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A Connectivity-based Psychometric Prediction Framework for Brain-behavior Relationship Studies

View ORCID ProfileJianxiao Wu, View ORCID ProfileSimon B. Eickhoff, View ORCID ProfileFelix Hoffstaedter, View ORCID ProfileKaustubh R. Patil, View ORCID ProfileHolger Schwender, View ORCID ProfileSarah Genon
doi: https://doi.org/10.1101/2020.01.15.907642
Jianxiao Wu
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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  • For correspondence: j.wu@fz-juelich.de s.genon@fz-juelich.de
Simon B. Eickhoff
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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Felix Hoffstaedter
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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Kaustubh R. Patil
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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Holger Schwender
Mathematical Institute, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
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Sarah Genon
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, GermanyInstitute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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  • For correspondence: j.wu@fz-juelich.de s.genon@fz-juelich.de
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Abstract

The recent availability of population-based studies with standard neuroimaging measurements and extensive psychometric characterization opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of the prediction model based on connectivity within a network of brain regions severely limits the interpretation of the brain-behavior patterns from a cognitive neuroscience perspective. To address this issue, we here propose a connectivity-based psychometric prediction (CBPP) framework based on individual region’s connectivity profile. Preliminary to the development of this region-wise machine learning approach, we performed an extensive assessment of the general CBPP framework based on whole-brain connectivity information. Because a systematic evaluation of different parameters was lacking from previous literature, we evaluated several approaches pertaining to the different steps of a CBPP study. We hence tested 72 different approach combinations in a cohort of over 900 healthy adults across 98 psychometric variables. Overall, our extensive evaluation combined to an innovative region-wise machine learning approach, offering a framework that optimizes both prediction performance and neurobiological validity (and hence interpretability) to study brain-behavior relationships.

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Posted January 15, 2020.
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A Connectivity-based Psychometric Prediction Framework for Brain-behavior Relationship Studies
Jianxiao Wu, Simon B. Eickhoff, Felix Hoffstaedter, Kaustubh R. Patil, Holger Schwender, Sarah Genon
bioRxiv 2020.01.15.907642; doi: https://doi.org/10.1101/2020.01.15.907642
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A Connectivity-based Psychometric Prediction Framework for Brain-behavior Relationship Studies
Jianxiao Wu, Simon B. Eickhoff, Felix Hoffstaedter, Kaustubh R. Patil, Holger Schwender, Sarah Genon
bioRxiv 2020.01.15.907642; doi: https://doi.org/10.1101/2020.01.15.907642

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