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Tensor Decomposition for Neurodevelopmental Disorder Prediction

  • Shah Muhammad Hamdi
  • Yubao Wu
  • Soukaina Filali Boubrahimi
  • Rafal Angryk
  • Lisa Crystal Krishnamurthy
  • Robin Morris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

Functional Magnetic Resonance Imaging (fMRI) has been successfully used by the neuroscientists for diagnosis and analysis of neurological and neurodevelopmental disorders. After transforming fMRI data into functional networks, graph classification algorithms have been applied for distinguishing healthy controls from impaired subjects. Recently, classification followed by tensor decomposition has been used as an alternative, since the sparsity of the functional networks is still an open question. In this work, we present five tensor models of fMRI data, considering the time series of the brain regions as the raw form. After decomposing the tensor using CANDECOMP/PARAFAC (CP) and Tucker decomposition, we compared nearest neighbor classification accuracy on the resulting subject factor matrix. We show experimental results using an fMRI dataset from adult subjects with neurodevelopmental reading disabilities and normal controls.

Keywords

fMRI Tensor decomposition Reading disabilities 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shah Muhammad Hamdi
    • 1
  • Yubao Wu
    • 1
  • Soukaina Filali Boubrahimi
    • 1
  • Rafal Angryk
    • 1
  • Lisa Crystal Krishnamurthy
    • 2
    • 3
  • Robin Morris
    • 3
    • 4
  1. 1.Departement of Computer ScienceGeorgia State UniversityAtlantaUSA
  2. 2.Center for Visual and Neurocognitive RehabilitationDecaturUSA
  3. 3.Center for Advanced Brain ImagingGeorgia State University and Georgia Institute of TechnologyAtlantaUSA
  4. 4.Department of PsychologyGeorgia State UniversityAtlantaUSA

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