Functional and motor neuronal analysis of defensive siphon responses in {\it Aplysia californica}

One of the central goals of neuroscience research is to determine how networks of neurons control and modify behavior. One of the most influential model systems for this kind of analysis is the siphon and gill withdrawal reflex of the marine mollusc A. californica. In response to tactile stimulation, the siphon displays 3 different responses: (1) a posterior pointing and leveling (flaring) of the siphon in response to tail stimulation (the siphon T response), (2) constriction and anterior pointing to head stimulation (the siphon H response) and (3) constriction and withdrawal between the animal's parapodia (the siphon S response). The siphon S response is pseudoconditioned by a noxious tail stimulus to resemble the siphon T response. Behavioral and combined behavioral/intracellular studies were conducted to determine the motor neuronal control of these behaviors and to search for mechanisms of siphon response transformation following pseudoconditioning. The present studies have found that the flaring component of pseudoconditioned siphon S responses occurs during mantle pumping (MP) triggered by noxious tail stimulation. Siphon stimulation also triggers MP, as recorded in neurons of the Interneuron II pattern generator which commands MP. The 4 LF$\rm\sb{SB}$ siphon motor neurons (SMNs) were found necessary and sufficient for the siphon T response, while SMNs RD$\rm\sb S$ and LD$\rm\sb{S1}$ were found necessary and sufficient for the siphon H response. Following pseudoconditioning, there is an increase in the number of evoked spikes to the test stimulus for the LF$\rm\sb{SB}$ cells and a decreased number for RD$\rm\sb S.$ Siphon flaring occurring during the pseudoconditioned response correlates with increased LF$\rm\sb{SB}$ activity during triggered MP cycles. This suggests that psuedoconditioning is in part due to reconfiguration of the motor outputs of the Interneuron II network. These results suggest that these defensive responses are controlled and patterned by a well-defined, finite set of motor neurons and interneurons (Interneuron II) that are dedicated to specific behavioral functions, but also have parallel distributed properties. ^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

DEVELOPMENT OF A BEAM-SPECIFIC PLANNING TARGET VOLUME AND A ROBUST PLAN ANALYSIS TOOL FOR PROTON THERAPY

Proton therapy is growing increasingly popular due to its superior dose characteristics compared to conventional photon therapy. Protons travel a finite range in the patient body and stop, thereby delivering no dose beyond their range. However, because the range of a proton beam is heavily dependent on the tissue density along its beam path, uncertainties in patient setup position and inherent range calculation can degrade thedose distribution significantly. Despite these challenges that are unique to proton therapy, current management of the uncertainties during treatment planning of proton therapy has been similar to that of conventional photon therapy. The goal of this dissertation research was to develop a treatment planning method and a planevaluation method that address proton-specific issues regarding setup and range uncertainties. Treatment plan designing method adapted to proton therapy: Currently, for proton therapy using a scanning beam delivery system, setup uncertainties are largely accounted for by geometrically expanding a clinical target volume (CTV) to a planning target volume (PTV). However, a PTV alone cannot adequately account for range uncertainties coupled to misaligned patient anatomy in the beam path since it does not account for the change in tissue density. In order to remedy this problem, we proposed a beam-specific PTV (bsPTV) that accounts for the change in tissue density along the beam path due to the uncertainties. Our proposed method was successfully implemented, and its superiority over the conventional PTV was shown through a controlled experiment.. Furthermore, we have shown that the bsPTV concept can be incorporated into beam angle optimization for better target coverage and normal tissue sparing for a selected lung cancer patient. Treatment plan evaluation method adapted to proton therapy: The dose-volume histogram of the clinical target volume (CTV) or any other volumes of interest at the time of planning does not represent the most probable dosimetric outcome of a given plan as it does not include the uncertainties mentioned earlier. Currently, the PTV is used as a surrogate of the CTV’s worst case scenario for target dose estimation. However, because proton dose distributions are subject to change under these uncertainties, the validity of the PTV analysis method is questionable. In order to remedy this problem, we proposed the use of statistical parameters to quantify uncertainties on both the dose-volume histogram and dose distribution directly. The robust plan analysis tool was successfully implemented to compute both the expectation value and its standard deviation of dosimetric parameters of a treatment plan under the uncertainties. For 15 lung cancer patients, the proposed method was used to quantify the dosimetric difference between the nominal situation and its expected value under the uncertainties.