the investigation of the heart-brain relationship is mostly carried out through statistical analysis exploring the association between cardiac indicators and cognitive biomarkers. This kind of investigations are usually performed in large-scale epidemiological datasets, for which joint measurements of both brain and heart are available. For this reason, most of these analyses are performed on cohorts representing the general population. Therefore, the generalisation of these findings to dementia studies is generally difficult, since extensive assessments of cardiac and cardiovascular function in currently available dementia dataset is usually lacking. Another limiting factor of current studies is the limited interpretability of the complex pathophysiological relations between heart and brain allowed by standard correlation analyses. Improving our understanding of the implications of cardiovascular function in dementia ultimately requires the development of more refined mechanistic models of cardiac physiology, as well as the development of novel approaches allowing to integrate these models with image-based brain biomarkers. To address these challenges, in this thesis we developed new computational tools based on the integration of mechanistic models within a statistical learning framework. First, we studied the association between non-observable physiological indicators, such as cardiac contractility, and brain-derived imaging features. To this end, the parameter-space of a mechanistic model of the cardiac function was constrained during the personalisation stage based on the relationships between the parameters of the cardiac model and brain information. This allows to tackle the ill-posedness of the inverse problem associated to model personalisation, and obtain patient-specific solutions that are comparable population-wise.Second, we developed a probabilistic imputation model that allows to impute missing cardiac information in datasets with limited data. The imputation leverages on the cardiac-brain dynamics learned in a large-scale population analysis, and uses this knowledge to obtain plausible solutions in datasets with partial data. The generative nature of the approach allows to simulate the evolution of cardiac model parameters as brain features change. The framework is based on a conditional variational autoencoder (CVAE) combined with Gaussian process (GP) regression. Third, we analysed the potential role of cardiac model parameters as early biomarkers for dementia, which could help to identify individuals at risk. To this end, we imputed missing cardiac information in an Alzheimer's disease (AD) longitudinal cohort. Next, via disease progression modelling we estimated the disease stage for each individual based on the evolution of biomarkers. This allowed to obtain a model of the disease evolution, to analyse the role of cardiac function in AD, and to identify cardiac model parameters as potential early-stage biomarkers of dementia. These results demonstrate the importance of the developed tools by providing clinically plausible associations between cardiac model parameters and brain imaging features in an epidemiological dataset, as well as highlighting insights about the physiological relationship between cardiac function and dementia biomarkers. The obtained results open new research directions, such as the use of more complex mechanistic models that allow to better characterise the heart-brain relationship, or the use of biophysical cardiac models to derive in-silico biomarkers for identifying individuals at risk of dementia in clinical routine, and/or for their inclusion in neuroprotective trials.Les études cliniques ont identifié plusieurs facteurs de risque cardiovasculaire associés à la démence et aux pathologies cardiaques, mais leur interaction pathologique reste mal comprise. Habituellement, l'étude de la relation cœur-cerveau est réalisée à travers d'analyses statistiques explorant l'association entre les indicateurs cardiaques et les biomarqueurs cognitifs. Ce type d'étude est généralement réalisé dans des bases de données épidémiologiques, pour lesquelles des mesures conjointes du cerveau et du cœur sont disponibles. Par conséquent, la généralisation de ces résultats aux études sur la démence est difficile, car les évaluations approfondies des fonctions cardiovasculaires dans les bases de données sur la démence actuellement disponibles font généralement défaut. Un autre facteur limitatif des études actuelles est l'interprétabilité limitée des relations physiopathologiques entre le cœur et le cerveau. L'amélioration de notre compréhension des implications de la fonction cardiovasculaire dans la démence nécessite le développement de modèles mécniaques de la physiologie cardiaque, ainsi que le développement de nouvelles approches permettant d'intégrer ces modèles avec des biomarqueurs cérébraux basés sur l'image. Pour relever ces défis, nous avons développé dans cette thèse de nouveaux outils informatiques basés sur l'intégration de modèles mécaniques dans un cadre d'apprentissage statistique. Premièrement, nous avons étudié l'association entre des indicateurs physiologiques non observables, tels que la contractilité cardiaque, et des caractéristiques d'imagerie dérivées du cerveau. À cette fin, l'espace des paramètres d'un modèle mécanique de la fonction cardiaque a été contraint pendant l'étape de personnalisation sur la base des relations entre les paramètres du modèle cardiaque et les informations cérébrales. Cela permet d’attenuer le caractère mal defini du problème inverse associé à la personnalisation du modèle, et d'obtenir des solutions spécifiques au patient qui sont comparables au sein de la population.Deuxièmement, nous avons développé un modèle d'imputation probabiliste qui permet d'imputer les informations cardiaques manquantes dans des bases de données limitées. L'imputation repose sur les dynamiques cœur-cerveau apprises à partir de l'analyse d'une grande population de sujets, et utilise cette connaissance pour obtenir des solutions plausibles dans des bases de données partielles. La nature générative de l'approche permet de simuler l'évolution des paramètres du modèle cardiaque lorsque les caractéristiques du cerveau changent. Troisièmement, nous avons analysé le rôle des paramètres du modèle cardiaque comme biomarqueurs précoces de la démence, ce qui pourrait aider à identifier les individus à risque. Dans ce but, nous avons imputé les informations cardiaques manquantes dans une cohorte longitudinale de la maladie d'Alzheimer. Ensuite, grâce à la modélisation de la progression de la maladie, nous avons estimé le stade de la maladie pour chaque individu sur la base de l'évolution des biomarqueurs. Ceci a permis d'obtenir un modèle de l'évolution de la maladie, d'analyser le rôle de la fonction cardiaque, et d'identifier les paramètres du modèle cardiaque comme biomarqueurs potentiels de la démence à un stade précoce. Les résultats démontrent l'importance des outils développés en obtenant des associations cliniquement plausibles entre les paramètres du modèle cardiaque et les caractéristiques de l'imagerie cérébrale. Ces résultats mettent également en évidence des informations sur la relation physiologique entre la fonction cardiaque et les biomarqueurs de la démence. Les résultats obtenus ouvrent de nouvelles voies de recherche, telles que l'utilisation de modèles mécaniques plus complexes permettant de mieux caractériser la relation cœur-cerveau, ou l'utilisation de modèles cardiaques biophysiques pour dériver des biomarqueurs in-silico afin d'identifier les individus à risque de démence

Biophysics-based statistical learning: Application to heart and brain interactions

International audienceInitiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of the cardiovascular function, such as cardiac contractility, cannot be measured in-vivo. While these non-observable parameters can be estimated by means of biophysical models, their personalisation is generally an ill-posed problem, often lacking critical data and only applied to small datasets. Therefore, to jointly study brain and heart, we propose an approach in which the parameter personalisation of a lumped cardiovascular model is constrained by the statistical relationships observed between model parameters and brain-volumetric indices extracted from imaging, i.e. ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We explored the plausibility of the learnt relationships by inferring the model parameters conditioned on the absence of part of the target clinical features, applying this framework in a cohort of more than 3 000 subjects and in a pathological subgroup of 59 subjects diagnosed with atrial fibrillation. Our results demonstrate the impact of such external features in the cardiovascular model personalisation by learning more informative parameter-space constraints. Moreover, physiologically plausible mechanisms are captured through these personalised models as well as significant differences associated to specific clinical conditions

Non-Invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-driven or Model-based?

International audienceRight heart catheterisation is considered as the gold standard for the assessment of patients with suspected pulmonary hyper-tension. It provides clinicians with meaningful data, such as pulmonary capillary wedge pressure and pulmonary vascular resistance, however its usage is limited due to its invasive nature. Non-invasive alternatives, like Doppler echocardiography could present insightful measurements of right heart but lack detailed information related to pulmonary vascu-lature. In order to explore non-invasive means, we studied a dataset of 95 pulmonary hypertension patients, which includes measurements from echocardiography and from right-heart catheterisation. We used data extracted from echocardiography to conduct cardiac circulation model per-sonalisation and tested its prediction power of catheter data. Standard machine learning methods were also investigated for pulmonary artery pressure prediction. Our preliminary results demonstrated the potential prediction power of both data-driven and model-based approaches

Tailoring pharmacotherapy to specific eating behaviours in obesity: Can recommendations for personalised therapy be made from the current data?

Pharmacotherapy provides an adjunct to behaviour modification in the management of obesity. There are a number of new drug therapies purportedly targeting appetite; liraglutide, and bupropion/naltrexone, which are European Medicines Agency and US Food and Drug Administration (FDA) approved, and lorcaserin and phentermine/topiramate, which have FDA approval only. Each of the six drugs, used singly or in combination, has distinct pharmacological, and presumably distinct behavioural, mechanisms of action, thus the potential to provide defined therapeutic options to personalise the management of obesity. Yet, with regard to pharmacotherapy for obesity, we are far from true personalised medicine. We review the limited mechanistic data with four mono and combination pharmacotherapies, to assess the potential for tailoring their use to target specific obesogenic behaviours. Potential treatment options are considered, but in the absence of adequate research in respect to effects of these drugs on eating behaviour, neural activity and psychological substrates that underlie poorly controlled eating, we are far from definitive therapeutic recommendations. Specific mechanistic studies and broader behavioural phenotyping, possibly in conjunction with pharmacogenetic research, are required to characterise responders for distinct pharmacotherapeutic options

Editorial: Mathematics for Healthcare as Part of Computational Medicine

This is the final version. Available on open access from Frontiers Media via the DOI in this recordEngineering and Physical Sciences Research Council (EPSRC