Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code.

Journal: Data in brief
Published Date: Feb 2, 2018

Abstract

This article provides data for five different neuropsychiatric disorders-Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome-along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters.

Authors

  • Xinyu Zhao
    AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
  • D Rangaprakash
    AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
  • Thomas S Denney
    AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
  • Jeffrey S Katz
    AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
  • Michael N Dretsch
    Human Dimension Division, HQ TRADOC, Fort Eustis, VA, USA.
  • Gopikrishna Deshpande
    AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.

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