Application of Signal Advance Technology to Electrophysiology

Medical instrumentation used in diagnosis and treatment relies on the accurate detection and processing of various physiological events and signals. While signal detection technology has improved greatly in recent years, there remain inherent delays in signal detection/ processing. These delays may have significant negative clinical consequences during various pathophysiological events. Reducing or eliminating such delays would increase the ability to provide successful early intervention in certain disorders thereby increasing the efficacy of treatment. In recent years, a physical phenomenon referred to as Negative Group Delay (NGD), demonstrated in simple electronic circuits, has been shown to temporally advance the detection of analog waveforms. Specifically, the output is temporally advanced relative to the input, as the time delay through the circuit is negative. The circuit output precedes the complete detection of the input signal. This process is referred to as signal advance (SA) detection. An SA circuit model incorporating NGD was designed, developed and tested. It imparts a constant temporal signal advance over a pre-specified spectral range in which the output is almost identical to the input signal (i.e., it has minimal distortion). Certain human patho-electrophysiological events are good candidates for the application of temporally-advanced waveform detection. SA technology has potential in early arrhythmia and epileptic seizure detection and intervention. Demonstrating reliable and consistent temporally advanced detection of electrophysiological waveforms may enable intervention with a pathological event (much) earlier than previously possible. SA detection could also be used to improve the performance of neural computer interfaces, neurotherapy applications, radiation therapy and imaging. In this study, the performance of a single-stage SA circuit model on a variety of constructed input signals, and human ECGs is investigated. The data obtained is used to quantify and characterize the temporal advances and circuit gain, as well as distortions in the output waveforms relative to their inputs. This project combines elements of physics, engineering, signal processing, statistics and electrophysiology. Its success has important consequences for the development of novel interventional methodologies in cardiology and neurophysiology as well as significant potential in a broader range of both biomedical and non-biomedical areas of application.

How health care delay and avoidance decisions are affected by finances and health insurance

Objective. To identify how an individual's finances and health insurance coverage affects their decision whether to avoid or delay medical care. Methods. Secondary data analysis of The Effects of Financial and Insurance Considerations on Health Care Utilization 2007 telephone survey data. Study inclusion criteria. 18 years old, Harris County resident, and had a need for medical care within the past year. Post weighing was done to correct for non-response bias. Results. Survey decision makers were predominately minorities (60%), Female (70%), and insured (71%). Ninety-two percent of participants sought care when needed, however, of this population 39% delayed medical care. Fifty-six percent of participants who delayed medical care sought care in the Doctor's office. For those who replied "Yes" to considering health insurance and finances in deciding to avoid medical care, 61% stated that they were confused about their insurance coverage as the explanation why. Fifty-five percent of Respondents indicated that delaying medical care was due to not knowing whether medical care was necessary. Conclusion. Additional research needs to be conducted to examine the relationship between onset of medical symptoms and final medical diagnosis to identify whether survey participants who delayed or avoided medical care actions were appropriate responses to their initial medical symptoms and final diagnosis. ^

Predictors of diagnostic delay among tuberculosis patients in a U.S.-Mexico border community

Delays in diagnosis of pulmonary tuberculosis have detrimental effects on the health of the ailing patient as well as the people around him or her. These effects are magnified in highly-travelled parts of the world. Identifying factors predictive of diagnostic delay is challenging, as these vary widely by culture and geography. Predictors of delay for tuberculosis patients living in the Northeastern Mexican city of Matamoros, a binationally-transited area, have yet to be described. Using secondary analysis of a retrospective survey, this study sought to identify predictors of diagnostic delay in a sample of culture-positive tuberculosis patients in Matamoros. Sociodemographic, behavioral, and health-related factors were measured and compared. Using bivariate and step-wise regression analyses at an alpha level of 0.05, the author found the following to be statically significant predictors for this sample (R 2=0.171): prior treatment of diabetes, recurrence of tuberculosis, and having ever used cocaine. A question assessing knowledge of immunocompromised subgroups was also identified as a predictor, although its implications are unclear. Notably, the instrument did not distinguish between patient and health system delay. In summary, more research should be conducted in the Matamoros area in order to fully understand the dynamics of delayed diagnosis and its application to public health practice.^

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance.