An improvement on local FDR analysis applied to functional MRI data.
Discovering effective connectivity between brain regions gained a lot of attention recently. A vector autoregressive model is a simple and flexible approach for exploratory structural modeling where the involvement of a large number of brain regions is crucial to avoid confounding. The non-zero coefficients of the VAR model are interpreted as actual effective connectivity between brain regions. Thus methods for a higher correct discovery rate are crucial for neuroscience.



We propose an improved version of the FDR analysis procedure which would be more suitable to fMRI data. The estimates of the VAR coefficients are often not symmetric about 0 with non-zero modes. In this case, we suggest to estimate the null distribution of the estimates which is assumed symmetric about 0 in two steps: use one side of the estimates and then both sides under some condition.



A theoretical argument is provided for the proposed procedure with a theorem and two types of experiments are made. In a simulation experiment, we show via ROC curves improvement over previous methods. We apply the proposed method to analyze real fMRI data with results interpreted in the language of cognitive neuroscience.



The proposed method outperforms the standard method in the simulation experiment with a VAR model of dimension up to 100 over a wide range of sample sizes. The improvement is made in the context of the true positive rate and performance consistency.



The proposed method is more appropriate for analyzing fMRI data with VAR models when the estimates of the VAR coefficients are not symmetric about 0 and have non-zero modes.
Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi, Saitama 3510198, Japan.
Namgil
N
Lee
Department of Psychology, Ewha Womans University, Seoul 03760, South Korea.
Ah-Young
AY
Kim
Department of Neurology, Ewha Womans University, Seoul 03760, South Korea.
Chang-Hyun
CH
Park
Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea. Electronic address: sung-ho.kim@kaist.edu.
Sung-Ho
SH
Kim
D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological
Brodmann area; Granger causality; Hub-correlates; Mixture distribution; Partial correlation coefficient; VAR model