Reconstruction of neuronal activity and connectivity patterns in the zebrafish olfactory bulb

In the olfactory bulb (OB), odors evoke distributed patterns of activity across glomeruli that are reorganized by networks of interneurons (INs). This reorganization results in multiple computations including a decorrelation of activity patterns across the output neurons, the mitral cells (MCs). To understand the mechanistic basis of these computations it is essential to analyze the relationship between function and structure of the underlying circuit. I combined in vivo twophoton calcium imaging with dense circuit reconstruction from complete serial block-face electron microscopy (SBEM) stacks of the larval zebrafish OB (4.5 dpf) with a voxel size of 9x9x25nm. To address bottlenecks in the workflow of SBEM, I developed a novel embedding and staining procedure that effectively reduces surface charging in SBEM and enables to acquire SBEM stacks with at least a ten-fold increase in both, signal-to-noise as well as acquisition speed. I set up a high throughput neuron reconstruction pipeline with >30 professional tracers that is available for the scientific community (ariadne-service.com). To assure efficient and accurate circuit reconstruction, I developed PyKNOSSOS, a Python software for skeleton tracing and synapse annotation, and CORE, a skeleton consolidation procedure that combines redundant reconstruction with targeted expert input. Using these procedures I reconstructed all neurons (>1000) in the larval OB. Unlike in the adult OB, INs were rare and appeared to represent specific subtypes, indicating that different sub-circuits develop sequentially. MCs were uniglomerular whereas inter-glomerular projections of INs were complex and biased towards groups of glomeruli that receive input from common types of sensory neurons. Hence, the IN network in the OB exhibits a topological organization that is governed by glomerular identity. Calcium imaging revealed that the larval OB circuitry already decorrelates activity patterns evoked by similar odors. The comparison of inter-glomerular connectivity to the functional interactions between glomeruli indicates that pattern decorrelation depends on specific, non-random inter-glomerular IN projections. Hence, the topology of IN networks in the OB appears to be an important determinant of circuit function

Learning Structure in Time Series for Neuroscience and Beyond

Advances in neuroscience are producing data at an astounding rate - data which are fiendishly complex both to process and to interpret. Biological neural networks are high-dimensional, nonlinear, noisy, heterogeneous, and in nearly every way defy the simplifying assumptions of standard statistical methods. In this dissertation we address a number of issues with understanding the structure of neural populations, from the abstract level of how to uncover structure in generic time series, to the practical matter of finding relevant biological structure in state-of-the-art experimental techniques. To learn the structure of generic time series, we develop a new statistical model, which we dub the probabilistic deterministic infinite automata (PDIA), which uses tools from nonparametric Bayesian inference to learn a very general class of sequence models. We show that the models learned by the PDIA often offer better predictive performance and faster inference than Hidden Markov Models, while being significantly more compact than models that simply memorize contexts. For large populations of neurons, models like the PDIA become unwieldy, and we instead investigate ways to robustly reduce the dimensionality of the data. In particular, we adapt the generalized linear model (GLM) framework for regres- sion to the case of matrix completion, which we call the low-dimensional GLM. We show that subspaces and dynamics of neural activity can be accurately recovered from model data, and with only minimal assumptions about the structure of the dynamics can still lead to good predictive performance on real data. Finally, to bridge the gap between recording technology and analysis, particularly as recordings from ever-larger populations of neurons becomes the norm, automated methods for extracting activity from raw recordings become a necessity. We present a number of methods for automatically segmenting biological units from optical imaging data, with applications to light sheet recording of genetically encoded calcium indicator fluorescence in the larval zebrafish, and optical electrophysiology using genetically encoded voltage indicators in culture. Together, these methods are a powerful set of tools for addressing the diverse challenges of modern neuroscience

Concordant spatio-temporal patterns of brain activation in zebrafish exposed to compounds with similar pharmacodynamics or with similar seizurogenic potential.

Abstract Drug development is a highly resource intensive process that uses large numbers of animals for assessing the safety and efficacy of drugs prior to clinical testing. Improving the efficiency of drug development in terms of financial expenditure and number of animals used is therefore of utmost concern, not only to industry, but also to animal welfare organisations such as the NC3Rs. Poor efficiency in drug development largely stems from drug attrition, particularly attrition in the latter stages of the testing due to the large amount of resources expended at the point of failure. It is therefore imperative that deleterious off-target effects are identified as early as possible. However, typically, identification of seizure as a side-effect of drugs is performed in the later stages of development due to the highly intensive and low-throughput nature of seizure assays. At which point, if a compound fails, a large amount of resources have been squandered. There therefore exists a need for high-throughput and relatively inexpensive seizure liability assays that can be used early in drug development to prevent compounds destined for failure undergoing unnecessary resource intensive testing. In this thesis we propose a refined approach using non-invasive imaging techniques in non-protected life stage zebrafish as a method for the detection of seizurogenic compounds early in drug development. In addition, we highlight its utility for elucidating the pharmacodynamics of compounds. In this study, a transgenic zebrafish line containing a GCaMP6s calcium sensor under the control of the pan-neuronal promoter elavl3 was used for functional profiling of compounds with varied pharmacologies. Light sheet microscopy was used to record fluorescent activity in three spatial dimensions over time (4-dimensions) from the zebrafish brain after exposure to forty-three different compounds with varied pharmacodynamics and seizure liability profiles. Hierarchical clustering was employed in order to assess if compounds with seizurogenic activity or similar pharmacodynamics elicited specific functional brain activity. It was found that compounds with dopaminergic and serotonergic mechanisms of action elicited highly specific and similar brain activity patterns and that non-seizurogenic drugs also clustered separately from seizurogenic ones. Subsequent analyses, focussed on the utilisation of machine learning techniques, developing a model that could be used to discriminate between compounds with and without potentially seizurogenic effects. It is clear, from the analyses presented here, that drugs do in fact elicit specific brain patterns in zebrafish and that these brain patterns are effectively detected using light sheet microscopy. This system is highly applicable for use within the drug industry and even in its relatively preliminary stages provided an accurate method of discriminating between compounds based on their physiological effects in zebrafish

Cardiac organoid technology and computational processing of cardiac physiology for advanced drug screening applications

Stem cell technology has gained considerable recognition since its inception to advance disease modeling and drug screening. This is especially true for tissues that are difficult to study due to tissue sensitivity and limited regenerative capacity, such as the heart. Previous work in stem cell-derived cardiac tissue has exploited how we can engineer biologically functional heart tissue by providing the appropriate external stimuli to facilitate tissue development. The goal of this dissertation is to explore the potentials of stem cell cardiac organoid models to recapitulate heart development and implement analytical computational tools to study cardiac physiology. These new tools were implemented as potential advancements in drug screening applications for better predictions of drug-related cardiotoxicity. Cardiac organoids, generated via micropatterning techniques, were explored to determine how controlling engineering parameters, specifically the geometry, direct tissue fate and organoid function. The advantage of cardiac organoid models is the ability to recapitulate and study human tissue morphogenesis and development, which has currently been restricted through animal models. The cardiac organoids demonstrated responsiveness manifested as impairments to tissue formation and contractile functions as a result of developmental drug toxicity. Single-cell genomic characterization of cardiac organoids unveiled a co-emergence of cardiac and endoderm tissue, which is seen in vivo through paracrine signaling between the liver and heart. We then implemented computational tools based on nonlinear mathematical analysis to evaluate the cardiac physiological drug response of stem cell-derived cardiomyocytes. This dissertation discusses in vitro tissue platforms as well as computational tools to study drug-induced cardiotoxicity. Using these tools, we can extend current toolboxes of understanding cardiac physiology for advanced investigations of stem-cell based cardiac tissue engineering

Visuomotor transformations underlying hunting behavior in zebrafish

Visuomotor circuits filter visual information and determine whether or not to engage downstream motor modules to produce behavioral outputs. However, the circuit mechanisms that mediate and link perception of salient stimuli to execution of an adaptive response are poorly understood. We combined a virtual hunting assay for tethered larval zebrafish with two-photon functional calcium imaging to simultaneously monitor neuronal activity in the optic tectum during naturalistic behavior. Hunting responses showed mixed selectivity for combinations of visual features, specifically stimulus size, speed, and contrast polarity. We identified a subset of tectal neurons with similar highly selective tuning, which show non-linear mixed selectivity for visual features and are likely to mediate the perceptual recognition of prey. By comparing neural dynamics in the optic tectum during response versus non-response trials, we discovered premotor population activity that specifically preceded initiation of hunting behavior and exhibited anatomical localization that correlated with motor variables. In summary, the optic tectum contains non-linear mixed selectivity neurons that are likely to mediate reliable detection of ethologically relevant sensory stimuli. Recruitment of small tectal assemblies appears to link perception to action by providing the premotor commands that release hunting responses. These findings allow us to propose a model circuit for the visuomotor transformations underlying a natural behavior

Computational and Imaging Methods for Studying Neuronal Populations during Behavior

One of the central questions in neuroscience is how the nervous system generates and regulates behavior. To understand the neural code for any behavior, an ideal experiment would entail (i) quantitatively defining that behavior, (ii) recording neuronal activity in relevant brain regions to identify the underlying neuronal circuits and eventually (iii) manipulating them to test their function. Novel methods in neuroscience have greatly advanced our abilities to conduct such experiments but are still insufficient. In this thesis, I developed methods for these three goals. In Chapter 2, I describe an automatic behavior identification and classification method for the cnidarian Hydra vulgaris using machine learning. In Chapter 3, I describe a fast volumetric two-photon microscope with dual-color laser excitation that can image in 3D the activity of populations of neurons from visual cortex of awake mice. In Chapter 4, I present a machine learning method that identifies cortical ensembles and pattern completion neurons in mouse visual cortex, using two-photon calcium imaging data. These methods advance current technologies, providing opportunities for new discoveries

Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution

Epileptic seizures are characterised by abnormal brain dynamics at multiple scales, engaging single neurons, neuronal ensembles and coarse brain regions. Key to understanding the cause of such emergent population dynamics, is capturing the collective behaviour of neuronal activity at multiple brain scales. In this thesis I make use of the larval zebrafish to capture single cell neuronal activity across the whole brain during epileptic seizures. Firstly, I make use of statistical physics methods to quantify the collective behaviour of single neuron dynamics during epileptic seizures. Here, I demonstrate a population mechanism through which single neuron dynamics organise into seizures: brain dynamics deviate from a phase transition. Secondly, I make use of single neuron network models to identify the synaptic mechanisms that actually cause this shift to occur. Here, I show that the density of neuronal connections in the network is key for driving generalised seizure dynamics. Interestingly, such changes also disrupt network response properties and flexible dynamics in brain networks, thus linking microscale neuronal changes with emergent brain dysfunction during seizures. Thirdly, I make use of non-linear causal inference methods to study the nature of the underlying neuronal interactions that enable seizures to occur. Here I show that seizures are driven by high synchrony but also by highly non-linear interactions between neurons. Interestingly, these non-linear signatures are filtered out at the macroscale, and therefore may represent a neuronal signature that could be used for microscale interventional strategies. This thesis demonstrates the utility of studying multi-scale dynamics in the larval zebrafish, to link neuronal activity at the microscale with emergent properties during seizures

Exploring behavioral circuits with holographic optogenetics and network imaging

Included works This thesis contains three previously published works: Semmelhack, Donovan, et al.; eLife 2014 Temizer, Donovan, et al.; Current Biology 2015 Thiele, Donovan, Baier; Neuron 2014 And one full manuscript, soon to be in the second round of review: Dal Maschio*, Donovan*, et al. *(Equal contributions) The work presented in these manuscripts is equivalent to a standard thesis