http://www.nitrc.org/forum/forum.php?forum_id=6177 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>An Improvement on Local FDR Analysis Applied to Functional MRI Data.</b></p> <p>J Neurosci Methods. 2016 Apr 18;</p> <p>Authors: Lee N, Kim AY, Park CH, Kim SH</p> <p>Abstract<br/> BACKGROUND: 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.<br/> NEW METHOD: 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.<br/> RESULTS: 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.<br/> 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.<br/> CONCLUSIONS: 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.<br/> </p><p>PMID: 27102044 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Mon, 04 May 2026 17:46:29 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator