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.

Cellular mechanisms of operant and classical conditioning

The ability to associate a predictive stimulus with a subsequent salient event (i.e., classical conditioning) and the ability to associate an expressed behavior with the consequences (i.e., operant conditioning) allow for a predictive understanding of a changing environment. Although they are operationally distinct, there has been considerable debate whether at some fundamental level classical and operant conditioning are mechanistically distinct or similar. Feeding behavior of Aplysia (i.e., biting) was chosen as the model system and was successfully conditioned with appetitive forms of both operant and classical conditioning. The neuronal circuitry responsible for feeding is well understood and is suitable for cellular analyses, thus providing for a mechanistic comparison between these two forms of associative learning. ^ Neuron B51 is part of the feeding circuitry of Aplysia and is critical for the expression of ingestive behaviors. B51 also is a locus of plasticity following both operant and classical conditioning. Both in vivo and in vitro operant conditioning increased the input resistance and the excitability of B51. No pairing-specific changes in the input resistance were observed following both in vivo and in vitro classical conditioning. However, classical conditioning decreased the excitability of B51. Thus, both operant and classical conditioning modified the threshold level for activation of neuron B51, but in opposite directions, revealing key differences in the cellular mechanisms underlying these two forms of associative learning. ^ Next, the cellular mechanisms underlying operant conditioning were investigated in more detail using a single-cell analogue. The single-cell analogue successfully recapitulated the previous in vivo and in vitro operant conditioning results by increasing the input resistance and the excitability of B51. Both PKA and PKC were necessary for operant conditioning. Dopamine appears to be the transmitter mediating the reinforcement signal in this form of conditioning. A D1 dopamine receptor antibody revealed that the D1receptor localizes to the axon hillock, which is also the region that gives the strongest response when iontophoresing dopamine. ^ The studies presented herein, thus, provide for a greater understanding of the mechanisms underlying both of these forms of associative learning and demonstrate that they likely operate through distinct cellular mechanisms. ^

Blood-oxygen-level-dependent (BOLD) signal changes in total cortex and subcortex, pain, reward, and vigilance regions during mechanical pressure pain after morphine administration

Morphine is the most common clinical choice in the management of severe pain. Although the molecular mechanisms of morphine have already been characterized, the cerebral circuits by which it attenuates the sensation of pain have not yet been studied in humans. The objective of this two-arm (morphine versus placebo), between-subjects study was to examine whether morphine affects pain via pain-related cortical circuits, but also via reward regions that relate to the motivational state, as well as prefrontal regions that relate to vigilance as a result of morphine's sedative effects. Cortical activity was measured by the blood-oxygen-level-dependent (BOLD) signal changes using functional magnetic resonance imaging (fMRI). ^ The novelty of this study is at three levels: (i) to develop a methodology that will assess the average BOLD signal across subjects for the pain, reward, and vigilance cortical systems; (ii) to examine whether the reward and/or sedative effects of morphine are contributing factors to cortical regions associated with the motivational state and vigilance; and (iii) to propose a neuroanatomical model related to the opioid-sensitive effects of reward and sedation as a function of cortical activity related to pain in an effort to assess future analgesics. ^ Consistent with our hypotheses, our findings showed that the decrease in total pain-related volume activated between the post- and the pre-treatment morphine group was about 78%, while the post-treatment placebo group displayed only a 5% decrease when compared to pre-treatment levels of activation. The volume increase in reward regions was 451% in the post-treatment compared to the pre-treatment morphine condition. Finally, the volumetric decrease in vigilance regions was 63% in the posttreatment compared to the pre-treatment morphine condition. ^ These findings imply that changes in the blood flow of the reward and vigilance regions may be contributing factors in producing the analgesic effect under morphine administration. Future studies need to replicate this study in a higher resolution fMRI environment and to assess the proposed neuroanatomical model in patient populations. The necessity of pain research is apparent, since pain cuts across different diseases especially chronic ones, and thus, is recognized as a vital public health developing area. ^