title: Functional connectivity change as shared signal dynamics.
abstract: BACKGROUND
  An increasing number of neuroscientific studies gain insights by focusing on differences in functional connectivity-between groups, individuals, temporal windows, or task conditions. We found using simulations that additional insights into such differences can be gained by forgoing variance normalization, a procedure used by most functional connectivity measures. Simulations indicated that these functional connectivity measures are sensitive to increases in independent fluctuations (unshared signal) in time series, consistently reducing functional connectivity estimates (e.g., correlations) even though such changes are unrelated to corresponding fluctuations (shared signal) between those time series. This is inconsistent with the common notion of functional connectivity as the amount of inter-region interaction.
  
  
  METHODS
  Simulations revealed that a version of correlation without variance normalization - covariance - was able to isolate differences in shared signal, increasing interpretability of observed functional connectivity change. Simulations also revealed cases problematic for non-normalized methods, leading to a "covariance conjunction" method combining the benefits of both normalized and non-normalized approaches.
  
  
  RESULTS
  We found that covariance and covariance conjunction methods can detect functional connectivity changes across a variety of tasks and rest in both clinical and non-clinical functional MRI datasets.
  
  
  CONCLUSIONS
  We verified using a variety of tasks and rest in both clinical and non-clinical functional MRI datasets that it matters in practice whether correlation, covariance, or covariance conjunction methods are used.
  
  
  CONCLUSIONS
  These results demonstrate the practical and theoretical utility of isolating changes in shared signal, improving the ability to interpret observed functional connectivity change.
authors.affiliation: Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Ave, Newark, NJ 07102, USA. Electronic address: mwcole@mwcole.net.
authors.forename: Michael W
authors.initials: MW
authors.lastname: Cole
authors.affiliation: Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA.
authors.forename: Genevieve J
authors.initials: GJ
authors.lastname: Yang
authors.affiliation: Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, USA; Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: Murray
authors.affiliation: Department of Psychology, University of Ljubljana, Ljubljana, Slovenia.
authors.forename: Grega
authors.initials: G
authors.lastname: Repovš
authors.affiliation: Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA; Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06520, USA.
authors.forename: Alan
authors.initials: A
authors.lastname: Anticevic
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology* / Q000503:physiopathology; D003198:Computer Simulation; D063132:Connectome / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D012559:Schizophrenia / Q000503:physiopathology*
keywords: Functional MRI; Functional connectivity; Resting-state functional connectivity; Schizophrenia; Task functional connectivity