Nonlinear association criterion, nonlinear Granger causality and related issues with applications to neuroimage studies

Journal of Neuroscience Methods
Chenyang Tao, Jianfeng Feng

Abstract

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. 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. 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 ...Continue Reading

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Citations

Sep 22, 2019·Biomedizinische Technik. Biomedical Engineering·Mehdi RajabiounRobert Coben
Nov 23, 2017·Frontiers in Human Neuroscience·Qiang LuoJianfeng Feng

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