Model order reduction for left ventricular mechanics via congruency training

Computational models of the cardiovascular system and specifically heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. To achieve clinical utility, models should be able to assimilate the diagnostic multi-modality data available for each patient and generate consistent representations of the underlying cardiovascular physiology. While finite element models of the heart can naturally account for patient-specific anatomies reconstructed from medical images, optimizing the many other parameters driving simulated cardiac functions is challenging due to computational complexity. With the goal of streamlining parameter adaptation, in this paper we present a novel, multifidelity strategy for model order reduction of 3-D finite element models of ventricular mechanics. Our approach is centered around well established findings on the similarity between contraction of an isolated muscle and the whole ventricle. Specifically, we demonstrate that simple linear transformations between sarcomere strain (tension) and ventricular volume (pressure) are sufficient to reproduce global pressure-volume outputs of 3-D finite element models even by a reduced model with just a single myocyte unit. We further develop a procedure for congruency training of a surrogate low-order model from multiscale finite elements, and we construct an example of parameter optimization based on medical images. We discuss how the presented approach might be employed to process large datasets of medical images as well as databases of echocardiographic reports, paving the way towards application of heart mechanics models in the clinical practice. © 2020 Di Achille et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.19-14- 00134Russell Sage Foundation, RSFSK and OS were funded by RSF (http:// www.rscf.ru/en/) as described below. Part of this work was carried out within the framework of the IIF UrB RAS government assignment and was partially supported by the UrFU Competitiveness Enhancement Program (agreement 02. A03.21.0006) as well as the RSF grant (No. 19-14- 00134). The Uran supercomputer at IMM UrB RAS was used for part of the model calculations. IBM provided support in the form of salaries for authors PA, JP, JK and VG but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the "author contributions" section

Predicting Forage Intake by Sheep through the Pampa Corte Model or NRC

The aim of the present study was to evaluate the precision and accuracy of the Pampa Corte and National Research Council (2007; NRC) models for predicting forage intake (FI) by sheep. Individual data (n = 213) of observed FI, body weight and chemical composition of consumed diet were taken from fifteen indoor digestibility trials conducted with male sheep housed in metabolic cages and fed only forage ad libitum. The diets were composed of tropical grasses, temperate grasses and legumes. Individual observations of FI were averaged by treatment (n = 32) into each experiment which were then compared to FI values predicted by Pampa Corte model or NRC using concordance correlation coefficient (CCC) and regression analysis. The average value of observed FI was 847 (± 241) whereas those predicted by Pampa Corte model and NRC were, respectively, 826 (± 230) and 987 (± 208) g DM/day. Observed values of FI were linearly related (P \u3c 0.01) to those predicted through either Pampa Corte or NRC. However, the Pampa Corte resulted in higher CCC than NRC. Also, through the Pampa Corte model, the linear regression presented a slope not different from 1 and an intercept not different from 0. The NRC model, however, resulted in a slope of the linear regression lower than 1 despite the intercept was not different from 0. The Pampa Corte model was more precise and accurate in predicting FI by sheep fed only forage than NRC

Generative Adversarial Networks for Construction of Virtual Populations of Mechanistic Models: Simulations to Study Omecamtiv Mecarbil Action

Biophysical models are increasingly used to gain mechanistic insights by fitting and reproducing experimental and clinical data. The inherent variability in the recorded datasets, however, presents a key challenge. In this study, we present a novel approach, which integrates mechanistic modeling and machine learning to analyze in vitro cardiac mechanics data and solve the inverse problem of model parameter inference. We designed a novel generative adversarial network (GAN) and employed it to construct virtual populations of cardiac ventricular myocyte models in order to study the action of Omecamtiv Mecarbil (OM), a positive cardiac inotrope. Populations of models were calibrated from mechanically unloaded myocyte shortening recordings obtained in experiments on rat myocytes in the presence and absence of OM. The GAN was able to infer model parameters while incorporating prior information about which model parameters OM targets. The generated populations of models reproduced variations in myocyte contraction recorded during in vitro experiments and provided improved understanding of OM’s mechanism of action. Inverse mapping of the experimental data using our approach suggests a novel action of OM, whereby it modifies interactions between myosin and tropomyosin proteins. To validate our approach, the inferred model parameters were used to replicate other in vitro experimental protocols, such as skinned preparations demonstrating an increase in calcium sensitivity and a decrease in the Hill coefficient of the force–calcium (F–Ca) curve under OM action. Our approach thereby facilitated the identification of the mechanistic underpinnings of experimental observations and the exploration of different hypotheses regarding variability in this complex biological system. © 2021, The Author(s).This work was partially supported by the EU’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie (g.a. 764738) state Program (No. AAAA-A19-119070190064-4) and the research grant from RFBR (No. 19-31-90089)

Membrane Potential-Dependent Modulation of Recurrent Inhibition in Rat Neocortex

Dynamic balance of excitation and inhibition is crucial for network stability and cortical processing, but it is unclear how this balance is achieved at different membrane potentials (Vm) of cortical neurons, as found during persistent activity or slow Vm oscillation. Here we report that a Vm-dependent modulation of recurrent inhibition between pyramidal cells (PCs) contributes to the excitation-inhibition balance. Whole-cell recording from paired layer-5 PCs in rat somatosensory cortical slices revealed that both the slow and the fast disynaptic IPSPs, presumably mediated by low-threshold spiking and fast spiking interneurons, respectively, were modulated by changes in presynaptic Vm. Somatic depolarization (>5 mV) of the presynaptic PC substantially increased the amplitude and shortened the onset latency of the slow disynaptic IPSPs in neighboring PCs, leading to a narrowed time window for EPSP integration. A similar increase in the amplitude of the fast disynaptic IPSPs in response to presynaptic depolarization was also observed. Further paired recording from PCs and interneurons revealed that PC depolarization increases EPSP amplitude and thus elevates interneuronal firing and inhibition of neighboring PCs, a reflection of the analog mode of excitatory synaptic transmission between PCs and interneurons. Together, these results revealed an immediate Vm-dependent modulation of cortical inhibition, a key strategy through which the cortex dynamically maintains the balance of excitation and inhibition at different states of cortical activity