Abstract
Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.
Highlights
A neuropsychiatric disorder is a brain-based dysfunctional condition associated with impairments of affect, cognition and behavior
Unsupervised models have been traditionally used to uncover clusters of subjects with similar patterns of imaging data, with applications in identifying disease clusters [8, 9] as well as sub-clusters [10] within a disease. Most of these studies use k-means clustering [11, 12]. It is besieged with methodological issues such as a priori choice of clusters needed in k-means, and the large dimensionality of imaging data necessitates some type of dimensionality reduction for clustering to work as intended
We address the above shortcomings using resting state functional magnetic resonance imaging data obtained from five different neuropsychiatric disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer’s Disease (AD), Autism Spectrum Disorder (ASD), Posttraumatic Stress
Summary
A neuropsychiatric disorder is a brain-based dysfunctional condition associated with impairments of affect, cognition and behavior. It is besieged with methodological issues such as a priori choice of clusters needed in k-means, and the large dimensionality of imaging data necessitates some type of dimensionality reduction for clustering to work as intended This last step is either not carried out [13], or carried out by preselecting features not from the structure in the data, but by some external considerations such as previous findings in a given disorder [14, 15]. Such approaches rob the method of its advantages of being truly data-driven, in that the clusters obtained from imaging data are seldom compared to data obtained from clinical diagnostic criteria and related behavioral phenotypes.
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