Manufacturing Methods for Magnetic Resonance Microscopy Tools with Application to Neuroscience

Magnetresonanztomographie (MR) ist ein unverzichtbares nicht-invases und hochselektives bildgebendes Verfahren in der Medizin. MR Tomographie wird kommerziell in der klinischen Diagnostik und der Forschung für Gehirnkrankheit, z.B. Epilepsie, Alzheimer und Parkinson, angewandt. In den Neurowissenschaften haben sich Kleintiere als biologische Modelle für die grundlegenden Studien zur diesen Gehirnkrankheiten etabliert. MR Methoden sind ein wertvolles Werkzeug um die Morphologie und den Metabolismus von Kleintieren zu untersuchen. Die Modelle für die Untersuchung von Gehirnkrankheiten schließen Zellen/Zellkulturen und organotypische hippocampale Schnittkulturen (OHSC) mit ein. Obwohl die MR Mikroskopie für die Untersuchung von OHSC schon angewandt wurde fehlt eine effektive Plattform für umfangreiche longitudinale Studien an OHSC wie sie in den Neurowissenschaften üblich sind. Zwei Detektorkonzepte für die MR Mikroskopie inklusive ihrer Auslegung, der Herstellung und der Charakterisierung, werden in dieser Arbeit beschrieben. Beide Konzepte basieren auf Herstellungsmethoden welche hohe Fertigungsgenauigkeiten zulassen und in ihrem Herstellungsvolumen skalierbar sind. Hohle solenoide Mikrospulen welche für hochauflösende Untersuchung von Zell und Zellanhäufungen geeignet sind werden eingeführt. Die Herstellung basiert auf dem automatisierten wickeln von Mikrospulen, eine skalierbare und hochpräzise Fertigungsmethode der Mikrotechnologie. Zudem werde induktiv gekoppelte Ober ächenspulen eingeführt. Diese Oberflächenspulen fokussieren den magnetischen Fluss und werden deshalb Lenz Linsen genannt. Die Lenz Linsen werden mit kabelgebundenen und induktiv gekoppelten Spulen verglichen. Ihre Breitband-Fähigkeit machen sie zu einem idealen Kandidaten für die Nutzung in verschiedensten MR Tomographie Systemen. Die Lenz Linsen wurden für den Einsatz in einer MR kompatiblen Inkubationsplattform ausgelegt, welche in dieser Arbeit entwickelt wurde. Der MR Inkubator erweitert die Funktionalität eines MR Tomographen um neurologische Gewebe (z.B. OHSC) über mehrere Stunden andauernde MR Messungen am Leben zu erhalten. Der MR Inkubator erlaubt longitudinale Studien an OHSC und bietet damit eine Plattform für umfangreiche Studien in den Neurowissenschaften. Die Lenz Linsen wurden zusammen mit dem MR Inkubator für MR Mikroskopie Mes- sung von akuten/ xierten hippocampalen Schnitten und OHSC genutzt. Die Resultate dieser MR Mikoskopie Messungen zeigen dass in OHSC die grobe Zytoarchitektur sicht- bar ist, ohne dass die OHSC während der Messungen sterben. Somit ist das eingeführte System bereit für longitudinale Studien an OHSC, welche bereits für die Aufklärung der Epilepsieprogression begonnen wurden

The role of the brain extracellular space in diffusion and cell signalling

134 p.The extracellular space (ECS) is a highly complex space consisting of narrow interconnected channels and reservoirs. The ECS substructures are usually few nanometers wide and consequently, they are very difficult to visualize. In addition, the brain ECS is a very dynamic structure, that changes at different temporal scales. These structural changes can be physiological or they can have a pathological cause. In fact, astrocytic swelling at the expense of the ECS volume is one of the hallmarks of epilepsy. Particularly, we are interested in how ECS volume changes affect GABAergic inhibition, the main source of inhibition in the brain and one of the most studied processes in the onset of epileptogenesis.On the other hand, most intercellular signalling in the brain occurs by diffusion of particles through the ECS channels. Understanding how diffusion is regulated by the fine geometry of the brain neuropil is becoming the focus of interest for researchers. However, progress in this field is limited by the difficulty to access local ECS diffusion with experimental techniques. Recently developed techniques, such as super-resolution shadow imaging (SUSHI), are opening the doors to understand diffusion of molecules through the brain sub-micron ECS structures. In this study, we aim to investigate how the nano-scale ECS geometry of the live brain tissue shapes the diffusion of transmitters and its impact on cellular communication. To attain this goal, we have developed a novel computational model, based on SUSHI images

Gap Junctions and Epileptic Seizures – Two Sides of the Same Coin?

Electrical synapses (gap junctions) play a pivotal role in the synchronization of neuronal ensembles which also makes them likely agonists of pathological brain activity. Although large body of experimental data and theoretical considerations indicate that coupling neurons by electrical synapses promotes synchronous activity (and thus is potentially epileptogenic), some recent evidence questions the hypothesis of gap junctions being among purely epileptogenic factors. In particular, an expression of inter-neuronal gap junctions is often found to be higher after the experimentally induced seizures than before. Here we used a computational modeling approach to address the role of neuronal gap junctions in shaping the stability of a network to perturbations that are often associated with the onset of epileptic seizures. We show that under some circumstances, the addition of gap junctions can increase the dynamical stability of a network and thus suppress the collective electrical activity associated with seizures. This implies that the experimentally observed post-seizure additions of gap junctions could serve to prevent further escalations, suggesting furthermore that they are a consequence of an adaptive response of the neuronal network to the pathological activity. However, if the seizures are strong and persistent, our model predicts the existence of a critical tipping point after which additional gap junctions no longer suppress but strongly facilitate the escalation of epileptic seizures. Our results thus reveal a complex role of electrical coupling in relation to epileptiform events. Which dynamic scenario (seizure suppression or seizure escalation) is ultimately adopted by the network depends critically on the strength and duration of seizures, in turn emphasizing the importance of temporal and causal aspects when linking gap junctions with epilepsy

On application of dynamical system methods in biomedical engineering

The spectrum of various methods and tools used for solving bioengineering problems is sufficiently wide. Dynamical systems (including the symbolic ones) in many cases become a base for design and implementation of methods of investigation and computer modeling complex processes. Whereas for solving direct problems we have many well-developed methods, results for inverse problems are much more modest. We discuss two methods for such tasks: Takens’ method for reconstruction attractor by a time series, and the based on ideas of symbolic dynamics method for digital image analysis using stationary flow on graph and weighted entropy. The results of numerical experiments are given

Computational Modeling of Seizure Dynamics Using Coupled Neuronal Networks: Factors Shaping Epileptiform Activity

International audienceEpileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology

MRI investigations into the pilocarpine model of status epilepticus.

Status epilepticus (SE) is a medical neurological emergency and may cause brain injury associated with epilepsy and cognitive decline. Evidence suggests that the hippocampus is particularly vulnerable to injury from SE and that the resulting hippocampal injury has been hypothesised to continue to evolve on to mesial temporal sclerosis associated temporal lobe epilepsy (MTS-TLE). This form of epilepsy is particularly difficult to treat with current medical therapies, and thus it is the most common epilepsy that requires surgical intervention. Therefore understanding the relationships between SE, hippocampal injury and MTS-TLE may provide the basis for development of novel therapies for treating post-SE injury. Clinical magnetic resonance imaging (MRI) studies have indicated that the hippocampus is injured following SE, but whether this injury will progress on to MTS-TLE remains unclear. To facilitate research, experimental models have been developed for investigating SE and its sequelae. Therefore, this study used the pilocarpine rat model for investigating SE and its related injury with multi-parametric MRI. The first part of this work investigated post-SE pathology with MRI and results indicate a characteristic injury profile for various brain regions injured by SE. Analysis of the hippocampus indicated a peak response 2 days following the insult showed a striking relationship to the degree of later injury, suggesting that imaging during the early period may predict later outcome. To investigate this response further, a combination of MRI and proteomic analysis was used and protein changes were identified. The second part of this study consisted of developing a method for imaging the onset and evolution of SE, and results suggest that limited perfusion may contribute to the hippocampal vulnerability to prolonged seizures. This work has identified an early MRI biomarker of later injury, and possible protein substrates for interventional therapy. Furthermore, evidence for the selective vulnerability to SE was found

Elucidating the Interplay of Structure, Dynamics, and Function in the Brain’s Neural Networks.

Brain’s structure, dynamics, and function are deeply intertwined. To understand how the brain functions, it is crucial to uncover the links between network structure and its dynamics. Here I examine different approaches to exploring the key connecting factors between network structure, dynamics and eventually its function. I predominantly concentrate on emergence and temporal evolution of synchronization, or coincidence of neuronal spike timings, as it has been associated with many brain functions while aberrant synchrony is implicated in many neurological disorders. Specifically, in chapter II, I investigate how the interplay of cellular properties with network coupling characteristics could affect the propensity of neural networks for synchronization. Then, in chapter III, I develop a set of measures that identify hallmarks and potentially predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. The developed metrics can be calculated in real time and therefore potentially applied in clinical situations. Finally, in chapter IV, I aim to tie the correlates of neural network dynamics to the brain function. More specifically, I elucidate dynamical underpinnings of learning and memory consolidation from in vivo recordings of mice experiencing contextual fear conditioning (CFC) and show, that the introduced notion of network stability may predict future animal performance on memory retrieval. Overall, the results presented within this dissertation underscore the importance of concurrent analysis of networks’ dynamical and structural properties. The developed approaches may prove useful beyond the specific application presented within this thesis.PhDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120768/1/mofakham_1.pd

In vitro neuronal cultures on MEA: an engineering approach to study physiological and pathological brain networks

Reti neuronali accoppiate a matrici di microelettrodi: un metodo ingegneristico per studiare reti cerebrali in situazioni fisiologiche e patologich

Homeostatische Plastizität - algorithmische und klinische Konsequenzen

Plasticity supports the remarkable adaptability and robustness of cortical processing. It allows the brain to learn and remember patterns in the sensory world, to refine motor control, to predict and obtain reward, or to recover function after injury. Behind this great flexibility hide a range of plasticity mechanisms, affecting different aspects of neuronal communication. However, little is known about the precise computational roles of some of these mechanisms. Here, we show that the interaction between spike-timing dependent plasticity (STDP), intrinsic plasticity and synaptic scaling enables neurons to learn efficient representations of their inputs. In the context of reward-dependent learning, the same mechanisms allow a neural network to solve a working memory task. Moreover, although we make no any apriori assumptions on the encoding used for representing inputs, the network activity resembles that of brain regions known to be associated with working memory, suggesting that reward-dependent learning may be a central force in working memory development. Lastly, we investigated some of the clinical implications of synaptic scaling and showed that, paradoxically, there are situations in which the very mechanisms that normally are required to preserve the balance of the system, may act as a destabilizing factor and lead to seizures. Our model offers a novel explanation for the increased incidence of seizures following chronic inflammation.Das menschliche Gehirn ist in der Lage sich an dramatische Veränderungen der Umgebung anzupassen. Hinter der Anpassungsfähigkeit des Gehirns stecken verschiedenste ernmechanismen. Einige dieser Mechanismen sind bereits relativ gut erforscht, wahrend bei anderen noch kaum bekannt ist, welche Rolle sie innerhalb der Informationsverarbeitungsprozesse im Gehirn spielen. Hier, soll gezeigt werden, dass das Zusammenspiel von Spike-Timing Dependent Plasticity' (STDP) mit zwei weiteren Prozessen, Synaptic Scaling' und Intrinsic Plasticity' (IP), es Nervenzellen ermöglicht Information effizient zu kodieren. Die gleichen Mechanismen führen dazu, dass ein Netzwerk aus Neuronen in der Lage ist, ein Arbeitsgedächtnis' für vergangene Stimuli zu entwickeln. Durch die Kombination von belohnungsabhängigem STDP und homöostatischen Mechanismen lernt das Netzwerk, die Stimulus-Repräsentationen für mehrere Zeitschritte verfügbar zu halten. Obwohl in unserem Modell-Design keinerlei. Informationen über die bevorzugte Art der Kodierung enthalten sind, finden wir nach Ende des Trainings neuronale Repräsentationen, die denjenigen aus vielen Arbeitsgedächtnis-Experimenten gleichen. Unser Modell zeigt, dass solche Repräsentationen durch Lernen enstehen können und dass Reward-abhängige Prozesse eine zentrale Kraft bei der Entwicklung des Arbeitsgedächtnisses spielen können. Abschliessend werden klinische Konsequenzen einiger Lern-Prozesse untersucht. Wir konnten zeigen, dass der selbe Mechanismus, der normalerweise die Aktivität im Gehirn in Balance hält, in speziellen Situationen auch zu Destabilisierung führen und epileptische Anfälle auslösen kann. Das hier vorgestellte Modell liefert eine neuartige Erklärung zur Entstehung von epileptischen Anfällen bei chronischen Entzündungen