Determining the Number of States in Dynamic Functional Connectivity Using Cluster Validity Indexes

• Authors: Victor M Vergara, Emory University; Mustafa Salman, Georgia Institute of Technology; Anees Abrol, Emory University; Flor A Espinoza, Emory University; Vince D Calhoun, Emory University
• Journal Title: JOURNAL OF NEUROSCIENCE METHODS
• Volume: Volume 337
• Publisher: Elsevier | 2020-05-01
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.jneumeth.2020.108651
• Subjects: Engineering, Electronics and Electrical; Computer Science; Engineering, Biomedical
• Keywords: Magnetic resonance imaging, dynamic functional network connectivity; clustering analysis; clustering validity index; k-means clustering; Elbow-Criterion; GAP-Statistics; Davies-Bouldin; Calinski-Harabasz; Silhouette; Wemmert-Gancarski; Ray-Turi; Ratkowsky-Lance; SD Dis; Dunn; Point-Biserial; McClain-Rao; Xie-Beni; PBM; Ball-Hall; Banfeld-Raftery; Ksq DetW; Log Det Ratio; Log SS Ratio; Scott-Symons; SD Scat; Trace W; Trace Wib and Det Ratio

Abstract

Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several clustering validity index (CVI) methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined.