Detecting Abnormalities in Resting-State Dynamics: An Unsupervised Learning Approach

  • Meenakshi KhoslaEmail author
  • Keith Jamison
  • Amy Kuceyeski
  • Mert R. Sabuncu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11861)


Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional connectivity, there has been a recent surge in examining the temporal patterns in these data. In this paper, we explore two strategies for capturing the normal variability in resting-state activity across a healthy population: (a) an autoencoder approach on the rs-fMRI sequence, and (b) a next frame prediction strategy. We show that both approaches can learn useful representations of rs-fMRI data and demonstrate their novel application for abnormality detection in the context of discriminating autism patients from healthy controls.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Meenakshi Khosla
    • 1
    Email author
  • Keith Jamison
    • 2
    • 3
  • Amy Kuceyeski
    • 2
    • 3
  • Mert R. Sabuncu
    • 1
    • 4
  1. 1.School of Electrical and Computer EngineeringCornell UniversityIthacaUSA
  2. 2.RadiologyWeill Cornell Medical CollegeNew YorkUSA
  3. 3.Brain and Mind Research InstituteWeill Cornell Medical CollegeNew YorkUSA
  4. 4.Nancy E. and Peter C. Meinig School of Biomedical EngineeringCornell UniversityIthacaUSA

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