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A study looking at clearing mouse STN tissue, imaging and segmenting neurons with the goal to link back to optogenetic functional studies in the same tissue.

Supplementary Material relevant to: VT/VF risk in animal model of hypertensive heart disease predicted by distribution of patchy fibrosis.

The use of micro-computed tomography (micro-CT) has risen exponentially over the past decade. The capability of Micro-CT to provide high-resolution images has played an important role in enabling researchers to observe and analyse minute anatomic details in small animals. Furthermore, the non-destructive nature of this imaging modality has allowed researchers to monitor the progression of various diseases in small animal models.Micro-CT is most commonly used for imaging hard tissues and materials, however it’s extendibility to imaging soft tissue has been restricted due to poor inter-tissue contrast. Over the past decade, numerous publications addressed methods of increasing the contrast in the images produced through incorporation of various staining agents.This report describes a proposed study in which iodine potassium iodide (I2KI), phosphotungstic acid (PTA) and phosphomolybdic acid (PMA) were investigated as contrast agents for soft tissue micro-CT imaging. The aim was to understand which stain provides the best inter-tissue contrast in rat cardiac tissue.A staining process itself was developed through this project. Through a literature review of related works it is evident that the perfusion-staining protocols from this project are novel and provide promising outcomes in terms of the contrast obtained.Micro-CT tissue images were assessed and compared for contrast and information content. Contrast was analysed using wide field and focused intensity histograms and statistical measures of intensity distributions. Structural information in each image was assessed using structure tensor analysis. Specific measures were fractional anisotropy and principle (fibre) structure orientations.The key outcomes of this project firstly include: (1) reproducible staining protocols that are capable of providing good contrast when imaging rat cardiac tissue using micro-CT; (2) the finding that PMA provides consistent and good levels of contrast in both normal rat cardiac tissue and diabetic rat cardiac tissue, closely followed by I2KI and by PTA; (3) that analysis between diabetic and non-diabetic rat cardiac tissues suggest an enhanced degree of structural anisotropy in diabetic cardiac tissue but transmural cardiac fibre orientation is unaffected.

The prediction of heart surface potentials using measurements from the body's surface is known as the inverse problem of electrocardiography. It is an ill-posed problem due to the multiple factors that affect the heart signal as it propagates through the body. This report details research performed into a machine learning solution to signal reconstruction as well as an analysis of optimal torso electrode positioning for prediction involving different areas of the heart. The dataset contains simultaneous measurements from a large number of body surface potential (BSP) and heart surface potential (HSP) electrodes, as well as their geometric locations, recorded from an experiment using a human model. Initially, Time Delayed Neural Nets were trained and tested across all BSP to HSP relationships resulting in a slight trend of increased reconstruction correlation with decreased separation of electrodes. However, the TDNNs had overfitted to the data and failed to predict alternate heartbeat pacings. Feed Forward Neural Nets (FFNNs) were tested in a many BSP to many HSP prediction method. Again overfitting occurred. To reduce overfitting, the number of training signals was reduced by analysing the optimal training BSPs for each HSP when using basic perceptrons. This analysis involved repeat sampling and ranking of different BSP combinations, initially, using a Monte Carlo approximation, until being replaced with a meta-heuristic which increased the yield of successful BSP combinations. Successful reconstructions across heartbeat pacings were produced using these optimal BSP combinations for 80 of the 108 HSPs, and future work exists for the testing of this method of prediction using real patient data., Comment: 11 pages, 21 figures

Invasive cardiac catheterisation is a common procedure that is carried out before surgical intervention. Yet, invasive cardiac diagnostics are full of risks, especially for young children. Decades of research has been conducted on the so called inverse problem of electrocardiography, which can be used to reconstruct Heart Surface Potentials (HSPs) from Body Surface Potentials (BSPs), for non-invasive diagnostics. State of the art solutions to the inverse problem are unsatisfactory, since the inverse problem is known to be ill-posed. In this paper we propose a novel approach to reconstructing HSPs from BSPs using a Time-Delay Artificial Neural Network (TDANN). We first design the TDANN architecture, and then develop an iterative search space algorithm to find the parameters of the TDANN, which results in the best overall HSP prediction. We use real-world recorded BSPs and HSPs from individuals suffering from serious cardiac conditions to validate our TDANN. The results are encouraging, in that coefficients obtained by correlating the predicted HSP with the recorded patient' HSP approach ideal values., 4 pages, 9 Figures, 1 Table

The heart is a vital organ that relies on the orchestrated propagation of electrical stimuli to coordinate each heart beat. Abnormalities in the heart's electrical behaviour can be managed with a cardiac pacemaker. Recently, the closed-loop testing of pacemakers with an emulation (real-time simulation) of the heart has been proposed. An emulated heart would provide realistic reactions to the pacemaker as if it were a real heart. This enables developers to interrogate their pacemaker design without having to engage in costly or lengthy clinical trials. Many high-fidelity heart models have been developed, but are too computationally intensive to be simulated in real-time. Heart models, designed specifically for the closed-loop testing of pacemakers, are too abstract to be useful in the testing of physical pacemakers. In the context of pacemaker testing, this paper presents a more computationally efficient heart model that generates realistic continuous-time electrical signals. The heart model is composed of cardiac cells that are connected by paths. Significant improvements were made to an existing cardiac cell model to stabilise its activation behaviour and to an existing path model to capture the behaviour of continuous electrical propagation. We provide simulation results that show our ability to faithfully model complex re-entrant circuits (that cause arrhythmia) that existing heart models can not.