From Neurons to Behavior: Visual Analytics Methods for Heterogeneous Spatial Big Brain Data

Florian Ganglberger
From Neurons to Behavior: Visual Analytics Methods for Heterogeneous Spatial Big Brain Data
Supervisor: Meister Eduard Gröller
Duration: 2014-2019
[image] [PhD Thesis]

Information

Abstract

Advances in neuro-imaging have allowed big brain initiatives and consortia to create vast resources of brain data that can be mined for insights into mental processes and biological principles. Research in this area does not only relate to mind and consciousness, but also to the understanding of many neurological disorders, such as Alzheimer’s disease, autism, and anxiety. Exploring the relationships between genes, brain circuitry, and behavior is therefore a key element in research that requires the joint analysis of a heterogeneous set of spatial brain data, including 3D imaging data, anatomical data, and brain networks at varying scales, resolutions, and modalities. Due to high-throughput imaging platforms, this data’s size and complexity goes beyond the state-of-the-art by several orders of magnitude. Current analytical workflows involve time-consuming manual data aggregation and extensive computational analysis in script-based toolboxes. Visual analytics methods for exploring big brain data can support neuroscientists in this process, so they can focus on understanding the data rather than handling it. In this thesis, several contributions that target this problem are presented. The first contribution is a computational method that fuses genetic information with spatial gene expression data and connectivity data to predict functional neuroanatomical maps. These maps indicate, which brain areas might be related to a specific function or behavior. The approach has been applied to predict yet unknown functional neuroanatomy underlying multigeneic behavioral traits identified in genetic association studies and has demonstrated that rather than being randomly distributed throughout the brain, functionally-related gene sets accumulate in specific networks. The second contribution is the creation of a data structure that enables the interactive exploration of big brain network data with billions of edges. By utilizing the resulting hierarchical and spatial organization of the data, this approach allows neuroscientists on-demand queries of incoming/outgoing connections of arbitrary regions of interest on different anatomical scales. These queries would otherwise exceed the limits of current consumer level PCs. The data structure is used in the third contribution, a novel web-based framework to explore neurobiological imaging and connectivity data of different types, modalities, and scale. It employs a query-based interaction scheme to retrieve 3D spatial gene expressions and various types of connectivity to enable an interactive dissection of networks in real-time with respect to their genetic composition. The data is related to a hierarchical organization of common anatomical atlases that enables neuroscientists to compare multimodal networks on different scales in their anatomical context. Furthermore, the framework is designed to facilitate collaborative work with shareable comprehensive workflows on the web. As a result, the approaches presented in this thesis may assist neuroscientists to refine their understanding of the functional organization of the brain beyond simple anatomical domains and expand their knowledge about how our genes affect our mind.

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BibTeX

@phdthesis{ganglberger2019,
  title =      "From Neurons to Behavior: Visual Analytics Methods for
               Heterogeneous Spatial Big Brain Data ",
  author =     "Florian Ganglberger",
  year =       "2019",
  abstract =   "Advances in neuro-imaging have allowed big brain initiatives
               and consortia to create vast resources of brain data that
               can be mined for insights into mental processes and
               biological principles. Research in this area does not only
               relate to mind and consciousness, but also to the
               understanding of many neurological disorders, such as
               Alzheimer’s disease, autism, and anxiety. Exploring the
               relationships between genes, brain circuitry, and behavior
               is therefore a key element in research that requires the
               joint analysis of a heterogeneous set of spatial brain data,
               including 3D imaging data, anatomical data, and brain
               networks at varying scales, resolutions, and modalities. Due
               to high-throughput imaging platforms, this data’s size and
               complexity goes beyond the state-of-the-art by several
               orders of magnitude. Current analytical workflows involve
               time-consuming manual data aggregation and extensive
               computational analysis in script-based toolboxes. Visual
               analytics methods for exploring big brain data can support
               neuroscientists in this process, so they can focus on
               understanding the data rather than handling it. In this
               thesis, several contributions that target this problem are
               presented. The first contribution is a computational method
               that fuses genetic information with spatial gene expression
               data and connectivity data to predict functional
               neuroanatomical maps. These maps indicate, which brain areas
               might be related to a specific function or behavior. The
               approach has been applied to predict yet unknown functional
               neuroanatomy underlying multigeneic behavioral traits
               identified in genetic association studies and has
               demonstrated that rather than being randomly distributed
               throughout the brain, functionally-related gene sets
               accumulate in specific networks. The second contribution is
               the creation of a data structure that enables the
               interactive exploration of big brain network data with
               billions of edges. By utilizing the resulting hierarchical
               and spatial organization of the data, this approach allows
               neuroscientists on-demand queries of incoming/outgoing
               connections of arbitrary regions of interest on different
               anatomical scales. These queries would otherwise exceed the
               limits of current consumer level PCs. The data structure is
               used in the third contribution, a novel web-based framework
               to explore neurobiological imaging and connectivity data of
               different types, modalities, and scale. It employs a
               query-based interaction scheme to retrieve 3D spatial gene
               expressions and various types of connectivity to enable an
               interactive dissection of networks in real-time with respect
               to their genetic composition. The data is related to a
               hierarchical organization of common anatomical atlases that
               enables neuroscientists to compare multimodal networks on
               different scales in their anatomical context. Furthermore,
               the framework is designed to facilitate collaborative work
               with shareable comprehensive workflows on the web. As a
               result, the approaches presented in this thesis may assist
               neuroscientists to refine their understanding of the
               functional organization of the brain beyond simple
               anatomical domains and expand their knowledge about how our
               genes affect our mind. ",
  month =      jun,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2019/ganglberger2019/",
}