Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deriving reproducible biomarkers from multi-site resting-state data: An
  Autism-based example   Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the
promise to reveal functional biomarkers of neuropsychiatric disorders. However,
extracting such biomarkers is challenging for complex multi-faceted
neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets
increase sample sizes to compensate for this complexity, at the cost of
uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to
those face in realistic diagnostic applications. Here, we demonstrate the
feasibility of inter-site classification of neuropsychiatric status, with an
application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large
(N=871) multi-site autism dataset. For this purpose, we investigate pipelines
that extract the most predictive biomarkers from the data. These R-fMRI
pipelines build participant-specific connectomes from functionally-defined
brain areas. Connectomes are then compared across participants to learn
patterns of connectivity that differentiate typical controls from individuals
with autism. We predict this neuropsychiatric status for participants from the
same acquisition sites or different, unseen, ones. Good choices of methods for
the various steps of the pipeline lead to 67% prediction accuracy on the full
ABIDE data, which is significantly better than previously reported results. We
perform extensive validation on multiple subsets of the data defined by
different inclusion criteria. These enables detailed analysis of the factors
contributing to successful connectome-based prediction. First, prediction
accuracy improves as we include more subjects, up to the maximum amount of
subjects available. Second, the definition of functional brain areas is of
paramount importance for biomarker discovery: brain areas extracted from large
R-fMRI datasets outperform reference atlases in the classification tasks.

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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con