A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences

  • Javid Dadashkarimi
  • Siyuan Gao
  • Erin Yeagle
  • Stephanie Noble
  • Dustin Scheinost
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11848)


Functional connectivity derived from functional magnetic resonance imaging data has been extensively used to characterize individual and group differences. While these connectomes have traditionally been constructed using resting-state data, recent work has highlighted the importance of combining multiple task connectomes, particularly for identifying individual differences. Yet, these methods have not yet been extended to investigate differences at the group level. Here, we propose a mass multivariate edge-wise approach to improve the detection of group differences by combining connectomes from multiple sources. For each edge, the magnitude of connection strength from each of multiple connectomes are included in statistical hypothesis testing. We evaluate the proposed approach by estimating sex differences in two large, publicly available datasets: the Human Connectome Project and Philadelphia Neurodevelopmental Cohort. Results indicate the proposed mass multivariate edge-wise analysis offers improved detection of group differences compared to univariate analysis, and support the utility of combining multiple connectomes to improve detection of group differences.



Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54 MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The remainder of the data used in this study were provided by the Philadelphia Neurodevelopmental Cohort (Principal Investigators: Hakon Hakonarson and Raquel Gur; phs000607.v1.p1). Support for the collection of the data sets was provided by grant RC2MH089983 awarded to Raquel Gur and RC2MH089924 awarded to Hakon Hakonarson. All subjects were recruited through the Center for Applied Genomics at The Children’s Hospital in Philadelphia.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Javid Dadashkarimi
    • 1
  • Siyuan Gao
    • 2
  • Erin Yeagle
    • 3
  • Stephanie Noble
    • 3
  • Dustin Scheinost
    • 3
  1. 1.Department of Computer ScienceYale UniversityNew HavenUSA
  2. 2.Department of Biomedical EngineeringYale UniversityNew HavenUSA
  3. 3.Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenUSA

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