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International audience; Initiatives 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.

Scientists and clinicians have traditionally targeted single brain regions with stimulation to modulate brain function and disease. However, brain regions do not operate in isolation, but interact with other regions through networks. As such, stimulation of one region may impact and be impacted by other regions in its network. Here we test whether the effects of brain stimulation can be enhanced by simultaneously targeting a region and its network, identified with resting state functional connectivity MRI. Fifteen healthy participants received two types of transcranial direct current stimulation (tDCS): a traditional two-electrode montage targeting a single brain region (left primary motor cortex [M1]) and a novel eight-electrode montage targeting this region and its associated resting state network. As a control, 8 participants also received multifocal tDCS mismatched to this network. Network-targeted tDCS more than doubled the increase in left M1 excitability over time compared to traditional tDCS and the multifocal control. Modeling studies suggest these results are unlikely to be due to tDCS effects on left M1 itself, however it is impossible to completely exclude this possibility. It also remains unclear whether multifocal tDCS targeting a network selectively modulates this network and which regions within the network are most responsible for observed effects. Despite these limitations, network-targeted tDCS appears to be a promising approach for enhancing tDCS effects beyond traditional stimulation targeting a single brain region. Future work is needed to test whether these results extend to other resting state networks and enhance behavioral or therapeutic effects.

International audience; Right 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.

International audience; Cerebrovascular diseases have been associated with a variety of heart diseases like heart failure or atrial fibrillation, however the mech-anistic relationship between these pathologies is largely unknown. Until now, the study of the underlying heart-brain link has been challenging due to the lack of databases containing data from both organs. Current large data collection initiatives such as the UK Biobank provide us with joint cardiac and brain imaging information for thousands of individuals , and represent a unique opportunity to gain insights about the heart and brain pathophysiology from a systems medicine point of view. Research has focused on standard statistical studies finding correlations in a phenomenological way. We propose a mechanistic analysis of the heart and brain interactions through the personalisation of the parameters of a lumped cardiovascular model under constraints provided by brain-volumetric parameters extracted from imaging, i.e: ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We applied this framework in a cohort of more than 3000 subjects and in a pathological subgroup of 53 subjects diagnosed with atrial fibrillation. Our results show that the use of brain feature constraints helps in improving the parameter estimation in order to identify significant differences associated to specific clinical conditions.

Ablative techniques based on irreversible elec-troporation (IRE) have emerged as an effective cancer thera-py. In these treatments electrical pulses are delivered in order to induce irrecoverable damage to the cell membranes, thus, causing cell death. An interesting feature of electroporation treatments is the capability to predict shape and extension of lesions by means of mathematical simulations. Those predic-tion methods have been refined thought in-vivo experimental observations. Aiming at minimization of animal experiments, vegetal models have been used to easily observe the IRE out-comes. However, these observations are limited at the surface of the tissue. Here we present an improved method able to observe the inner parts of the tissue and therefore evaluate in three-dimensions the results of IRE using potatoes as vegetal models. The technique consists in using a dye solution to en-hance the IRE area in sliced potato tubers. After slice digitali-zation, the electroporated area is automatically identified and the resulting treated volume is calculated. In addition, a three-dimensional reconstruction of both healthy tissue and IRE volume is generated. The proposed evaluation technique was used to assess different pulse protocols outcomes. Numerical simulations had been carried out to compare the numerical predictions to the experimental observations. The obtained results show a clear match between experimental and simulat-ed volumes confirming the reliability of the proposed method. This work received financial support from the Spanish Government through grants TEC2010-17285 and TEC2014- 52383-C3 (TEC2014-52383-C3-2-R) and from the Europe-an Commission through the Marie Curie grant TAMIVIVE (256376).