title: Nonlinear association criterion, nonlinear Granger causality and related issues with applications to neuroimage studies.
abstract: BACKGROUND
  Quantifying associations in neuroscience (and many other scientific disciplines) is often challenged by high-dimensionality, nonlinearity and noisy observations. Many classic methods have either poor power or poor scalability on data sets of the same or different scales such as genetical, physiological and image data.
  
  
  METHODS
  Based on the framework of reproducing kernel Hilbert spaces we proposed a new nonlinear association criteria (NAC) with an efficient numerical algorithm and p-value approximation scheme. We also presented mathematical justification that links the proposed method to related methods such as kernel generalized variance, kernel canonical correlation analysis and Hilbert-Schmidt independence criteria. NAC allows the detection of association between arbitrary input domain as long as a characteristic kernel is defined. A MATLAB package was provided to facilitate applications.
  
  
  RESULTS
  Extensive simulation examples and four real world neuroscience examples including functional MRI causality, Calcium imaging and imaging genetic studies on autism [Brain, 138(5):13821393 (2015)] and alcohol addiction [PNAS, 112(30):E4085-E4093 (2015)] are used to benchmark NAC. It demonstrates the superior performance over the existing procedures we tested and also yields biologically significant results for the real world examples.
  
  
  CONCLUSIONS
  NAC beats its linear counterparts when nonlinearity is presented in the data. It also shows more robustness against different experimental setups compared with its nonlinear counterparts.
  
  
  CONCLUSIONS
  In this work we presented a new and robust statistical approach NAC for measuring associations. It could serve as an interesting alternative to the existing methods for datasets where nonlinearity and other confounding factors are present.
authors.affiliation: Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, 200433, PR China; Department of Computer Science, Warwick University, Coventry CV4 7AL, UK.
authors.forename: Chenyang
authors.initials: C
authors.lastname: Tao
authors.affiliation: Centre for Computational Systems Biology and School of Mathematical Sciences, Fudan University, Shanghai, 200433, PR China; Department of Computer Science, Warwick University, Coventry CV4 7AL, UK; School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai 200433, PR China. Electronic address: jianfeng64@gmail.com.
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D059906:Neuroimaging / Q000379:methods*; D017711:Nonlinear Dynamics
keywords: Canonical correlation analysis; Granger causality; Nonlinear association; Parametric approximation of p-value; Permutation; Regularization; Reproducing kernel Hilbert space; Variable selection