title: Can Patel's τ accurately estimate directionality of connections in brain networks from fMRI?
abstract: Investigating directional interactions between brain regions plays a critical role in fully understanding brain function. Consequently, multiple methods have been developed for noninvasively inferring directional connectivity in human brain networks using functional MRI (fMRI). Recent simulations by Smith et al. showed that Patel's τ, a method based on higher-order statistics, was the best approach for inferring directional interactions from fMRI. Because simulations make restrictive assumptions about reality, we set out to verify this finding using experimental fMRI data obtained from a three-region network in a rat model with electrophysiological validation.
  
  
  
  Previous studies have shown that dynamic causal modeling can correctly estimate the directionality of this three-region network; Granger causality can also work after the deconvolution of the hemodynamic response. Therefore, we set out to test the hypothesis that Patel's τ obtained from either raw or deconvolved fMRI data should correctly estimate the directionality of neuronal influences obtained from intracerebral electroencephalogram in this network.
  
  
  
  Our results indicate that the accuracy of network directionality estimated using Patel's τ was not better than chance.
  
  
  
  First, our results highlight the necessity of experimental validation of simulation results. Second, it is unclear which brain mechanism is modeled by a directionality inferred from Patel's τ. Third, this study shows that a directional connection ascertained by different methods may mean different things and more experimental studies are needed for investigating the neuronal mechanisms underlying the direction of a connection in the brain ascertained by fMRI using different methods. M Magn Reson Med 78:2003-2010, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
authors.affiliation: AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA.
authors.forename: Yunzhi
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Univ. Grenoble Alpes, F-38000, Grenoble, France.|Inserm, U1216, Grenoble Institute of Neuroscience, F-38000, Grenoble, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: David
authors.affiliation: Department of Bioengineering, University of California, Riverside, California, USA.
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Hu
authors.affiliation: AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA.|Department of Psychology, Auburn University, Auburn, Alabama, USA.|Alabama Advanced Imaging Consortium, Auburn University and University of Alabama Birmingham, Alabama, USA.
authors.forename: Gopikrishna
authors.initials: G
authors.lastname: Deshpande
mesh_terms: D000818:Animals; D001921:Brain / Q000000981:diagnostic imaging* / Q000502:physiology; D003198:Computer Simulation; D004569:Electroencephalography; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000000981:diagnostic imaging* / Q000502:physiology; D051381:Rats
keywords: GAERS; Patel's τ; brain networks; effective connectivity; electrophysiological validation; higher-order statistics