Group A streptococcal disorders can result from autoantibodies generated against M proteins. These autoantibodies cross react with the basal ganglia resulting in movement disorders. Previously, we demonstrated binding of streptococcal mAb10F5, with CPu and phospholipids. To determine if mAb10F5 binding to basal ganglia and phospholipids is due to virulence of the antibody or antibody subtype, rats were injected with control IgG2 antibodies and euthanized after 24, 48, or 72 hours. Brains were harvested and immunofluorescence was used to analyze brain slices. Control IgG2 rats showed significantly less fluorescence in the CPu than mAb10F5 injected rats at every time point. These findings reaffirm 10F5 is an anti-basal ganglia antibody. To evaluate mechanism of antibody entry, mAb10F5 was examined for anti-phospholipid activity. MAb10F5 displayed greater affinity to phospholipids when compared to IgG2 controls. Our findings support mAb10F5 is an anti-basal ganglia and anti-phospholipid antibody due to its own virulence. / Access to thesis permanently restricted to Ball State community only / Department of Physiology and Health Science

PhD / When modeling a system as complex as the brain, considerable simplifications are inevitable. The nature of these simplifications depends on the available experimental evidence, and the desired form of model predictions. A focus on the former often inspires models of networks of individual neurons, since properties of single cells are more easily measured than those of entire populations. However, if the goal is to describe the processes responsible for the electroencephalogram (EEG), such models can become unmanageable due to the large numbers of neurons involved. Mean-field models in which assemblies of neurons are represented by their average properties allow activity underlying the EEG to be captured in a tractable manner. The starting point of the results presented here is a recent physiologically-based mean-field model of the corticothalamic system, which includes populations of excitatory and inhibitory cortical neurons, and an excitatory population representing the thalamic relay nuclei, reciprocally connected with the cortex and the inhibitory thalamic reticular nucleus. The average firing rates of these populations depend nonlinearly on their membrane potentials, which are determined by afferent inputs after axonal propagation and dendritic and synaptic delays. It has been found that neuronal activity spreads in an approximately wavelike fashion across the cortex, which is modeled as a two-dimensional surface. On the basis of the literature, the EEG signal is assumed to be roughly proportional to the activity of cortical excitatory neurons, allowing physiological parameters to be extracted by inverse modeling of empirical EEG spectra. One objective of the present work is to characterize the statistical distributions of fitted model parameters in the healthy population. Variability of model parameters within and between individuals is assessed over time scales of minutes to more than a year, and compared with the variability of classical quantitative EEG (qEEG) parameters. These parameters are generally not normally distributed, and transformations toward the normal distribution are often used to facilitate statistical analysis. However, no single optimal transformation exists to render data distributions approximately normal. A uniformly applicable solution that not only yields data following the normal distribution as closely as possible, but also increases test-retest reliability, is described in Chapter 2. Specialized versions of this transformation have been known for some time in the statistical literature, but it has not previously found its way to the empirical sciences. Chapter 3 contains the study of intra-individual and inter-individual variability in model parameters, also providing a comparison of test-retest reliability with that of commonly used EEG spectral measures such as band powers and the frequency of the alpha peak. It is found that the combined model parameters provide a reliable characterization of an individual's EEG spectrum, where some parameters are more informative than others. Classical quantitative EEG measures are found to be somewhat more reproducible than model parameters. However, the latter have the advantage of providing direct connections with the underlying physiology. In addition, model parameters are complementary to classical measures in that they capture more information about spectral structure. Another conclusion from this work was that a few minutes of alert eyes-closed EEG already contain most of the individual variability likely to occur in this state on the scale of years. In Chapter 4, age trends in model parameters are investigated for a large sample of healthy subjects aged 6-86 years. Sex differences in parameter distributions and trends are considered in three age ranges, and related to the relevant literature. We also look at changes in inter-individual variance across age, and find that subjects are in many respects maximally different around adolescence. This study forms the basis for prospective comparisons with age trends in evoked response potentials (ERPs) and alpha peak morphology, besides providing a standard for the assessment of clinical data. It is the first study to report physiologically-based parameters for such a large sample of EEG data. The second main thrust of this work is toward incorporating the thalamocortical system and the basal ganglia in a unified framework. The basal ganglia are a group of gray matter structures reciprocally connected with the thalamus and cortex, both significantly influencing, and influenced by, their activity. Abnormalities in the basal ganglia are associated with various disorders, including schizophrenia, Huntington's disease, and Parkinson's disease. A model of the basal ganglia-thalamocortical system is presented in Chapter 5, and used to investigate changes in average firing rates often measured in parkinsonian patients and animal models of Parkinson's disease. Modeling results support the hypothesis that two pathways through the basal ganglia (the so-called direct and indirect pathways) are differentially affected by the dopamine depletion that is the hallmark of Parkinson's disease. However, alterations in other components of the system are also suggested by matching model predictions to experimental data. The dynamics of the model are explored in detail in Chapter 6. Electrophysiological aspects of Parkinson's disease include frequency reduction of the alpha peak, increased relative power at lower frequencies, and abnormal synchronized fluctuations in firing rates. It is shown that the same parameter variations that reproduce realistic changes in mean firing rates can also account for EEG frequency reduction by increasing the strength of the indirect pathway, which exerts an inhibitory effect on the cortex. Furthermore, even more strongly connected subcircuits in the indirect pathway can sustain limit cycle oscillations around 5 Hz, in accord with oscillations at this frequency often observed in tremulous patients. Additionally, oscillations around 20 Hz that are normally present in corticothalamic circuits can spread to the basal ganglia when both corticothalamic and indirect circuits have large gains. The model also accounts for changes in the responsiveness of the components of the basal ganglia-thalamocortical system, and increased synchronization upon dopamine depletion, which plausibly reflect the loss of specificity of neuronal signaling pathways in the parkinsonian basal ganglia. Thus, a parsimonious explanation is provided for many electrophysiological correlates of Parkinson's disease using a single set of parameter changes with respect to the healthy state. Overall, we conclude that mean-field models of brain electrophysiology possess a versatility that allows them to be usefully applied in a variety of scenarios. Such models allow information about underlying physiology to be extracted from the experimental EEG, complementing traditional measures that may be more statistically robust but do not provide a direct link with physiology. Furthermore, there is ample opportunity for future developments, extending the basic model to encompass different neuronal systems, connections, and mechanisms. The basal ganglia are an important addition, not only leading to unified explanations for many hitherto disparate phenomena, but also contributing to the validation of this form of modeling.

The aim of this thesis was to investigate the neural underpinnings of persistent developmental stuttering. We explored neural systems important for speech-motor integration and focused on subcortical control systems: the basal ganglia and cerebellum. A secondary aim of this work was to distinguish effects related to general traits of the disorder from those reflecting specific states of stuttered speech. To address these aims we used a variety of neuroimaging methodologies as well as an extensive neuropsychological and empirical test battery. Our examination of neural pathway microstructure using diffusion-tensor imaging replicated previous findings of widespread disorganisation of white matter in people who stutter. This disruption included all major white matter pathways leading in and out of the cerebellum. In our second, third, and fourth studies we examined functional activity at rest and during different types of speech. The brain networks used by people who stutter and controls largely overlapped. The brain regions that distinguished general traits and specific states of stuttering were somewhat task-specific. Subcortical activation in the basal ganglia and cerebellum was related to the frequency of dysfluent speech in the scanner. In our final study we examined performance on a variety of classical tasks of motor learning. We observed evidence of delayed learning in response to changes in environmental feedback in the stuttering group relative to controls. Within people who stutter, subgroups who differ according to heritability of the disorder may also differ in the balance of dopamine in the basal ganglia. Overall, we concluded that cerebellar alterations contribute to the general trait of stuttering, while basal ganglia disruption may reflect specific effects within stuttering. Our work supports a broader role of the subcortical system in speech production, generally.

The motor symptoms of Parkinson's disease (PD) have been linked to the emergence of exaggerated oscillatory activity in the 13 - 35 Hz beta range in recordings of the basal ganglia (BG) thalamocortical circuit of PD patients and animal models. PD patients and animal models also express dopamine-dependent cognitive impairments, implying effects of dopamine loss on the function of the anterior cingulate cortex (ACC). This thesis examines the electrophysiological behavior of the BG thalamocortical circuit in PD and dopamine-normal states during cognitive and motor activity. In vivo recordings in the BG of PD and dystonic patients were used to study the influence of dopamine during a test of executive function. Normal executive function was also investigated in the dopamine-healthy ACC of chronic pain patients. Both the BG and ACC exhibited lateralized electrophysiological responses to feedback valence. The BG also exhibited dopamine-sensitive event-related behavior. In additional experiments, chronically implanted recording electrodes in awake, behaving hemiparkinsonian rats were used to examine the transmission of synchronized oscillatory activity from the BG, through the ventral medial (VM) thalamus, to the ACC. Modulation of subthalamic nucleus, VM thalamus, and ACC activity during a simple cognitive/movement task was also investigated in hemiparkinsonian rats. Findings in the rat model suggest that ACC-mediated executive function is dopamine-sensitive and is reflected in the region's electrophysiology. These results may provide further insight into the significance of excessive oscillatory activity in PD and its influence on cognitive systems.

Les ganglions de la base (GB) sont des structures cérébrales profondes participant à la sélection de comportements adaptés, avec ses composantes motrices, cognitives et émotionnelles. L’étude par IRMf BOLD de ces structures présente un grand intérêt pour explorer leur rôle et leur dysfonctionnement dans certaines pathologies, comme la maladie de Parkinson (MP). Cette technique permet, par l’étude du signal BOLD, de mettre en évidence des activations cérébrales suite à une activation neuronale. Or l’IRMf BOLD a été optimisée pour l’étude des activations corticales, et la mise en évidence d’activations dans les GB est difficile, surtout au niveau individuel. Ceci est en parti lié au fait que le signal BOLD est plus faible dans ces structures par rapport au cortex. Plusieurs raisons peuvent expliquer ce faible signal BOLD. Ainsi la charge en fer de ses structures, modifiant le paramètre de relaxation T 2 *, peut en être une des causes. En effet, la sensibilité de mesure du signal BOLD est optimale lorsque le temps d’écho (TE) de la séquence d’acquisition égale le T 2 * de la structure cérébrale d’intérêt. Notre premier travail a consisté à étudier l’hétérogénéité du T 2 * dans différentes structures cérébrales en tenant compte des effets de la MP, pathologie connue pour entrainer des accumulations de fer dans certaines régions. Nous avons par ailleurs étudié l’évolution du T 2 * de manière longitudinale, et ce paramètre est apparu comme un biomarqueur intéressant de l’évolutivité de la MP. Le deuxième travail a été consacré à étudier les activations des GB en tenant compte de l’hétérogénéité du T 2 *. Nous avons étudié les activations cérébrales suite à la réalisation d’une tâche motrice, en explorant entre autres l’effet TE. Nous avons montré que le choix du TE a finalement peu d’impact sur la capacité de détection des activations au niveau des GB. Nous proposons une stratégie pour l’étude individuelle de l’activité cérébrale au niveau des GB en utilisant le pourcentage de changement du signal BOLD dans des régions cérébrales d’intérêt préalablement définies sur l’analyse de groupe. / The basal ganglia (BG) are deep brain structures involved in the selection of appropriate behavior, with motor, cognitive and emotional components. The BOLD fMRI study of these structures is of great interest to explore their role and dysfunction in certain diseases, such as Parkinson's disease (PD). By studying the BOLD signal, this technique allows to identify brain activation following neuronal activation. However BOLD fMRI has been optimized for the study of cortical activations and detection of activations in the BG is difficult, mainly at the individual level. This is partly due to the fact that the BOLD signal is lower in these structures in relation to the cortex. Several reasons may explain the BOLD signal attenuation. Thereby, iron load in its structures, which changes the relaxation parameter T 2 *, may be a cause. Indeed, the BOLD signal is optimal when the echo time (TE) of the MRI acquisition sequence equal T 2 * of the considered brain structure. Our first work was to study the heterogeneity of T 2 * in different brain structures, taking into account the effects of PD. Indeed, PD is known to induce iron accumulation some regions. We also studied the evolution of T 2 * longitudinally, and this parameter has emerged as an interesting biomarker to track PD progression. The second work was to study the activation of BG taking into account T 2 * heterogeneity. We studied brain activation during a motor task, exploring in particular the effect of TE. We showed that the choice of TE has a low impact on BG activation detection sensitivity. We propose a strategy for individual quantification of neuronal activity in the BG, using the BOLD percentage signal change in pre-defined regions of interest, obtained from the group analysis.

abstract: The basal ganglia are four sub-cortical nuclei associated with motor control and reward learning. They are part of numerous larger mostly segregated loops where the basal ganglia receive inputs from specific regions of cortex. Converging on these inputs are dopaminergic neurons that alter their firing based on received and/or predicted rewarding outcomes of a behavior. The basal ganglia's output feeds through the thalamus back to the areas of the cortex where the loop originated. Understanding the dynamic interactions between the various parts of these loops is critical to understanding the basal ganglia's role in motor control and reward based learning. This work developed several experimental techniques that can be applied to further study basal ganglia function. The first technique used micro-volume injections of low concentration muscimol to decrease the firing rates of recorded neurons in a limited area of cortex in rats. Afterwards, an artificial cerebrospinal fluid flush was injected to rapidly eliminate the muscimol's effects. This technique was able to contain the effects of muscimol to approximately a 1 mm radius volume and limited the duration of the drug effect to less than one hour. This technique could be used to temporarily perturb a small portion of the loops involving the basal ganglia and then observe how these effects propagate in other connected regions. The second part applied self-organizing maps (SOM) to find temporal patterns in neural firing rate that are independent of behavior. The distribution of detected patterns frequency on these maps can then be used to determine if changes in neural activity are occurring over time. The final technique focused on the role of the basal ganglia in reward learning. A new conditioning technique was created to increase the occurrence of selected patterns of neural activity without utilizing any external reward or behavior. A pattern of neural activity in the cortex of rats was selected using an SOM. The pattern was then reinforced by being paired with electrical stimulation of the medial forebrain bundle triggering dopamine release in the basal ganglia. Ultimately, this technique proved unsuccessful possibly due to poor selection of the patterns being reinforced. / Dissertation/Thesis / Ph.D. Bioengineering 2014

Le rôle du noyaux de la base dans le contrôle moteur reste une question ouverte. Nous avons conçu une tâche motrice contrôlée dite « start-stop » chez la souris, permettant de quantifier les paramètres cinématiques associés à l'action. Les enregistrements extracellulaires ont démontré que notre tâche recrute 8 classes différentes de neurones dans le striatum. Le groupe « quoi » était composé des neurones « Beginning » et « End », activés au début ou à la fin de la séquence motrice, respectivement. Le groupe « comment » était composé des neurones « Running », « Onset » et « Offset » qui représentaient l'ensemble de l'exécution de la séquence motrice et étaient corrélées aux performances de vitesse. Nous avons aussi signalé la présence de les cellules « Immobility » activées pendant l'immobilité. La comparaison des performances au cours d’essais réussis par rapport à des mouvements spontanés a montré que la fraction des différentes classes de neurones striataux reste stable. Cependant, la fraction de cellules modulant leur activité en fonction de la séquence était fortement diminuée dans les mouvements spontanés par rapport aux essais réussis, et l'activité des cellules « Running » étaient moins corrélées avec la vitesse et la durée de la course de l'animal. Nos données sont cohérentes avec un modèle de fonctionnement des ganglions de la base dans lequel l'activité striatale est organisée d'une manière temporellement précise pour initier, maintenir et mettre fin à l'activité des séquences motrices. Des tests d'optogénétique en cours permettront de confirmer les relations causales supposées entre l'activité striatale et la mise en œuvre de mouvements spécifiques. / The precise role of the basal ganglia in the control of motor actions is still under debate. We designed a well-controlled start-stop running paradigm for mice, that enabled to quantify the kinematic parameters associated with motor execution. Extracellular recordings demonstrated that our task massively recruited 8 different functional striatal classes that modulated their activity at distinct phases of the sequential action and immobility. The “what” group comprised Beginning and End neurons which were specifically tuned at the beginning or at the end of the motor sequence, respectively. The “how” group was the most representative and comprised Running, Onset and Offset cells which, cooperatively represented the entire execution of the motor sequence and were well correlated with speed performance. Strikingly, we also reported the presence of the Immobility cells that specifically fired during immobility periods. Comparison of performances during good trials versus spontaneous runs showed that the fraction of the different striatal classes remained stable. However, the fraction of cells with significant sequence-related modulation was prominently decreased in spontaneous compared to good trial runs, and the activity of the Running cells was less correlated with animal's running speed and duration. Our data are consistent with a basal ganglia model where striatal activity is coordinately organized in a temporally precise manner to initiate, maintain and terminate motor sequences. Ongoing experiments with optogenetics techniques (developed during this thesis) will confirm inferred causal relationships of striatal activity to specific motor implementations.

Les ganglions de la base (BG) sont un ensemble de noyaux qui contrôle des taches fondamentales de la vie quotidienne, notamment le control des mouvements, ainsi que l’apprentissage et le reward. En particulier, le striatum est le noyau principal des BG et le majeur relais d’input. Il est formé par deux sous-types de neurones de projection (SPN) qui modulent l’activité de sortie des BG directement (dSPN) ou indirectement (iSPN) via d’autres structures. Les deux populations sont intermelangés, ce qui permet l’activation parallèle des deux voies. Une perte d’équilibre entre l’activité des dSPN et des iSPN est partie de l’étiologie de plusieurs neuropathies des BG, y compris la maladie de Parkinson et celle de Huntington. Malgré l’importance fonctionnelle de ces neurones, on a une connaissance très incomplète de comment les deux sous-types sont spécifiés au cours du développement; de plus, la question de comment les deux sous-types se mélangent pour former l’architecture fonctionnelle du striatum reste à élucider. Utilisant une combinaison unique d’outils génétiques disponible dans la souris, j’ai montré que les dSPN et iSPN sont spécifiés dés très tôt et diffèrent dans leur distribution dans le striatum embryonnaire pour s’intermélanger progressivement. De plus, je montre que ce processus de mélange repose sur l’expression du facteur de transcription Ebf1, un gène qui est exprimé spécifiquement dans le dSPN et contrôle aussi l’intégration de ces derniers dans les circuits des BG. Mes résultats fournissent un nouveau contexte pour investiguer les mécanismes moléculaires qui contrôlent l’assemblage du striatum et donnent des informations essentielles pour la génération de neurones striataux in vitro. Une autre population des BG, les neurones du corridor, ont la même origine que les SPN; cependant, au lieu de migrer vers le striatum, ces neurones forment une structure provisoire qui est cruciale pour former la capsule interne, un des majeurs faisceaux d’axones dans le cerveau des mammifères. Malgré leur importance pour le développement de la connectivité cérébrale, on ne sait pas si ces neurones jouent aussi un rôle dans le cerveaux adulte. À travers une combinaison de fate mapping génétique et d’analyse moléculaire à différent stades du développement, je montre que ces neurones contribuent à des noyaux spécifiques de l’amygdale étendue, une structure impliquée dans le control de la peur et de l’anxiété. Ces résultats montrent que les neurones du corridor pourraient contribuer à la régulation de l’anxiété et améliorent notre connaissance sur la formation de ces structures, qui sont très conservés au cours de l’évolution et qui ont un grand intérêt pathologique. Pris dans l’ensemble, mes résultats fournissent non seulement des nouvelles et très importantes informations sur la façon dont les circuits des BG sont formés, mais déterminent un nouveau cadre conceptuel pour investiguer le développement et la connectivité du cerveau antérieur. / Basal ganglia (BG) are a set of brain nuclei that control crucial aspects of everyday life such as motor control, habit learning and reward. In particular, the striatum is the biggest nucleus and input station of BG. It is formed by two subsets of projection neurons (SPN) that modulate BG output activity either directly (dSPN) or indirectly via other BG structures (iSPN). The two populations are intermixed, allowing parallel activation of the two pathways. Impaired balance of dSPN and iSPN activity is part of the aetiology of many BG neuropathies, including Parkinson’s and Huntington’s diseases; however, to date we have poor knowledge on how the two subtypes are specified and how they intermix during development. Using a unique combination of mouse genetic tools, here I show that dSPN and iSPN are specified early as independent populations, have different early distribution and gradually intermix. Moreover, I show that the process of intermix relies on expression of transcription factor Ebf1 in dSPN, a gene that also controls dSPN ability to integrate in BG circuits. These findings provide a new framework to investigate the molecular mechanisms controlling striatal mosaic assembly and will provide instrumental to generate fully formed striatal neurons in vitro. Another BG population, corridor neurons, shares common origin with SPN; however, instead of migrating toward the striatum, these cells form a transient corridor (Co) that is crucial for the formation of the internal capsule, a major axonal pathway in mammals. Despite their importance for brain wiring, whether Co cells also play a role in the adult brain is unknown. Through a combination of genetic fate map and in vivo timecourse, I surprisingly show that these cells participate to specific nuclei of the central extended amygdala, a structure implicated in anxiety and fear response. This finding indicates that Co neurons might contribute to anxiety regulation and sheds new light on the formation of evolutionarily conserved structures of great behavioral and clinical interest. Taken together, my findings not only provide new and critical information on neuronal migration and circuit formation in the BG, but also a new conceptual framework to investigate the formation of nuclear structures of the anterior brain.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第22297号 / 理博第4611号 / 新制||理||1661(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 高田 昌彦, 教授 中村 克樹, 教授 濱田 穣 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

In Parkinson's disease (PD), large parts of the brain transition into states of enhanced neural synchronization. These phase transitions have been associated with the death of dopaminergic neurons as well as with impaired motor function. In this thesis, we address the much-debated question of how parkinsonian synchronization depends on dopamine depletion in the basal ganglia (BG). To this end, we develop spiking neural network (SNN) models of BG circuits and study them via bifurcation analysis. First, we derive mean-field models that allow to account for various forms of short-term plasticity in SNNs. We show that such short-term plasticity mechanisms can lead to highly synchronous, periodic bursting dynamics and discuss the relevance of this bursting regime for PD. Second, we find that the external pallidum, an important part of the BG, cannot cause parkinsonian oscillations autonomously. However, our results suggest that the external pallidum may contribute to the emergence of cross-frequency coupling that has been reported for parkinsonian oscillations. Finally, we describe an open-source Python toolbox that we developed to implement and analyze mean-field models of neural dynamics. Together, this thesis provides insight into BG synchronization processes as well as the mathematical basis and software for future studies of neural synchronization.:1 Introduction 1.1 A complex systems perspective of the brain 1.2 Brain function and the phase transition to synchronized neural activity 1.3 Low-dimensional manifolds of synchronized neural activity 1.4 Phase transitions to synchronized neural activity in Parkinson’s disease 1.5 Thesis overview 2 Mathematical Models and Methods 2.1 A non-linear oscillator model of neural activity 2.2 Dynamical systems methods for the study of neural network models 2.3 Dynamics of a single QIF neuron 3 Low-Dimensional Dynamics in Spiking Neural Networks 3.1 Mean-field approaches in neuroscience 3.2 Dynamics of QIF networks with post-synaptic STP 3.3 Dynamics of QIF networks with spike-frequency adaptation 3.4 Mean-field dynamics of QIF networks with pre-synaptic STP 3.5 Discussion 4 Phase Transitions and Neural Synchronization in the External Pallidum 4.1 A new perspective on GPe structure and function 4.2 GPe model definition and analysis 4.3 Phase transitions in the GPe under static and periodic input 4.4 Discussion 5. Modeling of Neural Mean-Field Dynamics Via PyRates 5.1 Computational modeling in neuroscience 5.2 The Framework 5.3 Pre-implemented methods for neural modeling workflows 5.4 Results 5.5 Discussion 6. Conclusion and Outlook