In this thesis mathematical techniques and models are applied to electroencephalographic (EEG) recordings to study mechanisms of idiopathic generalised epilepsy (IGE). First, we compare network structures derived from resting-state EEG from people with IGE, their unaffected relatives, and healthy controls. Next, these static networks are combined with a dynamical model describing the ac- tivity of a cortical region as a population of phase-oscillators. We then examine the potential of the differences found in the static networks and the emergent properties of the dynamic network as individual biomarkers of IGE. The emphasis of this approach is on discerning the potential of these markers at the level of an indi- vidual subject rather than their ability to identify differences at a group level. Finally, we extend a dynamic model of seizure onset to investigate how epileptiform discharges vary over the course of the day in ambulatory EEG recordings from people with IGE. By per- turbing the dynamics describing the excitability of the system, we demonstrate the model can reproduce discharge distributions on an individual level which are shown to express a circadian tone. The emphasis of the model approach is on understanding how changes in excitability within brain regions, modulated by sleep, metabolism, endocrine axes, or anti-epileptic drugs (AEDs), can drive the emer- gence of epileptiform activity in large-scale brain networks. Our results demonstrate that studying EEG recordings from peo- ple with IGE can lead to new mechanistic insight on the idiopathic nature of IGE, and may eventually lead to clinical applications. We show that biomarkers derived from dynamic network models perform significantly better as classifiers than biomarkers based on static network properties. Hence, our results provide additional ev- idence that the interplay between the dynamics of specific brain re- gions, and the network topology governing the interactions between these regions, is crucial in the generation of emergent epileptiform activity. Pathological activity may emerge due to abnormalities in either of those factors, or a combination of both, and hence it is essential to develop new techniques to characterise this interplay theoretically and to validate predictions experimentally.

No description available.

<p>This thesis aims to elucidate the mechanisms and response characteristics of neural circuits in the vertebrate brain capable of responding selectively to stimulus duration. The research within focused on, but is not limited to, auditory neurons; however, most of the results extend to other sensory modalities. These neurons are known, appropriately, as duration-tuned neurons (DTNs). Duration-tuned neurons tend to prefer stimulus durations similar to the duration of species-specific vocalizations and have preferred durations ranging from 1 ms up to over 100 ms across species.</p> <p>To study the mechanisms underlying DTNs, biologically inspired computational models were produced to explore previously hypothesized mechanisms of duration tuning. These models support the mechanisms by reproducing the responses of <em>in vivo</em> DTNs and predicting additional <em>in vivo</em> response characteristics. The models demonstrate an inherent flexibility in the mechanisms to extend across a wide range of durations and also reveal subtleties in response profiles that arise from particular model parameters.</p> <p>To quantify the encoding efficiency of <em>in vivo</em> DTNs, information theoretic measures were applied to the responses of 97 DTNs recorded from the auditory midbrain (inferior colliculus) of the big brown bat. Stimulus duration encoding robustness, as measured by stimulus-specific information, tended to align with the stimulus durations that produce the largest responses. In contrast, stimulus durations with the most sensitivity to changes in stimulus duration, as measured with an approximation of the observed Fisher information, tended to be stimulus durations shorter or longer than the duration evoking the largest response. Remarkably, both optimal and non-optional Bayesian decoding methods were able to accurately recover stimulus duration from population responses, including durations that lacked neurons dedicated to best representing that duration. These results suggest that DTNs are excellent at encoding stimulus duration, a feature that has been generally assumed but not previously quantified.</p> / Doctor of Philosophy (PhD)

The proper functioning of nervous systems requires electrical activity to be tightly regulated. Perturbations in the intrinsic properties of neurons, and in excitatory input, are imposed throughout nervous system development as cell morphology and network activity evolve. In mature nervous systems these changes continue as a result of synaptic plasticity and external stimuli. It is therefore likely that homeostatic mechanisms exist to regulate membrane conductances that determine the excitability of individual neurons, and several mechanisms have been characterised to date. This thesis characterises a novel in vitro model for homeostatic control of intrinsic excitability. The principal finding is that cultured hippocampal neurons respond to chronic depolarisation over a period of days by attenuating their response to injected current. This effect was found to depend on the level of depolarisation and the length of treatment, and is accompanied by changes in both active and passive membrane conductances. In addition, the effect is reversible and dependent on L-type calcium channel activity. Using experimental data to parameterise a conductance-based computer model suggests that the changes in conductance properties account for the observed differences in excitability.

Tsunoda et al. (2001) recently studied the nature of object representation in monkey inferotemporal cortex using a combination of optical imaging and extracellular recordings. In particular, they examined IT neuron responses to complex natural objects and "simplified" versions thereof. In that study, in 42% of the cases, optical imaging revealed a decrease in the number of activation patches in IT as stimuli were "simplified". However, in 58% of the cases, "simplification" of the stimuli actually led to the appearance of additional activation patches in IT. Based on these results, the authors propose a scheme in which an object is represented by combinations of active and inactive columns coding for individual features. We examine the patterns of activation caused by the same stimuli as used by Tsunoda et al. in our model of object recognition in cortex (Riesenhuber 99). We find that object-tuned units can show a pattern of appearance and disappearance of features identical to the experiment. Thus, the data of Tsunoda et al. appear to be in quantitative agreement with a simple object-based representation in which an object's identity is coded by its similarities to reference objects. Moreover, the agreement of simulations and experiment suggests that the simplification procedure used by Tsunoda (2001) is not necessarily an accurate method to determine neuronal tuning.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 123-129).

Thesis (M.Sc. (Computing Sc.)) - Simon Fraser University, 2004. / Theses (School of Computing Science) / Simon Fraser University. Also available on the World Wide Web.

Thesis (Ph. D.)--University of North Texas, Dec., 2008. / Title from title page display. Includes bibliographical references.

Thesis (M.A. in Psychology)--Vanderbilt University, Dec. 2007. / Title from title screen. Includes bibliographical references.

Astrocytes are the most abundant type of glial cell in the brain, required to ensure optimal neuronal functioning, neurogenesis, and brain vascular tone. Moreover, they play a crucial role in support of neuronal metabolism. The human brain utilizes around 20% of the energy consumed to ensure its proper function. Glucose, an important energy source for the brain, access the neuropil across the blood-brain barrier (BBB), and then is transported into astrocytes through their perivascular end-feet, where it can be stored as glycogen. Furthermore, lactate can be synthesized through glycogenolysis and then shuttled via monocarboxylate transporters (MCTs) to neurons to fuel their tricarboxylic acid (TCA) cycle. This mechanism is known as astrocyte-neuron lactate shuttle (ANLS) and is involved in learning and memory formation. Aging is associated with a decline of faculties such as memory, motor skills, and sensory perception. These deficits are not thought to be due to a substantial loss of neurons but rather changes at the level of connectivity, morphological changes, and white matter structure. In the present study, we aim to compare the glycogen distribution in layer I of the somatosensory cortex between adult (4 months old) and geriatric mice (24 months old). We carried out the visual analysis using Connectome Explorer, which allows us to explore, in real-time, brain reconstructions at the nanometric-level. Using the computational tool GLAM (Glycogen-derived Lactate Absorption Map), we can infer a probability map of the locations where astrocytic glycogen-derived lactate is most likely accessing the surrounding neurites. We analyzed and compared the probability maps on axons, dendrites, boutons, and spines to make a functional hypothesis about single compartments’ energy consumption. Our results indicate that aging brains have a more glycolytic metabolism, with fewer peaks facing mitochondria, and smaller glycogen granules.