Autoregressive Modeling and Feature Analysis of DNA Sequences

<p/> <p>A parametric signal processing approach for DNA sequence analysis based on autoregressive (AR) modeling is presented. AR model residual errors and AR model parameters are used as features. The AR residual error analysis indicate a high specificity of coding DNA sequences, while AR feature-based analysis helps distinguish between coding and noncoding DNA sequences. An AR model-based string searching algorithm is also proposed. The effect of several types of numerical mapping rules in th proposed method is demonstrated.</p

Homeostasis of brain dynamics in epilepsy: a feedback control systems perspective of seizures

In an effort to understand basic functional mechanisms that can produce epileptic seizures, some key features are introduced in coupled lumped-parameter neural population models that produce seizure -like events and dynamics similar to the ones during the route of the epileptic brain towards seizures. In these models, modified from existing ones in the literature, internal feedback mechanisms are incorporated to maintain the normal low level of synchronous behavior in the presence of coupling variations. While the internal feedback is developed using basic feedback systems principles, it is also functionally equivalent to actual neurophysiological mechanisms such as homeostasis that act to maintain normal activity in neural systems that are subject to extrinsic and intrinsic perturbations. Here it is hypothesized that a plausible cause of seizures is a pathology in the internal feedback action; normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to seizure -like high amplitude oscillations. Several external seizure-control paradigms, that act to achieve the operational objective of maintaining normal levels of synchronous behavior, are also developed and tested in this paper. In particular, closed-loop modulating control with predefined stimuli, and closed-loop feedback decoupling control are considered. Among these, feedback decoupling control is the consistently successful and robust seizure-control strategy. The proposed model and remedies are consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology. The results from the analysis of these models have two key implications, namely, developing a basic theory for epilepsy and other brain disorders, and the development of a robust seizure-control device through electrical stimulation and/or drug intervention modalities

Controlling synchronization in a neuron-level population model

We have studied coupled neural populations in an effort to understand basic mechanisms that maintain their normal synchronization level despite changes in the inter-population coupling levels. Towards this goal, we have incorporated coupling and internal feedback structures in a neuron-level population model from the literature. We study two forms of internal feedback--regulation of excitation, and compensation of excessive excitation with inhibition. We show that normal feedback actions quickly regulate/compensate an abnormally high coupling between the neural populations, whereas a pathology in these feedback actions can lead to abnormal synchronization and seizure -like high amplitude oscillations. We then develop an external control paradigm, termed feedback decoupling, as a robust synchronization control strategy. The external feedback decoupling controller acts to achieve the operational objective of maintaining normal-level synchronous behavior irrespective of the pathology in the internal feedback mechanisms. Results from such an analysis have an interesting physical interpretation and specific implications for the treatment of diseases such as epilepsy. The proposed remedy is consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology

Measuring resetting of brain dynamics at epileptic seizures: application of global optimization and spatial synchronization techniques

Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL(max)), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion\u27s sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL(max), a measure of phase (ϕ(max)) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is shown that using either of the two resetting criteria, and for all three dynamical measures, dynamical resetting at seizures occurs with a significantly higher probability (α = 0.05) than resetting at randomly selected non-seizure points in days of EEG recordings per patient. It is also shown that dynamical resetting at seizures using time constants of STL(max) synchronization/desynchronization occurs with a higher probability than using the other synchronization measures, whereas dynamical resetting at seizures using the level of synchronization/desynchronization criterion is detected with similar probability using any of the three measures of synchronization. These findings show the robustness of seizure resetting with respect to measures of EEG dynamics and criteria of resetting utilized, and the critical role it might play in further elucidation of ictogenesis, as well as in the development of novel treatments for epilepsy