微電列陣今已被廣泛用於各種生理和理的研究。通過把微電列陣連接到肌肉細胞，細胞外的電生理信號會被有效地記錄，我們進而對尖峰信號的傳播模式進行分析，以便了解肌肉收縮的模式。本文旨在對觀測到的電生理信號進行統計模型擬合，從而獲得對於肌肉收縮模式的統計推論。我們提出了三種方法用以提取尖峰信號的激活時間，分別為均值方差法、局部加權回歸法(LOWESS方法)和Butterworth濾波法。然後對抽取出來的尖峰信號應用隨機Hough轉換，識別出多個傳播的信號波，從而得到肌肉收縮的率。對於每個信號波，我們建立了兩個模型來描述信號的傳播模式，即圓形波陣面模型和線性波陣模型。通過這兩種模型擬合，表達信傳播特徵的參數可被估算，例如激發信號波的起源位和起始時間，信號的傳播方向以及速度等。利用根據兩種模型合成的模擬數據，我們證明了隨機霍夫轉換算法和模型擬合的有效性及準確性，並把文中提出的算法用於大鼠心肌培養細胞的一個數據集。由此數據集得出的結果可以用於監測細胞的電生理變化，從而闡明藥物或環條件對心肌細胞產生的影響。 / The microelectrode array (MEA) has been widely used in physiological and pharmacological research. By attaching the MEA system to muscle cells, extracellular electrophysiological signals can be recorded, and the spike-signal propagation pattern can be analyzed for understanding the muscle contraction pattern. This thesis aims at providing a statistical framework for analyzing the muscle contraction pattern from the observed electrophysiological signals. We first provides three methods for extracting the activation time of signal spikes: the mean-variance method, the LOWESS smoothing method, and the Butterworth filtering method. The randomized Hough transform is then applied to the signal spikes to identify the multiple propagating waves, which gives the rate of beating. For each propagating wave, we propose two models to describe the signal propagation pattern, namely the circular wavefront model and the linear wavefront model. By fitting these two models, parameters that characterize the signal propagation can be estimated, such as the origin and time of excitation, the direction of propagation, and the speed of propagation. We demonstrate the performances of the randomized Hough tranform algorithm and model fitting in two simulation studies, and apply these approaches to a real data set of cultured cardiac myocytes of rats. The result may be used to monitor the electrophysiological changes and thereby elucidate the drug effect or environmental condition on cardiomyocytes. / Detailed summary in vernacular field only. / Lu, Jiayi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 62-65). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivating problem --- p.1 / Chapter 1.2 --- An overview of MEA --- p.2 / Chapter 1.3 --- Electrophysiology of cardiac myocytes --- p.5 / Chapter 1.4 --- Organization --- p.5 / Chapter 2 --- A generative model for MEA data --- p.7 / Chapter 2.1 --- Circular wavefront model --- p.9 / Chapter 2.2 --- Linear wavefront model --- p.11 / Chapter 3 --- Computing method for MEA signals --- p.13 / Chapter 3.1 --- Preliminaries --- p.13 / Chapter 3.1.1 --- Locally weighted scatterplot smoothing(LOWESS) --- p.13 / Chapter 3.1.2 --- Butterworth filter --- p.16 / Chapter 3.1.3 --- Hough transform --- p.16 / Chapter 3.1.4 --- Nonlinear minimization --- p.21 / Chapter 3.2 --- Overall procedure for MEA data analysis --- p.24 / Chapter 3.3 --- Extract the spike activation time --- p.25 / Chapter 3.4 --- Identification of multiple propagating waves --- p.28 / Chapter 3.5 --- Model fitting --- p.29 / Chapter 3.5.1 --- Circular wavefront model --- p.29 / Chapter 3.5.2 --- Linear wavefront model --- p.33 / Chapter 4 --- Simulation study based on synthesized data --- p.35 / Chapter 4.1 --- Wave detection using Hough transform --- p.35 / Chapter 4.1.1 --- Data synthesis from linear wavefront model --- p.35 / Chapter 4.1.2 --- Performance of the randomized Hough transform --- p.38 / Chapter 4.2 --- Model fitting for signal propagating pattern --- p.38 / Chapter 4.2.1 --- Data Synthesis from circular wavefront model --- p.38 / Chapter 4.2.2 --- Performance of the model fitting algorithm --- p.42 / Chapter 5 --- Real data application --- p.47 / Chapter 5.1 --- Data set --- p.47 / Chapter 5.2 --- Extract the spike activation time --- p.49 / Chapter 5.3 --- Identify multiple propagating waves --- p.52 / Chapter 5.4 --- Model fitting --- p.52 / Chapter 5.4.1 --- Fitting the circular wavefront model --- p.52 / Chapter 5.4.2 --- Fitting the linear wavefront model --- p.55 / Chapter 5.4.3 --- Comparison of the two models --- p.56 / Chapter 6 --- Conclusions and future directions --- p.60 / Chapter 6.1 --- Conclusions --- p.60 / Chapter 6.2 --- Future directions --- p.61

Event related potentials (ERPs) are obtained from noninvasive electroencephalograms (EEG) which measure neuronal activity of brain on the scalp. However, conventional ERPs derived by averaging of single EEG trials have strong latency variability and are smeared, resulting in blurred scalp topography, especially in late components of ERP. The smearing problem had been addressed by reconstructing ERPs after latency correction with a new EEG analysis method Residue Iteration Decompo¬sition (RIDE), which was demonstrated in a face priming paradigm to improve distinctness in scalp topography (Ouyang et al., 2011). This thesis aims to (1) extend the benefits of RIDE to neural source space by localizing the neural generators of ERPs, thereby developing an integrated RIDE framework for improvement in source localization and causal modeling of effective source networks, and (2) apply the framework to the face priming paradigm for famous faces, to explore the dynamics of face processing and priming. We localized sources through brain electrical source analysis for both conventional ERP and RIDE derived ERPs (RERPs). RERPs allowed localization of an additional motor execution source (Premotor Cortex, PMC), apart from 5 other common sources, of which 2 (Occipital Lobe, OL; Fusiform Gyrus, FG) were obtained from early activity (< 250 ms) and 3 (Mediotemporal lobe, MTL; Prefrontal Cortex, PFC; Anterior Temporal Lobe, ATL) from late activities (> 250 ms) of RERPs respectively. Priming effects, i.e., the difference between primed famous (PF) and unprimed famous (UF) face conditions in source waveforms (SWFs), were extended and enhanced in RERPs, especially for late sources. The priming effects revealed (1) the role of sources in each hemisphere that play in perception, memory and execution, (2) parallel processing of information in sources, (3) early processing in the right hemisphere, and (4) predominance of the right hemisphere in face recognition. Results confirmed SWFs of RERPs as better choice for the dynamic causal model (DCM). Two candidate DCM models, forward (F) and forward-backward (FB) were outlined on each hemisphere with SWFs from PF and UF conditions of RERP data. Priming has tendency to facilitate the FB model in the left hemisphere. On the other hand, independent of model preference, priming strengthened a bidirectional connection between FG and PFC in both hemispheres; this indicates a strong role of FG in structural representation and of PFCs in controlling decisions about face familiarity. Priming modulates the pathway FGMTLPFC differently in the two hemispheres, strengthening the involvement of MTL in the left hemisphere and weakening in the right hemisphere. This indicates proficiency of the left and right MTL in processing different aspects of facial information. Further, a backward connection ATLPFC in the left hemisphere was found to be functionally relevant for both conditions in speeding up response time in individual subjects, reinforcing the role of PFC in executive functioning and ATL in naming of famous faces. Thus, an integrated framework of source localization and DCM with RERPs allows a novel, comprehensive understanding of time resolved dynamics in face recognition and priming, thereby piloting prospects of its application to other experimental paradigms.