Channel noise effects on neural synchronization

Synchronization in neural networks is strongly tied to the implementation of cognitive processes, but abnormal neuronal synchronization has been linked to a number of brain disorders such as epilepsy and schizophrenia. Here we examine the effects of channel noise on the synchronization of small Hodgkin-Huxley neuronal networks. The principal feature of a Hodgkin-Huxley neuron is the existence of protein channels that transition between open and closed states with voltage dependent rate constants. The Hodgkin-Huxley model assumes infinitely many channels, so fluctuations in the number of open channels do not affect the voltage. However, real neurons have finitely many channels which lead to fluctuations in the membrane voltage and modify the timing of the spikes, which may in turn lead to large changes in the degree of synchronization. We demonstrate that under mild conditions, neurons in the network reach a steady state synchronization level that depends only on the number of neurons in the network. The channel noise only affects the time it takes to reach the steady state synchronization level.Comment: 7 Figure

Statistics of Tumor Micro-environment

Introduction: Immune cells play a prominent role in keeping tumors suppressed, but how the distribution of these immune cells within a tumor’s microenvironment remains poorly understood. The long-term goal of this project is to study how statistical spatial distributions of different immune cells is associated with clinical outcome. The first objective is developing an algorithm for identifying different types of immune cells. Methods: The data motivating this project includes spatial localization information (x-y coordinates) and expression levels of immune cell CD markers quantified by immunofluorescence immunohistochemistry (IF-IHC) in ~1,500 cases of invasive breast cancer. Using expression levels of CD markers in cancer cells (viewed as background noise), we compute upper nonparametric tolerance limits for CD expression in cancer cells. The stroma cells with CD expression above this tolerance limit are considered to be immune cells of the corresponding CD marker type. Results: We have developed a Python program allowing us to quickly process a dataset of x-y coordinates of various cells that took up IHC stain, and creates a dataset of coordinates that are true immune cells. We have additionally analyzed multiple parameters for the development of a tolerance interval and concluded that a combination of 95%-confidence 99%-content allows for a minuscule chance of including the stroma cells that are not immune while maintaining enough data for analysis. Exploratory analysis of spatial point patterns of identified immune cell populations and their association with progression-free survival is in progress. Discussion: We have developed an algorithm for the identification of different types of immune cells associated with type-specific CD markers quantified using IF-IHC. These tools enable further studies of spatial arrangement of immune cells in the tumor tissue and relating them to clinical outcome

Spatial Metrics of Interaction between CD163-Positive Macrophages and Cancer Cells and Progression-Free Survival in Chemo-Treated Breast Cancer

Tumor-associated macrophages (TAMs) promote progression of breast cancer and other solid malignancies via immunosuppressive, pro-angiogenic and pro-metastatic effects. Tumor-promoting TAMs tend to express M2-like macrophage markers, including CD163. Histopathological assessments suggest that the density of CD163-positive TAMs within the tumor microenvironment is associated with reduced efficacy of chemotherapy and unfavorable prognosis. However, previous analyses have required research-oriented pathologists to visually enumerate CD163+ TAMs, which is both laborious and subjective and hampers clinical implementation. Objective, operator-independent image analysis methods to quantify TAM-associated information are needed. In addition, since M2-like TAMs exert local effects on cancer cells through direct juxtacrine cell-to-cell interactions, paracrine signaling, and metabolic factors, we hypothesized that spatial metrics of adjacency of M2-like TAMs to breast cancer cells will have further information value. Immunofluorescence histo-cytometry of CD163+ TAMs was performed retrospectively on tumor microarrays of 443 cases of invasive breast cancer from patients who subsequently received adjuvant chemotherapy. An objective and automated algorithm was developed to phenotype CD163+ TAMs and calculate their density within the tumor stroma and derive several spatial metrics of interaction with cancer cells. Shorter progression-free survival was associated with a high density of CD163+ TAMs, shorter median cancer-to-CD163+ nearest neighbor distance, and a high number of either directly adjacent CD163+ TAMs (within juxtacrine proximity \u3c12 µm to cancer cells) or communicating CD163+ TAMs (within paracrine communication distance \u3c250 µm to cancer cells) after multivariable adjustment for clinical and pathological risk factors and correction for optimistic bias due to dichotomization

Effects of Channel Noise on Neural Networks

The human brain contains on the order of $10^9$ neurons with each neuron having on the order of $10^4$ synaptic connections with other neurons. Within each neuron, there are protein channels that dictate when ions can flow through them. It is the flow of these ions that is the basis for action potential generation, and these action potentials are the source of neural communication and information. These channels exist in various configurations some of which are conducting (``open'') and some of which are non-conducting (``closed''). Moreover, these channels can stochastically switch between the open and closed states. It is nothing short of remarkable that the brain functions as it does despite the randomness present within each neuron.What role these microscopic fluctuations, herein known as channel noise, have on macroscopic neural network properties is an open area of neuroscience that has generated a great deal of interest in recent years due to the advancement of computational methods. In this thesis, we first introduce the Hodgkin-Huxley model and mathematical equations which incorporate this channel noise in the Hodgkin-Huxley model. We then study the role of channel noise on properties of small neural networks which begins in Chapter 3. The first property we will look at is how channel noise affects the timing of the first action potential after stimulus onset. This property, known as first spike latency, is believed to be a coding mechanism used by neurons to communicate information between stimuli and brain processing. We will then look at the role of channel noise on neural synchronization. Abnormal synchronization has been strongly correlated with a number of neural disorders such as Alzheimer's disease and Parkinson's disease.One area of research in neuroscience that is of fundamental interest is the relationship between neural spiking and cognitive processing. For this thesis, in addition to the small neural network models for first spike latency and synchronization, we will consider a recently developed model for cognition and study the model's behavior when subjected to noise. We will conclude with a brief summary of the results obtained as well as discuss ways to extend the research to larger neural network systems

Spatial Metrics of Interaction between CD163-Positive Macrophages and Cancer Cells and Progression-Free Survival in Chemo-Treated Breast Cancer

Tumor-associated macrophages (TAMs) promote progression of breast cancer and other solid malignancies via immunosuppressive, pro-angiogenic and pro-metastatic effects. Tumor-promoting TAMs tend to express M2-like macrophage markers, including CD163. Histopathological assessments suggest that the density of CD163-positive TAMs within the tumor microenvironment is associated with reduced efficacy of chemotherapy and unfavorable prognosis. However, previous analyses have required research-oriented pathologists to visually enumerate CD163+ TAMs, which is both laborious and subjective and hampers clinical implementation. Objective, operator-independent image analysis methods to quantify TAM-associated information are needed. In addition, since M2-like TAMs exert local effects on cancer cells through direct juxtacrine cell-to-cell interactions, paracrine signaling, and metabolic factors, we hypothesized that spatial metrics of adjacency of M2-like TAMs to breast cancer cells will have further information value. Immunofluorescence histo-cytometry of CD163+ TAMs was performed retrospectively on tumor microarrays of 443 cases of invasive breast cancer from patients who subsequently received adjuvant chemotherapy. An objective and automated algorithm was developed to phenotype CD163+ TAMs and calculate their density within the tumor stroma and derive several spatial metrics of interaction with cancer cells. Shorter progression-free survival was associated with a high density of CD163+ TAMs, shorter median cancer-to-CD163+ nearest neighbor distance, and a high number of either directly adjacent CD163+ TAMs (within juxtacrine proximity <12 μm to cancer cells) or communicating CD163+ TAMs (within paracrine communication distance <250 μm to cancer cells) after multivariable adjustment for clinical and pathological risk factors and correction for optimistic bias due to dichotomization