Cancer-induced bone pain is characterized by moderate to severe ongoing pain that commonly requires the use of opiates. Even when ongoing pain is well controlled, patients can suffer breakthrough pain (BTP), episodic severe pain that "breaks through" the medication. We developed a novel model of cancer-induced BTP using female rats with mammary adenocarcinoma cells sealed within the tibia. We demonstrated previously that rats with bone cancer learn to prefer a context paired with saphenous nerve block to elicit pain relief (i.e., conditioned place preference, CPP), revealing the presence of ongoing pain. Treatment with systemic morphine abolished CPP to saphenous nerve block, demonstrating control of ongoing pain. Here, we show that pairing BTP induced by experimenter-induced movement of the tumor-bearing hindlimb with a context produces conditioned place avoidance (CPA) in rats treated with morphine to control ongoing pain, consistent with clinical observation of BTP. Preventing movement-induced afferent input by saphenous nerve block before, but not after, hindlimb movement blocked movement-induced BTP. Ablation of isolectin B4 (IB4)-binding, but not TRPV1(+), sensory afferents eliminated movement-induced BTP, suggesting that input from IB4-binding fibers mediates BTP. Identification of potential molecular targets specific to this population of fibers may allow for the development of peripherally restricted analgesics that control BTP and improve quality of life in patients with skeletal metastases.

The nervous system, including the brain, is a complex network with billions of complex neurons. Ion channels mediate the electrical signals that neurons use to integrate input and produce appropriate output, and could thus be thought of as key instruments in the neuronal orchestra. In the field of neuroscience we are not only curious about how our brains work, but also strive to characterize and develop treatments for neural disorders, in which the neuronal harmony is distorted. By modulating ion channel activity (pharmacologically or otherwise) it might be possible to effectively restore neuronal harmony in patients with various types of neural (including channelopathic) disorders. However, this exciting strategy is impeded by the gaps in our understanding of ion channels and neurons, so more research is required. Thus, the aim of this thesis is to improve the understanding of how specific ion channel types contribute to shaping neuronal dynamics, and in particular, neuronal integration, excitability and memory. For this purpose I have used computational modeling, an approach which has recently emerged as an excellent tool for understanding dynamically complex neurophysiological phenomena. In the first of two projects leading to this thesis, I studied how neurons in the brain, and in particular their dendritic structures, are able to integrate synaptic inputs arriving at low frequencies, in a behaviorally relevant range of ~8 Hz. Based on recent experimental data on synaptic transient receptor potential channels (TRPC), metabotropic glutamate receptor (mGluR) dynamics and glutamate decay times, I developed a novel model of the ion channel current ITRPC, the importance of which is clear but largely neglected due to an insufficient understanding of its activation mechanisms. We found that ITRPC, which is activated both synaptically (via mGluR) and intrinsically (via Ca2+) and has a long decay time constant (τdecay), is better suited than the classical rapidly decaying currents (IAMPA and INMDA) in supporting low-frequency temporal summation. It was further concluded that τdecay varies with stimulus duration and frequency, is linearly dependent on the maximal glutamate concentration, and might require a pair-pulse protocol to be properly assessed. In a follow-up study I investigated small-amplitude (a few mV) long-lasting (a few seconds) depolarizations in pyramidal neurons of the hippocampal cortex, a brain region important for memory and spatial navigation. In addition to confirming a previous hypothesis that these depolarizations involve an interplay of ITRPC and voltage-gated calcium channels, I showed that they are generated in distal dendrites, are intrinsically stable to weak excitatory and inhibitory synaptic input, and require spatial and temporal summation to occur. I further concluded that the existence of multiple stable states cannot be ruled out, and that, in spite of their small somatic amplitudes, these depolarizations may strongly modulate the probability of action potential generation. In the second project I studied the axonal mechanisms of unmyelinated peripheral (cutaneous) pain-sensing neurons (referred to as C-fiber nociceptors), which are involved in chronic pain. To my knowledge, the C-fiber model we developed for this purpose is unique in at least three ways, since it is multicompartmental, tuned from human microneurography (in vivo) data, and since it includes several biologically realistic ion channels, Na+/K+ concentration dynamics, a Na-K-pump, morphology and temperature dependence. Based on simulations aimed at elucidating the mechanisms underlying two clinically relevant phenomena, activity-dependent slowing (ADS) and recovery cycles (RC), we found an unexpected support for the involvement of intracellular Na+ in ADS and extracellular K+ in RC. We also found that the two major Na+ channels (NaV1.7 and NaV1.8) have opposite effects on RC. Furthermore, I showed that the differences between mechano-sensitive and mechano-insensitive C-fiber types might reside in differing ion channel densities. To conclude, the work of this thesis provides key insights into neuronal mechanisms with relevance for memory, pain and neural disorders, and at the same time demonstrates the advantage of using computational modeling as a tool for understanding and discovering fundamental properties of central and peripheral neurons. / <p>QC 20120914</p>

<p>Signal processing within the neurophysiological field is challenging and requires short processing time and reliable results. In this thesis, three main problems are considered.</p><p>First, a modified line source model for simulation of muscle action potentials (APs) is presented. It is formulated in continuous-time as a convolution of a muscle-fiber dependent transmembrane current and an electrode dependent weighting (impedance) function. In the discretization of the model, the Nyquist criterion is addressed. By applying anti-aliasing filtering, it is possible to decrease the discretization frequency while retaining the accuracy. Finite length muscle fibers are incorporated in the model through a simple transformation of the weighting function. The presented model is suitable for modeling large motor units.</p><p>Second, the possibility of discerning the individual AP components of the concentric needle electromyogram (EMG) is explored. Simulated motor unit APs (MUAPs) are prefiltered using Wiener filtering. The mean fiber concentration (MFC) and jitter are estimated from the prefiltered MUAPs. The results indicate that the assessment of the MFC may well benefit from the presented approach and that the jitter may be estimated from the concentric needle EMG with an accuracy comparable with traditional single fiber EMG.</p><p>Third, automatic, rather than manual, detection and discrimination of recorded C-fiber APs is addressed. The algorithm, detects the Aps reliably using a matched filter. Then, the detected APs are discriminated using multiple hypothesis tracking combined with Kalman filtering which identifies the APs originating from the same C-fiber. To improve the performance, an amplitude estimate is incorporated into the tracking algorithm. Several years of use show that the performance of the algorithm is excellent with minimal need for audit.</p>

Signal processing within the neurophysiological field is challenging and requires short processing time and reliable results. In this thesis, three main problems are considered. First, a modified line source model for simulation of muscle action potentials (APs) is presented. It is formulated in continuous-time as a convolution of a muscle-fiber dependent transmembrane current and an electrode dependent weighting (impedance) function. In the discretization of the model, the Nyquist criterion is addressed. By applying anti-aliasing filtering, it is possible to decrease the discretization frequency while retaining the accuracy. Finite length muscle fibers are incorporated in the model through a simple transformation of the weighting function. The presented model is suitable for modeling large motor units. Second, the possibility of discerning the individual AP components of the concentric needle electromyogram (EMG) is explored. Simulated motor unit APs (MUAPs) are prefiltered using Wiener filtering. The mean fiber concentration (MFC) and jitter are estimated from the prefiltered MUAPs. The results indicate that the assessment of the MFC may well benefit from the presented approach and that the jitter may be estimated from the concentric needle EMG with an accuracy comparable with traditional single fiber EMG. Third, automatic, rather than manual, detection and discrimination of recorded C-fiber APs is addressed. The algorithm, detects the Aps reliably using a matched filter. Then, the detected APs are discriminated using multiple hypothesis tracking combined with Kalman filtering which identifies the APs originating from the same C-fiber. To improve the performance, an amplitude estimate is incorporated into the tracking algorithm. Several years of use show that the performance of the algorithm is excellent with minimal need for audit.