COMPUTATIONAL MODELS OF INFLAMMATION AND WOUND HEALING

The acute inflammatory response to biological stress involves a highly conserved cascade of events mediated by a large array of cells and molecules. While not intrinsically detrimental, inflammation can cause secondary or ancillary damage to tissues, which in turn leads to the production of molecules that amplify inflammatory response and, in extreme cases, promote organ dysfunction and death. Therefore, there is a need to identify and modulate dysregulated inflammatory processes while allowing healthy inflammation to carry on. While in vitro and in vivo studies have brought many insights into the components and dynamics of the inflammatory response, computational techniques are becoming increasingly relevant to tease out complex relationships and inter-dependencies that may not be directly measureable. In this dissertation, we explore a computational model of pressure ulcer formation that generates tissue-realistic output and clinically-relevant predictions. By simulating basic inflammatory mechanisms and ischemia/reperfusion injury to soft tissue, our model spontaneously produces both resolving and ulcerative inflammatory patterns from a single set of parameter values. We use statistical methods to explore which mechanisms in the model are responsible for this spontaneous bifurcation. We also use data-driven methods to examine dynamics of inflammatory mediators during in vitro murine hepatocellular stress. Our results lead to identification of MCP-1 as a clinically-predictive inflammatory mediator in human trauma patients

A machine learning approach to support deep brain stimulation programming

Adjusting the stimulation parameters is a challenge in deep brain stimulation (DBS) therapy due to the vast number of different configurations available. As a result, systems based on the visualization of the volume of tissue activated (VTA) produced by a particular stimulation setting have been developed. However, the medical specialist still has to search, by trial and error, for a DBS set-up that generates the desired VTA. Therefore, our goal is developing a DBS parameter tuning strategy for current clinical devices that allows defining a target VTA under biophysically viable constraints. We propose a machine learning approach that allows estimating the DBS parameter values for a given VTA, which comprises two main stages: i) A K-nearest neighbors-based deformation to define a target VTA preserving biophysically viable constraints. ii) A parameter estimation stage that consists of a data projection using metric learning to highlight relevant VTA properties, and a regression/classification algorithm to estimate the DBS parameters that generate the target VTA. Our methodology allows setting a biophysically compliant target VTA and accurately predicts the required configuration of stimulation parameters. Also, the performance of our approach is stable for both isotropic and anisotropic tissue conductivities. Furthermore, the computational time of the trained system is acceptable for real-world implementations

Bayesian Local Smoothing Modeling and Inference for Pre-surgical FMRI Data.

There is a growing interest in using fMRI measurements and analyses as tools for pre-surgical planning. For such applications, spatial precision and control over false negatives and false positives are vital, requiring careful design of an image smoothing method and a classification procedure. This dissertation seeks computationally efficient approaches to overcome the limitation of existing methods and address new challenges in pre-surgical fMRI analyses. In the first study, we develop a Bayesian solution for the pre-surgical analysis of a single fMRI brain image. Specifically, we propose a novel spatially adaptive conditionally autoregressive model (CWAS) that adaptively and locally smoothes the fMRI data. We introduce a Bayesian theoretical decision approach that allows control of both false positives and false negatives to identify activated and deactivated brain regions. We benchmark the proposed solution to two existing spatially adaptive smoothing models, through simulation studies and two patients' pre-surgical fMRI datasets. In the second study, we extend the idea of spatially adaptive smoothing to multiple fMRI brain images in order to leverage spatial correlations across multiple images. In particular, we propose three spatially adaptive multivariate conditional autoregressive models that can be considered as extensions of the multivariate conditional autoregressive (MCAR) model (Gelfand and Vounatsou, 2003), the CWAS model, and the model of Reich and Hodges (2008), respectively, and one mixed-effects model assuming that all observed fMRI images originate from one common image. We compare the performance of the proposed models with those from the MCAR and CWAS models using simulation studies and two sets of fMRI brain images, acquired either from the same patient, same paradigm or same patient, different paradigms. The last study is motivated by fMRI brain images acquired at two different spatial resolutions from the same patient. We develop a Bayesian hierarchical model with spatially varying coefficients to retain the spatial precision from the high resolution image while utilizing information from the low resolution image to improve estimation and inference. Comparisons between the proposed model and the CWAS model, which operates at a single spatial resolution, are performed on simulated data and a patient's multi-resolution pre-surgical fMRI data.PhDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133339/1/zhuqingl_1.pd

Automated Knowledge Discovery from Functional Magnetic Resonance Images using Spatial Coherence

Functional Magnetic Resonance Imaging (fMRI) has the potential to unlock many of the mysteries of the brain. Although this imaging modality is popular for brain-mapping activities, clinical applications of this technique are relatively rare. For clinical applications, classification models are more useful than the current practice of reporting loci of neural activation associated with particular disorders. Also, since the methods used to account for anatomical variations between subjects are generally imprecise, the conventional voxel-by-voxel analysis limits the types of discoveries that are possible. This work presents a classification-based framework for knowledge discovery from fMRI data. Instead of voxel-centric knowledge discovery, this framework is segment-centric, where functional segments are clumps of voxels that represent a functional unit in the brain. With simulated activation images, it is shown that this segment-based approach can be more successful for knowledge discovery than conventional voxel-based approaches. The spatial coherence principle refers to the homogeneity of behavior of spatially contiguous voxels. Auto-threshold Contrast Enhancing Iterative Clustering (ACEIC) - a new algorithm based on the spatial coherence principle is presented here for functional segmentation. With benchmark data, it is shown that the ACEIC method can achieve higher segmentation accuracy than Probabilistic Independent Component Analysis - a popular method used for fMRI data analysis. The spatial coherence principle can also be exploited for voxel-centric image-classification problems. Spatially Coherent Voxels (SCV) is a new feature selection method that uses the spatial coherence principle to eliminate features that are unlikely to be useful for classification. For a Substance Use Disorder dataset, it is demonstrated that feature selection with SCV can achieve higher classification accuracies than conventional feature selection methods

Bayesian Estimation of Probabilistic Atlas for Anatomically-Informed Functional MRI Group Analyses

International audienceTraditional analyses of Functional Magnetic Resonance Imaging (fMRI) use little anatomical information. The registration of the images to a template is based on the individual anatomy and ignores functional information; subsequently detected activations are not confined to gray matter (GM). In this paper, we propose a statistical model to estimate a probabilistic atlas from functional and T1 MRIs that summarizes both anatomical and functional information and the geometric variability of the population. Registration and Segmentation are performed jointly along the atlas estimation and the functional activity is constrained to the GM, increasing the accuracy of the atlas

Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study

Background: Ablation is an effective therapy in atrial fibrillation (AF) patients in which an electrical driver can be identified. Objective: The aim of this study is to present and discuss a novel and strictly non-invasive approach to map and identify atrial regions responsible for AF perpetuation. Methods: Surface potential recordings of 14 patients with AF were recorded using a 67-lead recording system. Singularity points (SPs) were identified in surface phase maps after band-pass filtering at the highest dominant frequency (HDF). Mathematical models of combined atria and torso were constructed and used to investigate the ability of surface phase maps to estimate rotor activity in the atrial wall. Results: The simulations show that surface SPs originate at atrial SPs, but not all atrial SPs are reflected at the surface. Stable SPs were found in AF signals during 8.3±5.7% vs. 73.1±16.8% of the time in unfiltered vs. HDF-filtered patient data respectively (p<0.01). The average duration of each rotational pattern was also lower in unfiltered than in HDF-filtered AF signals (160±43 vs. 342±138 ms, p<0.01) resulting in 2.8±0.7 rotations per rotor. Band-pass filtering reduced the apparent meandering of surface HDF rotors by reducing the effect of the atrial electrical activity taking place at different frequencies. Torso surface SPs representing HDF rotors during AF were reflected at specific areas corresponding to the fastest atrial location. Conclusion: Phase analysis of surface potential signals after HDF-filtering during AF shows reentrant drivers localized to either the LA or RA, helping in localizing ablation targetsThis work was supported in part by the Spanish Society of Cardiology (Becas Investigacion Clinica 2009); the Universitat Politecnica de Valencia through its research initiative program; the Generalitat Valenciana grant (ACIF/2013/021); the Ministerio de Economia y Competitividad, Rod RIC; the Centro Nacional de Investigaciones Cardiovasculares (proyecto CNIC-13); the Coulter Foundation from the Biomedical Engineering Department, University of Michigan; the Gelman Award from the Cardiovascular Division, University of Michigan; the National Heart, Lung, and Blood Institute grants (P01411.039707, P01-1111187226, and R01-11L118304); and the Leducq Foundation. Dr Femandez-Aviles served on the advisory board of Medtronic and has received research funding from St Jude Medical Spain. Dr Berenfeld has received research support from Medtronic and St Jude Medical; he is a colbunder and scientific officer of Rhythm Solutions. None of the companies disclosed financed the research described in this article.Rodrigo Bort, M.; Guillem Sánchez, MS.; Climent, AM.; Pedrón Torrecilla, J.; Liberos Mascarell, A.; Millet Roig, J.; Fernandez-Aviles, F.... (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm. 11(9):1584-1591. https://doi.org/10.1016/j.hrthm.2014.05.013S1584159111

Improving data extraction methods for large molecular biology datasets.

In the past, an experiment involving a pair wise comparison normally involved one or a few dependant variables. Now, 1000s of dependent variables can be measured simultaneously in a single experiment, be it detecting genes via a microarray experiment, sequencing genomes, or detecting microbial species based on DNA fragments using molecular techniques. How we analyze such large collections of data will be a major scientific focus over the next decade. Statistical methods that were once acceptable for comparing a few conditions are being revised to handle 1000?s of experiments. Molecular biology techniques that explored 1 gene or species have evolved and are now capable of generating complex datasets requiring new strategies and ways of thinking in order to discover biologically meaningful results. The central theme of this dissertation is to develop strategies that deal with a number of issues that are present in these large scale datasets. In chapter 1, I describe a microarray analytical method that can be applied to low replicate experiments. In chapter?s 2-4, the focus is how to best analyze data from ARISA (a PCR based molecular method for rapidly generating a finger print of microbial diversity). Chapter 2 focuses on qualifying ARISA data so that data will best represent its biological source, prior to further analysis. Chapter 3 focuses on how to best compare ARISA profiles to one another. Chapter 4 focuses on developing a software tool that implements the data processing and clustering strategies from chapter?s 2 and 3. The findings described herein provide the scientific community with improved analytical strategies in both the microarray and ARISA research areas