Bayesian Active Learning for Personalization and Uncertainty Quantification in Cardiac Electrophysiological Model

Cardiacvascular disease is the top death causing disease worldwide. In recent years, high-fidelity personalized models of the heart have shown an increasing capability to supplement clinical cardiology for improved patient-specific diagnosis, prediction, and treatment planning. In addition, they have shown promise to improve scientific understanding of a variety of disease mechanisms. However, model personalization by estimating the patient-specific tissue properties that are in the form of parameters of a physiological model is challenging. This is because tissue properties, in general, cannot be directly measured and they need to be estimated from measurements that are indirectly related to them through a physiological model. Moreover, these unknown tissue properties are heterogeneous and spatially varying throughout the heart volume presenting a difficulty of high-dimensional (HD) estimation from indirect and limited measurement data. The challenge in model personalization, therefore, summarizes to solving an ill-posed inverse problem where the unknown parameters are HD and the forward model is complex with a non-linear and computationally expensive physiological model. In this dissertation, we address the above challenge with following contributions. First, to address the concern of a complex forward model, we propose the surrogate modeling of the complex target function containing the forward model – an objective function in deterministic estimation or a posterior probability density function in probabilistic estimation – by actively selecting a set of training samples and a Bayesian update of the prior over the target function. The efficient and accurate surrogate of the expensive target function obtained in this manner is then utilized to accelerate either deterministic or probabilistic parameter estimation. Next, within the framework of Bayesian active learning we enable active surrogate learning over a HD parameter space with two novel approaches: 1) a multi-scale optimization that can adaptively allocate higher resolution to heterogeneous tissue regions and lower resolution to homogeneous tissue regions; and 2) a generative model from low-dimensional (LD) latent code to HD tissue properties. Both of these approaches are independently developed and tested within a parameter optimization framework. Furthermore, we devise a novel method that utilizes the surrogate pdf learned on an estimated LD parameter space to improve the proposal distribution of Metropolis Hastings for an accelerated sampling of the exact posterior pdf. We evaluate the presented methods on estimating local tissue excitability of a cardiac electrophysiological model in both synthetic data experiments and real data experiments. Results demonstrate that the presented methods are able to improve the accuracy and efficiency in patient-specific model parameter estimation in comparison to the existing approaches used for model personalization

Perceptual Decision-making Difficulty Modulates Feedforward Effective Connectivity to the Dorsolateral Prefrontal Cortex

Diverse cortical structures are known to coordinate activity as a network in relaying and processing of visual information to discriminate visual objects. However, how this discrimination is achieved is still largely unknown. To contribute to answering this question, we used face-house categorization tasks with three levels of noise in face and house images in functional magnetic resonance imaging (fMRI) experiments involving thirty-three participants. The behavioral performance error and response time (RT) were correlated with noise in face-house images. We then built dynamical causal models (DCM) of fMRI blood-oxygenation level dependent (BOLD) signals from the face and house category-specific regions in ventral temporal (VT) cortex, the fusiform face area (FFA) and parahippocampal place area (PPA), and the dorsolateral prefrontal cortex (dlPFC). We found a strong feed-forward intrinsic connectivity pattern from FFA and PPA to dlPFC. Importantly, the feed-forward connectivity to dlPFC was significantly modulated by the perception of both faces and houses. The dlPFC-BOLD activity, the connectivity from FFA and PPA to the dlPFC all increased with noise level. These results suggest that the FFA-PPA-dlPFC network plays an important role for relaying and integrating competing sensory information to arrive at perceptual decisions

An augmented moment method for stochastic ensembles with delayed couplings: II. FitzHugh-Nagumo model

Dynamics of FitzHugh-Nagumo (FN) neuron ensembles with time-delayed couplings subject to white noises, has been studied by using both direct simulations and a semi-analytical augmented moment method (AMM) which has been proposed in a recent paper [H. Hasegawa, E-print: cond-mat/0311021]. For $N$-unit FN neuron ensembles, AMM transforms original $2N$-dimensional {\it stochastic} delay differential equations (SDDEs) to infinite-dimensional {\it deterministic} DEs for means and correlation functions of local and global variables. Infinite-order recursive DEs are terminated at the finite level $m$ in the level-$m$ AMM (AMM$m$), yielding $8(m+1)$-dimensional deterministic DEs. When a single spike is applied, the oscillation may be induced if parameters of coupling strength, delay, noise intensity and/or ensemble size are appropriate. Effects of these parameters on the emergence of the oscillation and on the synchronization in FN neuron ensembles have been studied. The synchronization shows the {\it fluctuation-induced} enhancement at the transition between non-oscillating and oscillating states. Results calculated by AMM5 are in fairly good agreement with those obtained by direct simulations.Comment: 15 pages, 3 figures; changed the title with correcting typos, accepted in Phys. Rev. E with some change

Competitive forbs in high-producing temporary grasslands with perennial ryegrass and red clover can increase plant diversity and herbage yield

European agriculture focuses on increasing biodiversity. However, in highly productive temporary grasslands in grass / arable systems, the biodiversity is usually low. Three non-leguminous species have shown high competitive strength in temporary grasslands and ample possibility to increase biodiversity without compromising yields. In this experiment, we examined in detail the competitiveness and productivity of the three forb species: chicory (Cichorium intybus), ribwort plantain (Plantago lanceolata) and caraway (Carum carvi) grown in different proportions in mixtures including traditional grassland species: perennial ryegrass (Lolium perenne) and red clover (Trifolium pratense), with fertiliser application as an additional factor. Dry matter (DM) yield and botanical composition were measured during one complete growing season. Annual DM yields were mostly equal when forbs were included in the grassland mixture. However, a three species mixture composed of perennial ryegrass, red clover and ribwort plantain had highest yield potential, especially with fertilisation. Chicory and ribwort plantain showed high competitive strength in the mixtures. Grass gave considerable response to fertilisation, but no consistent trend was found in the forbs. In conclusion, we found positive effect of plant species diversity and fertilisation on the ability of grassland mixtures to produce higher herbage yield

Competitive forbs in high-producing temporary grasslands with perennial ryegrass and red clover can increase plant diversity and herbage yield

In highly productive temporary grasslands in Europe, plant diversity is usually low. Some non-leguminous species have shown a high competitive ability in temporary grasslands and can increase plant diversity without compromising yields. In an experiment, the competitiveness and productivity of three forb species: chicory (Cichorium intybus), ribwort plantain (Plantago lanceolata) and caraway (Carum carvi), grown in different proportions in mixtures including traditional sown grassland species, perennial ryegrass and red clover, were examined with slurry application as an additional factor. Dry matter (DM) yield and botanical composition were measured during one complete growing season. Annual DM yields were mostly similar when forbs were included in the grassland mixture. A three-species mixture (perennial ryegrass, red clover and ribwort plantain) had the highest yield potential, especially for the slurry application treatment. Chicory and ribwort plantain were highly competitive in the mixtures. The response in the DM yield of perennial ryegrass to slurry application was considerable, but no consistent trend was found in the forbs. In conclusion, forbs contributed to increased plant species diversity and herbage DM yield, and fertilisation had positive effect on herbage yield of grassland mixtures