http://www.nitrc.org/forum/forum.php?forum_id=5717 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Sensitivity Enhancement of Task-evoked fMRI using Ensemble Empirical Mode Decomposition.</b></p> <p>J Neurosci Methods. 2015 Oct 30;</p> <p>Authors: Lin SN, Lin GH, Tsai PJ, Hsu AL, Lo MT, Yang AC, Lin CP, Wu CW</p> <p>Abstract<br/> BACKGROUND: Functional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience.<br/> NEW METHOD: To deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise.<br/> RESULTS: We revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD.<br/> COMPARISON WITH EXISTING METHOD(S): Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t-score distribution.<br/> CONCLUSIONS: We found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.<br/> </p><p>PMID: 26523767 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Mon, 04 May 2026 5:14:53 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator