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Medical-image Analysis and Statistical Interpretation Lab

Evaluation of Statistical Inference on Empirical Resting State fMRI.

Posted by on Saturday, May 31, 2014 in Neuroimaging.

Xue Yang, Hakmook Kang, Allen T. Newton, Bennett A. Landman, “Evaluation of Statistical Inference on Empirical Resting State fMRI.” IEEE Transactions on Biomedical Engineering. IEEE Trans Biomed Eng. 2014 Apr;61(4):1091-9. PMC24658234†

Full text: https://www.ncbi.nlm.nih.gov/pubmed/24658234

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.

Representative one voxel t-values as data are randomly decimated. An rs-fMRI dataset is randomly diminished into N data size levels with M subsets in each level. Inference methods are applied to each subset to estimate voxel-wise t-maps. The highlighted point (left) indicates the rs-fMRI seed region.
Representative one voxel t-values as data are randomly decimated. An rs-fMRI dataset is randomly diminished into N data size levels with M subsets in each level. Inference methods are applied to each subset to estimate voxel-wise t-maps. The highlighted point (left) indicates the rs-fMRI seed region.