Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discovery of Multi-Level Network Differences Across Populations of
  Heterogeneous Connectomes   A connectome is a map of the structural and/or functional connections in the
brain. This information-rich representation has the potential to transform our
understanding of the relationship between patterns in brain connectivity and
neurological processes, disorders, and diseases. However, existing
computational techniques used to analyze connectomes are oftentimes
insufficient for interrogating multi-subject connectomics datasets: most
current methods are either solely designed to analyze single connectomes, or
leverage heuristic graph statistics that are unable to capture the complete
topology of connections between brain regions. To enable more rigorous
connectomics analysis, we introduce a set of robust and interpretable
statistical hypothesis tests motivated by recent theoretical advances in random
graph models. These tests facilitate simultaneous analysis of multiple
connectomes across different levels of network topology, enabling the robust
and reproducible discovery of hierarchical brain structures that vary in
relation with phenotypic profiles. In addition to explaining the theoretical
foundations and guarantees of our hypothesis tests, we demonstrate their
superiority over current state-of-the-art connectomics methods through
extensive simulation studies, as well as synthetic and real-data experiments.
Using a set of high-resolution connectomes obtained from genetically distinct
mouse strains (including the BTBR mouse -- a standard model of autism -- and
three behavioral wild-types), we illustrate how our methods can be used to
successfully uncover latent information in multi-subject connectomics data and
yield valuable insights into the connective correlates of neurological
phenotypes. The code necessary to reproduce the analyses, simulations, and
figures presented in this work are available in a series of Jupyter Notebooks
(https://github.com/neurodata/MCC).

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The categories of this paper are: chem-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: eess