title: Development and validation of consensus clustering-based framework for brain segmentation using resting fMRI.
abstract: BACKGROUND
  Clustering methods are increasingly employed to segment brain regions into functional subdivisions using resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods are highly sensitive to the (i) precise algorithms employed, (ii) their initializations, and (iii) metrics used for uncovering the optimal number of clusters from the data.
  
  
  METHODS
  To address these issues, we develop a novel consensus clustering evidence accumulation (CC-EAC) framework, which effectively combines multiple clustering methods for segmenting brain regions using rs-fMRI data. Using extensive computer simulations, we examine the performance of widely used clustering algorithms including K-means, hierarchical, and spectral clustering as well as their combinations. We also examine the accuracy and validity of five objective criteria for determining the optimal number of clusters: mutual information, variation of information, modified silhouette, Rand index, and probabilistic Rand index.
  
  
  RESULTS
  A CC-EAC framework with a combination of base K-means clustering (KC) and hierarchical clustering (HC) with probabilistic Rand index as the criterion for choosing the optimal number of clusters, accurately uncovered the correct number of clusters from simulated datasets. In experimental rs-fMRI data, these methods reliably detected functional subdivisions of the supplementary motor area, insula, intraparietal sulcus, angular gyrus, and striatum.
  
  
  METHODS
  Unlike conventional approaches, CC-EAC can accurately determine the optimal number of stable clusters in rs-fMRI data, and is robust to initialization and choice of free parameters.
  
  
  CONCLUSIONS
  A novel CC-EAC framework is proposed for segmenting brain regions, by effectively combining multiple clustering methods and identifying optimal stable functional clusters in rs-fMRI data.
authors.affiliation: Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States. Electronic address: sryali@stanford.edu.
authors.forename: Srikanth
authors.initials: S
authors.lastname: Ryali
authors.affiliation: Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States.
authors.forename: Tianwen
authors.initials: T
authors.lastname: Chen
authors.affiliation: Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States.
authors.forename: Aarthi
authors.initials: A
authors.lastname: Padmanabhan
authors.affiliation: Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.affiliation: Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States; Program in Neuroscience, Stanford University School of Medicine, Stanford, CA 94305, United States; Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, United States.
authors.forename: Vinod
authors.initials: V
authors.lastname: Menon
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007257:Information Theory; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D011336:Probability; D012146:Rest
keywords: Consensus clustering; Data clustering; Parcellation; Resting-state fMRI; Segmentation