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

doi:10.1016/j.dib.2018.01.080

Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code

Data Brief. 2018 Feb 2:22:570-573. doi: 10.1016/j.dib.2018.01.080. eCollection 2019 Feb.

Authors

Xinyu Zhao¹, D Rangaprakash^{1

2}, Thomas S Denney Jr^{1

3

4

5}, Jeffrey S Katz^{1

3

4

5}, Michael N Dretsch^{6

7}, Gopikrishna Deshpande^{1

3

4

8

5}

Affiliations

¹ AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
² Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
³ Department of Psychology, Auburn University, Auburn, AL, USA.
⁴ Alabama Advanced Imaging Consortium, Birmingham, USA.
⁵ Center for Neuroscience, Auburn University, USA.
⁶ Human Dimension Division, HQ TRADOC, Fort Eustis, VA, USA.
⁷ US Army Aeromedical Research Laboratory, Fort Rucker, AL, USA.
⁸ Center for Health Ecology and Equity Research, Auburn University, USA.

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 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: Clustering; Effective connectivity; Functional connectivity; Functional magnetic resonance imaging; Genetic algorithm; Psychiatric disorders; Unsupervised learning.