Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code
Xinyu Zhao,
D. Rangaprakash,
Thomas S. Denney, Jr.,
Jeffrey S. Katz,
Michael N. Dretsch,
Gopikrishna Deshpande
Affiliations
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; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
Thomas S. Denney, Jr.
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, USA; Center for Neuroscience, Auburn University, USA
Jeffrey S. Katz
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, USA; Center for Neuroscience, Auburn University, USA
Michael N. Dretsch
Human Dimension Division, HQ TRADOC, Fort Eustis, VA, USA; US Army Aeromedical Research Laboratory, Fort Rucker, AL, USA
Gopikrishna Deshpande
AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA; Department of Psychology, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, USA; Center for Health Ecology and Equity Research, Auburn University, USA; Center for Neuroscience, Auburn University, USA; Correspondence to: Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA. Fax: +1 334 844 0214.
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 a priori 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. Keywords: Functional magnetic resonance imaging, Functional connectivity, Effective connectivity, Unsupervised learning, Clustering, Genetic algorithm, Psychiatric disorders
