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Medical-image Analysis and Statistical Interpretation Lab

Quantitative Evaluation of Statistical Inference in Resting State Functional MRI

Posted by on Sunday, September 30, 2012 in News.

Xue Yang and Bennett A. Landman. “Quantitative Evaluation of Statistical Inference in Resting State Functional MRI”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Nice, France, September 2012 (32% acceptance rate)

Full text: https://www.ncbi.nlm.nih.gov/pubmed/23286055

Abstract

Modern statistical inference techniques may be able to improve the sensitivity and specificity of resting state functional MRI (rs-fMRI) connectivity analysis through more realistic characterization of distributional assumptions. In simulation, the advantages of such modern methods are readily demonstrable. However quantitative empirical validation remains elusive in vivo as the true connectivity patterns are unknown and noise/artifact distributions are challenging to characterize with high fidelity. Recent innovations in capturing finite sample behavior of asymptotically consistent estimators (i.e., SIMulation and EXtrapolation – SIMEX) have enabled direct estimation of bias given single datasets. Herein, we leverage the theoretical core of SIMEX to study the properties of inference methods in the face of diminishing data (in contrast to increasing noise). The stability of inference methods with respect to synthetic loss of empirical data (defined as resilience) is used to quantify the empirical performance of one inference method relative to another. We illustrate this new approach in a comparison of ordinary and robust inference methods with rs-fMRI.

Resilience features capture the stability of an inference method to data decimation. An rs-fMRI dataset (1) is temporally decimated into subsets; each inference method (2) is applied independently to each subset; voxel-wise statistics (3) are estimated; and the parameter maps (4) capture spatial dependencies.
Resilience features capture the stability of an inference method to data decimation. An rs-fMRI dataset (1) is temporally decimated into subsets; each inference method (2) is applied independently to each subset; voxel-wise statistics (3) are estimated; and the parameter maps (4) capture spatial dependencies.

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