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

• Type of Material: Article
• 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
• Language: English
• Date: 2020-05-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2020 Elsevier B.V. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.jneumeth.2020.108651
• Title of Journal or Parent Work: JOURNAL OF NEUROSCIENCE METHODS
• Volume: 337
• Grant/Funding Information: This work was funded by the following NIH grants P20GM103472/1R01EB006841/R01REB020407 and National Science Foundation (#1539067) to V.C.
• Keywords: Log Det Ratio; Ball-Hall; Ratkowsky-Lance; Magnetic resonance imaging, dynamic functional network connectivity; Scott-Symons; Log SS Ratio; Silhouette; Trace W; clustering analysis; Elbow-Criterion; Ray-Turi; Wemmert-Gancarski; Banfeld-Raftery; Trace Wib and Det Ratio; Point-Biserial; Calinski-Harabasz; PBM; SD Scat; Davies-Bouldin; Ksq DetW; clustering validity index; Dunn; SD Dis; McClain-Rao; Xie-Beni; k-means clustering; GAP-Statistics

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.