An improvement on local FDR analysis applied to functional MRI data

- Vector autoregressive (VAR) model for effective connectivity analysis.
- Estimate of the null distribution of partial correlation coefficients (PCC's) based on less contaminated (by alternative distributions) part of estimated PCC's.
- The estimation procedure of the null distribution is in two steps: first based on one side of the estimated PCC's and then based on both sides under some condition.
- Improvement in the context of the true positive rate and performance consistency.

BackgroundDiscovering 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.New methodWe 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.ResultsA 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.Comparison with existing method(s)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.ConclusionsThe 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.