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Deriving robust biomarkers from multi-site resting-state data: An Autism-based example

View ORCID ProfileAlexandre Abraham, Michael Milham, Adriana Di Martino, R. Cameron Craddock, Dimitris Samaras, Bertrand Thirion, Gael Varoquaux
doi: https://doi.org/10.1101/075853
Alexandre Abraham
aParietal Team, INRIA Saclay-Île-de-France, Saclay, France.
b2, Neurospin bât 145, 91191 Gif-Sur-Yvette, France.
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  • ORCID record for Alexandre Abraham
  • For correspondence: abraham.alexandre@gmail.com
Michael Milham
eChild Mind Institute, New York, USA
fNathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA
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Adriana Di Martino
gNYU Langone Medical Center, New York, USA
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R. Cameron Craddock
eChild Mind Institute, New York, USA
fNathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA
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Dimitris Samaras
cStony Brook University, NY 11794, USA
dEcole Centrale, 92290 Châtenay Malabry, France.
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Bertrand Thirion
aParietal Team, INRIA Saclay-Île-de-France, Saclay, France.
b2, Neurospin bât 145, 91191 Gif-Sur-Yvette, France.
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Gael Varoquaux
aParietal Team, INRIA Saclay-Île-de-France, Saclay, France.
b2, Neurospin bât 145, 91191 Gif-Sur-Yvette, France.
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Abstract

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 neuropathologies, 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 copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 19, 2016.
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Deriving robust biomarkers from multi-site resting-state data: An Autism-based example
Alexandre Abraham, Michael Milham, Adriana Di Martino, R. Cameron Craddock, Dimitris Samaras, Bertrand Thirion, Gael Varoquaux
bioRxiv 075853; doi: https://doi.org/10.1101/075853
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Deriving robust biomarkers from multi-site resting-state data: An Autism-based example
Alexandre Abraham, Michael Milham, Adriana Di Martino, R. Cameron Craddock, Dimitris Samaras, Bertrand Thirion, Gael Varoquaux
bioRxiv 075853; doi: https://doi.org/10.1101/075853

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