Cognitive Neuroscience of Schizophrenia: Stochastic Modelling of Cognitive-Process Latencies and Nonlinear Dynamics of Neuro-signals
Abstract
We study known and potential numerical earmarks of schizophrenia through mathematical methods. One known numerical characteristic of schizophrenia is that of prolonged encoding latencies in response to cognitive stimuli. Motivated by the need to explain interaction patterns in 2 x 2 factorial data where one factor is encoding load and the other is diagnostic status, we define a class of general serial mixture models based on the number of encoding subprocesses executed and the speed at which they are executed. Mathematical derivations performed on these models yield closed form expressions for the mean encoding latency and average intertrial variance, which in turn yield expressions for the mean interaction contrast and variance interaction contrast. Different interaction signatures correspond to different members of the model class. A wealth of examples are provided linking various potential physical and neurophysiological encoding mechanisms to members of the model class. We also derive results for a specific subset of the general model class where only the number of subprocesses is allowed to vary over factorial cells. Our development includes a numerical test (verified by theory and simulation methods) to determine if the number of encoding subprocesses varies over trials. Theoretical results are then developed for the case where the speed of encoding subprocesses is allowed to vary. Secondly, by means of an exhaustive literature search and application of contingency tables, we investigate whether a collection of numerical indices, called nonlinear indices or complexity indices, can be utilized to support or refute a conjecture in the literature which states that complexity in EEG recordings tends to be higher in schizophrenia patients than controls with this tendency being dampened (and even inverted) by medication, increasing age, and decreasing symptomatology. Our analysis indicates only weak effects due to age and medication, and suggests that symptomatology may play a greater role. Moreover, we observe a strong ``study effect'' which suggests that laboratory procedures may also play a role. Our systematic review of nonlinear indices does, however, indicate that heart rate variability is reduced in schizophrenia and bipolar disorder.