A systems analysis of neural connectivity and functionality in Caenorhabditis elegans

Part I considers the stereotypic patterns of synaptic connectivity in neural circuits, referred to as wiring specificity. Two aspects of wiring specificity are lamina specificity - placing synaptic partners in close spatial proximity - and synaptic specificity - making the appropriate synaptic connections among many physically adjacent neurons. Combinatorial expression patterns of surface proteins could be used to uniquely label neurons for the purposes of synaptic specificity. To test this model in the worm, the C. elegans nerve ring was volumetrically reconstructed from serial sectioned legacy electron micrographs, which provides detailed spatial and morphological information of neural processes in the nerve ring. Comparing the spatial data with the synaptic wiring diagram shows that the C. elegans nerve ring exhibits both lamina-like specificity and synaptic specificity. Combinatorial expression patterns of CAM genes in nerve ring neurons were analyzed. If a few key conditions regarding gene expression are satisfied, then the number of known combinatorial CAM expression patterns is sufficient to uniquely label neurons and can account for more than 90% of the observed synaptic specificity. Part II develops a new rotatable microfluidic device for simultaneously imaging calcium activity in bilateral neuron pairs within the same animal. Typically when imaging calcium activity in C. elegans, only the left or right side of the animal is imaged. This is due to the natural orientation of the worm which places neurons on the left and right side in different focal planes. Our new device allows the animal to be rotated, placing cells on the right and left side of the animal in the same focal plane

Optimization of Traced Neuron Skeleton Using Lasso-Based Model

Reconstruction of neuronal morphology from images involves mainly the extraction of neuronal skeleton points. It is an indispensable step in the quantitative analysis of neurons. Due to the complex morphology of neurons, many widely used tracing methods have difficulties in accurately acquiring skeleton points near branch points or in structures with tortuosity. Here, we propose two models to solve these problems. One is based on an L1-norm minimization model, which can better identify tortuous structure, namely, a local structure with large curvature skeleton points; the other detects an optimized branch point by considering the combination patterns of all neurites that link to this point. We combined these two models to achieve optimized skeleton detection for a neuron. We validate our models in various datasets including MOST and BigNeuron. In addition, we demonstrate that our method can optimize the traced skeletons from large-scale images. These characteristics of our approach indicate that it can reduce manual editing of traced skeletons and help to accelerate the accurate reconstruction of neuronal morphology

Reconstruction of 3D Neuronal Structures from Densely Packed Electron Microscopy Data Stacks

The goal of fully decoding how the brain works requires a detailed wiring diagram of the brain network that reveals the complete connectivity matrix. Recent advances in high-throughput 3D electron microscopy (EM) image acquisition techniques have made it possible to obtain high-resolution 3D imaging data that allows researchers to follow axons and dendrites and to identify pre-synaptic and post-synaptic sites, enabling the reconstruction of detailed neural circuits of the nervous system at the level of synapses. However, these massive data sets pose unique challenges to structural reconstruction because the inevitable staining noise, incomplete boundaries, and inhomogeneous staining intensities increase difficulty of 3D reconstruction and visualization. In this dissertation, a new set of algorithms are provided for reconstruction of neuronal morphology from stacks of serial EM images. These algorithms include (1) segmentation algorithms for obtaining the full geometry of neural circuits, (2) interactive segmentation tools for manual correction of erroneous segmentations, and (3) a validation method for obtaining a topologically correct segmentation when a set of segmentation alternatives are available. Experimental results obtained by using EM images containing densely packed cells demonstrate that (1) the proposed segmentation methods can successfully reconstruct full anatomical structures from EM images, (2) the editing tools provide a way for the user to easily and quickly refine incorrect segmentations, (3) and the validation method is effective in combining multiple segmentation results. The algorithms presented in this dissertation are expected to contribute to the reconstruction of the connectome and to open new directions in the development of reconstruction methods

Enabling Scalable Neurocartography: Images to Graphs for Discovery

In recent years, advances in technology have enabled researchers to ask new questions predicated on the collection and analysis of big datasets that were previously too large to study. More specifically, many fundamental questions in neuroscience require studying brain tissue at a large scale to discover emergent properties of neural computation, consciousness, and etiologies of brain disorders. A major challenge is to construct larger, more detailed maps (e.g., structural wiring diagrams) of the brain, known as connectomes. Although raw data exist, obstacles remain in both algorithm development and scalable image analysis to enable access to the knowledge within these data volumes. This dissertation develops, combines and tests state-of-the-art algorithms to estimate graphs and glean other knowledge across six orders of magnitude, from millimeter-scale magnetic resonance imaging to nanometer-scale electron microscopy. This work enables scientific discovery across the community and contributes to the tools and services offered by NeuroData and the Open Connectome Project. Contributions include creating, optimizing and evaluating the first known fully-automated brain graphs in electron microscopy data and magnetic resonance imaging data; pioneering approaches to generate knowledge from X-Ray tomography imaging; and identifying and solving a variety of image analysis challenges associated with building graphs suitable for discovery. These methods were applied across diverse datasets to answer questions at scales not previously explored

A machine learning approach to the unsupervised segmentation of mitochondria in subcellular electron microscopy data

Recent advances in cellular and subcellular microscopy demonstrated its potential towards unravelling the mechanisms of various diseases at the molecular level. The biggest challenge in both human- and computer-based visual analysis of micrographs is the variety of nanostructures and mitochondrial morphologies. The state-of-the-art is, however, dominated by supervised manual data annotation and early attempts to automate the segmentation process were based on supervised machine learning techniques which require large datasets for training. Given a minimal number of training sequences or none at all, unsupervised machine learning formulations, such as spectral dimensionality reduction, are known to be superior in detecting salient image structures. This thesis presents three major contributions developed around the spectral clustering framework which is proven to capture perceptual organization features. Firstly, we approach the problem of mitochondria localization. We propose a novel grouping method for the extracted line segments which describes the normal mitochondrial morphology. Experimental findings show that the clusters obtained successfully model the inner mitochondrial membrane folding and therefore can be used as markers for the subsequent segmentation approaches. Secondly, we developed an unsupervised mitochondria segmentation framework. This method follows the evolutional ability of human vision to extrapolate salient membrane structures in a micrograph. Furthermore, we designed robust non-parametric similarity models according to Gestaltic laws of visual segregation. Experiments demonstrate that such models automatically adapt to the statistical structure of the biological domain and return optimal performance in pixel classification tasks under the wide variety of distributional assumptions. The last major contribution addresses the computational complexity of spectral clustering. Here, we introduced a new anticorrelation-based spectral clustering formulation with the objective to improve both: speed and quality of segmentation. The experimental findings showed the applicability of our dimensionality reduction algorithm to very large scale problems as well as asymmetric, dense and non-Euclidean datasets

A model-based method for 3D reconstruction of cerebellar parallel fibres from high-resolution electron microscope images

In order to understand how the brain works, we need to understand how its neural circuits process information. Electron microscopy remains the only imaging technique capable of providing sufficient resolution to reconstruct the dense connectivity between all neurons in a circuit. Automated electron microscopy techniques are approaching the point where usefully large circuits might be successfully imaged, but the development of automated reconstruction techniques lags far behind. No fully-automated reconstruction technique currently produces acceptably accurate reconstructions, and semi-automated approaches currently require an extreme amount of manual effort. This reconstruction bottleneck places severe limits on the size of neural circuits that can be reconstructed. Improved automated reconstruction techniques are therefore highly desired and under active development. The human brain contains ~86 billion neurons and ~80% of these are located in the cerebellum. Of these cerebellar neurons, the vast majority are granule cells. The axons of these granule cells are called parallel fibres and tend to be oriented in approximately the same direction, making 2+1D reconstruction approaches feasible. In this work we focus on the problem of reconstructing these parallel fibres and make four main contributions: (1) a model-based algorithm for reconstructing 2D parallel fibre cross-sections that achieves state of the art 2D reconstruction performance; (2) a fully-automated algorithm for reconstructing 3D parallel fibres that achieves state of the art 3D reconstruction performance; (3) a semi-automated approach for reconstructing 3D parallel fibres that significantly improves reconstruction accuracy compared to our fully-automated approach while requiring ~40 times less labelling effort than a purely manual reconstruction; (4) a "gold standard" ground truth data set for the molecular layer of the mouse cerebellum that will provide a valuable reference for the development and benchmarking of reconstruction algorithms

Human brain networks: consensus, reproducibility, inter-modal comparison and epilepsy pathology

Classical and contemporary research in neuroscience postulates that connectivity is a fundamental component of human brain function. Recently, advances in computational neuroimaging have enabled reconstruction of macroscopic human brain structural connectivity in vivo using diffusion MRI. Studies show that the structural network topology may discriminate between neurological phenotypes or relate to individual brain function. To investigate disease effectively, it is necessary to determine the network methodological and biological variability. Reproducibility was calculated for two state-of-the-art reconstruction pipelines in healthy subjects. High reproducibility of connection weights was observed, which increased with connection strength. A high agreement between pipelines was found across network density thresholds. In addition, a robust core network was identified coinciding with a peak in similarity across thresholds, and replicated with alternative atlases. This study demonstrates the utility of applying multiple structural network pipelines to diffusion data in order to identify the most important connections. Focal epilepsy is characterised by seizures that can spread to contiguous and non-contiguous sites. Diffusion MRI and cortico-cortical evoked potentials were acquired in focal epilepsy patients to reconstruct and correlate their structural and effective brain networks and examine connectivity of the ictal-onset zone and propagative regions. Automated methods are described to reconstruct comparable largescale structural and effective networks. A high overlap and low correlation was observed between network modalities. Low correlation may be due to imperfections in methodology, such as difficulty tracing U-fibers using tractography. Effective connectivity amplitude, baseline fluctuation, and outward connectivity tended to be higher at ictal-onset regions, while higher structural connectivity between ictal-onset regions was observed. Furthermore, a high prevalence of structural and effective connections to sites of non-contiguous seizure spread was found. These results support the concept of highly excitable cortex underlying ictal-onset regions which promotes non-contiguous seizure spread via high outward connectivity

Psr1p interacts with SUN/sad1p and EB1/mal3p to establish the bipolar spindle

Regular Abstracts - Sunday Poster Presentations: no. 382During mitosis, interpolar microtubules from two spindle pole bodies (SPBs) interdigitate to create an antiparallel microtubule array for accommodating numerous regulatory proteins. Among these proteins, the kinesin-5 cut7p/Eg5 is the key player responsible for sliding apart antiparallel microtubules and thus helps in establishing the bipolar spindle. At the onset of mitosis, two SPBs are adjacent to one another with most microtubules running nearly parallel toward the nuclear envelope, creating an unfavorable microtubule configuration for the kinesin-5 kinesins. Therefore, how the cell organizes the antiparallel microtubule array in the first place at mitotic onset remains enigmatic. Here, we show that a novel protein psrp1p localizes to the SPB and plays a key role in organizing the antiparallel microtubule array. The absence of psr1+ leads to a transient monopolar spindle and massive chromosome loss. Further functional characterization demonstrates that psr1p is recruited to the SPB through interaction with the conserved SUN protein sad1p and that psr1p physically interacts with the conserved microtubule plus tip protein mal3p/EB1. These results suggest a model that psr1p serves as a linking protein between sad1p/SUN and mal3p/EB1 to allow microtubule plus ends to be coupled to the SPBs for organization of an antiparallel microtubule array. Thus, we conclude that psr1p is involved in organizing the antiparallel microtubule array in the first place at mitosis onset by interaction with SUN/sad1p and EB1/mal3p, thereby establishing the bipolar spindle.postprin

Removal of antagonistic spindle forces can rescue metaphase spindle length and reduce chromosome segregation defects

Regular Abstracts - Tuesday Poster Presentations: no. 1925Metaphase describes a phase of mitosis where chromosomes are attached and oriented on the bipolar spindle for subsequent segregation at anaphase. In diverse cell types, the metaphase spindle is maintained at a relatively constant length. Metaphase spindle length is proposed to be regulated by a balance of pushing and pulling forces generated by distinct sets of spindle microtubules and their interactions with motors and microtubule-associated proteins (MAPs). Spindle length appears important for chromosome segregation fidelity, as cells with shorter or longer than normal metaphase spindles, generated through deletion or inhibition of individual mitotic motors or MAPs, showed chromosome segregation defects. To test the force balance model of spindle length control and its effect on chromosome segregation, we applied fast microfluidic temperature-control with live-cell imaging to monitor the effect of switching off different combinations of antagonistic forces in the fission yeast metaphase spindle. We show that spindle midzone proteins kinesin-5 cut7p and microtubule bundler ase1p contribute to outward pushing forces, and spindle kinetochore proteins kinesin-8 klp5/6p and dam1p contribute to inward pulling forces. Removing these proteins individually led to aberrant metaphase spindle length and chromosome segregation defects. Removing these proteins in antagonistic combination rescued the defective spindle length and, in some combinations, also partially rescued chromosome segregation defects. Our results stress the importance of proper chromosome-to-microtubule attachment over spindle length regulation for proper chromosome segregation.postprin