2011 — 2014 |
Prescott, Steven A. |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Biophysical Mechanisms Regulating Synchrony Transfer in Somatosensory Cortex @ University of Pittsburgh At Pittsburgh
DESCRIPTION (provided by applicant): Sensory input can evoke very different percepts depending on how information is processed by the nervous system. Fundamental aspects of that neural processing remain poorly understood. Evidence points to correlation of spiking across neurons as a possible neural coding mechanism, especially in sensory systems. For example, sensory information is most effectively transmitted to the cortex when spiking is synchronized across thalamocortical neurons, and available evidence suggests that synchronous activity continues to be propagated to downstream cortical layers. This transfer of synchrony between pre- and postsynaptic neurons (i.e. synchrony transfer) is crucial, lest the information carried by synchronous spiking be lost. An important yet unresolved issue is how well synchrony is transferred between layers of cortex and, in general, how synchrony transfer is regulated. One thing is clear: sets of neurons transfer synchronous input to their postsynaptic targets only if they themselves respond to synchronous inputs with synchronous spiking. What, then, are the biophysical mechanisms that control spike synchrony across a set of neurons receiving synchronous input? Deciphering the cellular and synaptic bases for synchrony transfer has proven extremely challenging because synchrony is a multi-neuron, network-level phenomenon that is difficult to measure or control using standard experimental techniques. Consequently, the task has fallen to computer modeling. But although modeling has provided valuable insights, the need for experimentation persists. Our solution to this challenge is to embed real neurons in virtual networks by integrating electrophysiology with mathematical modeling. This will enable us to experimentally investigate the biophysical mechanisms regulating synchrony transfer in a slice preparation of rat somatosensory cortex. In brief, we will simulate synaptic connectivity patterns by combining dynamic clamp and mathematical modeling such that individually recorded neurons operate (and will be analyzed) as if they are part of a network propagating synchronous activity. Synchrony transfer will be quantified by comparing output synchrony, calculated by cross-correlation of recorded output spike trains, with input synchrony, specified when constructing our simulated synaptic input. We will use this innovative approach to test our central hypothesis that biophysical mechanisms at the level of single neurons, microcircuits, and synaptic plasticity can enable good synchrony transfer between cortical layers. We have identified spike generation, feedforward inhibition, and spike time dependent plasticity as candidate mechanisms based on theoretical insights derived from our previous work. Relating network-level phenomena like synchrony with their underlying biophysical mechanisms is essential for understanding the neurobiological basis of sensory processing. By combining mathematical modeling with electrophysiology to study real neurons embedded in virtual networks, our proposed study will establish direct links between network-level synchrony and the cellular and synaptic mechanisms regulating synchrony transfer. PUBLIC HEALTH RELEVANCE: Sensory input can evoke very different percepts depending on how the nervous system processes information. Derangements in that processing can lead to chronic perceptual abnormalities such as neuropathic pain, which is characterized by spontaneous painful sensations and hypersensitivity to normally innocuous stimuli. Deciphering the biophysical basis for network-level processing will facilitate translation of molecular breakthroughs into clinically effective treatments for complex, hard-to-treat conditions like neuropathic pain.
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2011 — 2012 |
Prescott, Steven A. |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Computational Investigation of Neuropathic Changes in Primary Afferent Excitabili @ University of Pittsburgh At Pittsburgh
DESCRIPTION (provided by applicant): Neuropathic pain results from damage to or dysfunction of the nervous system. It is a source of incalculable suffering and remains notoriously difficult to treat despite advances in basic research. Ectopic spiking in primary afferents contributes directly to neuropathic pain by driving central sensitization and by providing abnormal sensory input to the CNS. Accordingly, researchers have spent considerable effort trying to understand ectopic spiking, and indeed, much is now known about which ion channels are expressed in different primary afferents and how those channels are altered under neuropathic conditions. However, changes in ion channel expression or properties do not always have straightforward effects on cellular excitability;for example, a single mutation in Nav1.7 channels has been shown to have opposite effects on excitability depending on the other channels present in the cell (Rush et al. 2006;PNAS 103: 8245-50). This illustrates that cellular excitability is an emergent property that depends on the complex interaction between membrane currents. We argue, therefore, that successful development of new analgesics requires an approach that specifically addresses and accounts for the complex ways in which membrane currents interact. Complex (i.e. nonlinear) inter- actions imply that membrane currents compete, cooperate, or interfere with one another. Deciphering those inter- actions requires computational tools that are foreign to pain research. We propose to import tools from dynamical systems theory and, more importantly, to establish the conceptual framework by which to integrate those tools with experimental approaches. We will demonstrate the utility of our integrated approach by using it to explain how patterns of qualitative, injury-induced changes in neuronal excitability (that are clearly linked with neuropathic pain) arise from aberrant nonlinear interactions between quantitatively altered membrane currents. Our approach is a multidisciplinary one that synergistically combines computer modeling, mathematical analysis, and experiments. Top-down modeling will be used to replicate cellular excitability changes in minimal computer models so that dynamical systems analysis can be used to explain excitability changes on the basis of altered nonlinear interactions. Guided by the theoretical knowledge gained through top-down modeling and analysis, bottom-up modeling will be used to identify which injury-induced changes in specific membrane currents are sufficient to explain cellular hyperexcitability patterns. Furthermore, to establish causal links between the changes predicted by top-down and bottom-up modeling, we will conduct dynamic clamp experi- ments in real neurons from naove and nerve-injured animals to determine which molecular (channel) changes are necessary and sufficient to explain hyperexcitability in large diameter dorsal root ganglion (DRG) neurons. In summary, our focus on nonlinear interactions between membrane currents is novel. Our proposed solution for investigating those interactions using computational tools (which have heretofore been missing from pain research) as part of an integrative, multidisciplinary approach is equally innovative and potentially transformative. PUBLIC HEALTH RELEVANCE: Neuropathic pain is a source of incalculable suffering that remains notoriously difficult to treat. Given its prevalence of approximately one in 15 people and its burden on caregivers and the healthcare system, the total costs are enormous. The difficulty developing new analgesics with greater efficacy against neuropathic pain highlights the need for new and innovative lines of basic research, such as computational modeling and dynamical analysis, that will facilitate interpretation of existing data and, ultimately, lead to clinical translation.
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