Name
Abstract | ||
|---|---|---|
Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional magnetic resonance imaging (rs-fMRI) connectivity analysis through more realistic assumptions. In simulation, the advantages of such methods are readily demonstrable. However, quantitative empirical validation remains elusive in vivo as the true connectivity patterns are unknown and noise distributions are challenging to characterize, especially in ultra-high field (e.g., 7T fMRI). Though the physiological characteristics of the fMRI signal are difficult to replicate in controlled phantom studies, it is critical that the performance of statistical techniques be evaluated. The SIMulation EXtrapolation (SIMEX) method has enabled estimation of bias with asymptotically consistent estimators on empirical finite sample data by adding simulated noise . To avoid the requirement of accurate estimation of noise structure, the proposed quantitative evaluation approach leverages the theoretical core of SIMEX to study the properties of inference methods in the face of diminishing data (in contrast to increasing noise). The performance of ordinary and robust inference methods in simulation and empirical rs-fMRI are compared using the proposed quantitative evaluation approach. This study provides a simple, but powerful method for comparing a proxy for inference accuracy using empirical data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TBME.2013.2294013 | IEEE Trans. Biomed. Engineering |
Keywords | Field | DocType |
sensitivity,statistical parametric mapping,simex,resilience,inference mechanisms,statistical analysis,noise distributions,resampling,empirical resting state fmri,empirical finite sample data,extrapolation,simulation extrapolation method,biomedical mri,image sampling,functional magnetic resonance imaging,rs-fmri,statistical inference,phantoms,connectivity analysis,medical image processing,validation,specificity,functional magnetic resonance imaging (fmri) connectivity analysis,phantom,noise,estimation,data models,robustness,magnetic resonance imaging,correlation,monte carlo methods | Computer science,Imaging phantom,Resting state fMRI,Statistical inference,Artificial intelligence,Replicate,Computer vision,Functional magnetic resonance imaging,Pattern recognition,Inference,Extrapolation,Machine learning,Estimator | Journal |
Volume | Issue | ISSN |
61 | 4 | 1558-2531 |
Citations | PageRank | References |
3 | 0.38 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue Yang | 1 | 19 | 3.78 |
| Hakmook Kang | 2 | 11 | 4.41 |
| Allen Newton | 3 | 17 | 4.37 |
| Bennett A. Landman | 4 | 700 | 74.20 |