Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism.

Journal: NeuroImage. Clinical
Published Date: Apr 9, 2015

Abstract

Despite consensus on the neurological nature of autism spectrum disorders (ASD), brain biomarkers remain unknown and diagnosis continues to be based on behavioral criteria. Growing evidence suggests that brain abnormalities in ASD occur at the level of interconnected networks; however, previous attempts using functional connectivity data for diagnostic classification have reached only moderate accuracy. We selected 252 low-motion resting-state functional MRI (rs-fMRI) scans from the Autism Brain Imaging Data Exchange (ABIDE) including typically developing (TD) and ASD participants (n = 126 each), matched for age, non-verbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification, implementing several machine learning tools. While support vector machines in combination with particle swarm optimization and recursive feature elimination performed modestly (with accuracies for validation datasets <70%), diagnostic classification reached a high accuracy of 91% with random forest (RF), a nonparametric ensemble learning method. Among the 100 most informative features (connectivities), for which this peak accuracy was achieved, participation of somatosensory, default mode, visual, and subcortical regions stood out. Whereas some of these findings were expected, given previous findings of default mode abnormalities and atypical visual functioning in ASD, the prominent role of somatosensory regions was remarkable. The finding of peak accuracy for 100 interregional functional connectivities further suggests that brain biomarkers of ASD may be regionally complex and distributed, rather than localized.

Authors

  • Colleen P Chen
    Department of Psychology, Brain Development Imaging Laboratory, San Diego State University, San Diego, CA, USA ; Computational Science Research Center, San Diego State University, San Diego, CA, USA.
  • Christopher L Keown
    Department of Psychology, Brain Development Imaging Laboratory, San Diego State University, San Diego, CA, USA ; Department of Cognitive Science, University of California, San Diego, CA, USA.
  • Afrooz Jahedi
    Computational Science, San Diego State University/ Claremont Graduate University's Joint Doctoral Program, San Diego, CA, USA.
  • Aarti Nair
    Department of Psychology, Brain Development Imaging Laboratory, San Diego State University, San Diego, CA, USA ; Joint Doctoral Program in Clinical Psychology, San Diego State University and University of California San Diego, San Diego, CA, United States.
  • Mark E Pflieger
    Computational Science Research Center, San Diego State University, San Diego, CA, USA ; Cortech Translational Solutions Center, La Mesa, CA, USA.
  • Barbara A Bailey
    Computational Science Research Center, San Diego State University, San Diego, CA, USA ; Department of Mathematics and Statistics, San Diego State University, San Diego, CA, USA.
  • Ralph-Axel Müller
    Brain Development Imaging Lab (BDIL), Psychology, San Diego State University (SDSU), 6363 Alvarado Ct. Suite 200, San Diego, CA 92120, USA.

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