Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference

  • Sourabh PalandeEmail author
  • Vipin Jose
  • Brandon Zielinski
  • Jeffrey Anderson
  • P. Thomas Fletcher
  • Bei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10511)


A large body of evidence relates autism with abnormal structural and functional brain connectivity. Structural covariance MRI (scMRI) is a technique that maps brain regions with covarying gray matter density across subjects. It provides a way to probe the anatomical structures underlying intrinsic connectivity networks (ICNs) through the analysis of the gray matter signal covariance. In this paper, we apply topological data analysis in conjunction with scMRI to explore network-specific differences in the gray matter structure in subjects with autism versus age-, gender- and IQ-matched controls. Specifically, we investigate topological differences in gray matter structures captured by structural covariance networks (SCNs) derived from three ICNs strongly implicated in autism, namely, the salience network (SN), the default mode network (DMN) and the executive control network (ECN). By combining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism, from SCNs derived from SN and ECN. These differences in brain architecture are consistent with direct structural analysis using scMRI (Zielinski et al. 2012).



This work was supported by NIH grant R01EB022876 and NSF grant IIS-1513616.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sourabh Palande
    • 1
    • 2
    Email author
  • Vipin Jose
    • 1
    • 2
  • Brandon Zielinski
    • 3
  • Jeffrey Anderson
    • 4
  • P. Thomas Fletcher
    • 1
    • 2
  • Bei Wang
    • 1
    • 2
  1. 1.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA
  2. 2.School of ComputingUniversity of UtahSalt Lake CityUSA
  3. 3.Pediatrics and NeurologyUniversity of UtahSalt Lake CityUSA
  4. 4.RadiologyUniversity of UtahSalt Lake CityUSA

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