20 research outputs found
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
Computing Scalable Multivariate Glocal Invariants of Large (Brain-) Graphs
Graphs are quickly emerging as a leading abstraction for the representation
of data. One important application domain originates from an emerging
discipline called "connectomics". Connectomics studies the brain as a graph;
vertices correspond to neurons (or collections thereof) and edges correspond to
structural or functional connections between them. To explore the variability
of connectomes---to address both basic science questions regarding the
structure of the brain, and medical health questions about psychiatry and
neurology---one can study the topological properties of these brain-graphs. We
define multivariate glocal graph invariants: these are features of the graph
that capture various local and global topological properties of the graphs. We
show that the collection of features can collectively be computed via a
combination of daisy-chaining, sparse matrix representation and computations,
and efficient approximations. Our custom open-source Python package serves as a
back-end to a Web-service that we have created to enable researchers to upload
graphs, and download the corresponding invariants in a number of different
formats. Moreover, we built this package to support distributed processing on
multicore machines. This is therefore an enabling technology for network
science, lowering the barrier of entry by providing tools to biologists and
analysts who otherwise lack these capabilities. As a demonstration, we run our
code on 120 brain-graphs, each with approximately 16M vertices and up to 90M
edges.Comment: Published as part of 2013 IEEE GlobalSIP conferenc
An Automated Images-to-Graphs Framework for High Resolution Connectomics
Reconstructing a map of neuronal connectivity is a critical challenge in
contemporary neuroscience. Recent advances in high-throughput serial section
electron microscopy (EM) have produced massive 3D image volumes of nanoscale
brain tissue for the first time. The resolution of EM allows for individual
neurons and their synaptic connections to be directly observed. Recovering
neuronal networks by manually tracing each neuronal process at this scale is
unmanageable, and therefore researchers are developing automated image
processing modules. Thus far, state-of-the-art algorithms focus only on the
solution to a particular task (e.g., neuron segmentation or synapse
identification).
In this manuscript we present the first fully automated images-to-graphs
pipeline (i.e., a pipeline that begins with an imaged volume of neural tissue
and produces a brain graph without any human interaction). To evaluate overall
performance and select the best parameters and methods, we also develop a
metric to assess the quality of the output graphs. We evaluate a set of
algorithms and parameters, searching possible operating points to identify the
best available brain graph for our assessment metric. Finally, we deploy a
reference end-to-end version of the pipeline on a large, publicly available
data set. This provides a baseline result and framework for community analysis
and future algorithm development and testing. All code and data derivatives
have been made publicly available toward eventually unlocking new biofidelic
computational primitives and understanding of neuropathologies.Comment: 13 pages, first two authors contributed equally V2: Added additional
experiments and clarifications; added information on infrastructure and
pipeline environmen
Neural Reconstruction Integrity: A Metric for Assessing the Connectivity Accuracy of Reconstructed Neural Networks
Neuroscientists are actively pursuing high-precision maps, or graphs consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we developed a metric for measuring the performance of such methods by comparing their output with those generated by human annotators (“ground truth” data). Whereas classic metrics for comparing annotated neural tissue reconstructions generally do so at the voxel level, the metric proposed here measures the “integrity” of neurons based on the degree to which a collection of synaptic terminals belonging to a single neuron of the reconstruction can be matched to those of a single neuron in the ground truth data. The metric is largely insensitive to small errors in segmentation and more directly measures accuracy of the generated brain graph. It is our hope that use of the metric will facilitate the broader community's efforts to improve upon existing methods for acquiring brain graphs. Herein we describe the metric in detail, provide demonstrative examples of the intuitive scores it generates, and apply it to a synthesized neural network with simulated reconstruction errors. Demonstration code is available
Neural Reconstruction Integrity: A Metric for Assessing the Connectivity Accuracy of Reconstructed Neural Networks
Neuroscientists are actively pursuing high-precision maps, or graphs consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we developed a metric for measuring the performance of such methods by comparing their output with those generated by human annotators (“ground truth” data). Whereas classic metrics for comparing annotated neural tissue reconstructions generally do so at the voxel level, the metric proposed here measures the “integrity” of neurons based on the degree to which a collection of synaptic terminals belonging to a single neuron of the reconstruction can be matched to those of a single neuron in the ground truth data. The metric is largely insensitive to small errors in segmentation and more directly measures accuracy of the generated brain graph. It is our hope that use of the metric will facilitate the broader community's efforts to improve upon existing methods for acquiring brain graphs. Herein we describe the metric in detail, provide demonstrative examples of the intuitive scores it generates, and apply it to a synthesized neural network with simulated reconstruction errors. Demonstration code is available