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>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-102362 |
| Date | January 2012 |
| Creators | Petersson, Marcus |
| Publisher | KTH, Beräkningsbiologi, CB, Stockholm |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-CSC-A, 1653-5723 ; 2012:09 |
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