ROC methods for evaluation of fMRI techniques

Receiver operating characteristic (ROC) methods provide a standardized and statistically meaningful means for comparing signal‐detection accuracy. A brief overview of ROC methods is presented. Example applications include a comparison of four different postprocessing algorithms operating on simulated fMRI time‐course data sets and on human null data sets to which a simulated fMR response had been added. ROC methods also were used to reanalyze one data set from a previously published work. Additional ROC methods that also may be useful for fMRI comparisons are described.

[1]  D. McClish.  Analyzing a Portion of the ROC Curve , 1989, Medical decision making : an international journal of the Society for Medical Decision Making.

[2]  Jonathan D. Cohen,et al.  Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster‐Size Threshold , 1995, Magnetic resonance in medicine.

[3]  J C Gore,et al.  An roc approach for evaluating functional brain mr imaging and postprocessing protocols , 1995, Magnetic resonance in medicine.

[4]  D. Chakraborty,et al.  Free-response methodology: alternate analysis and a new observer-performance experiment. , 1990, Radiology.

[5]  D. Dorfman,et al.  Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—Rating-method data , 1969 .

[6]  Adrian T. Lee,et al.  Discrimination of Large Venous Vessels in Time‐Course Spiral Blood‐Oxygen‐Level‐Dependent Magnetic‐Resonance Functional Neuroimaging , 1995, Magnetic resonance in medicine.

[7]  Karl J. Friston,et al.  Analysis of functional MRI time‐series , 1994, Human Brain Mapping.

[8]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[9]  D P Chakraborty,et al.  Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data. , 1989, Medical physics.

[10]  J. Hanley.  The Robustness of the "Binormal" Assumptions Used in Fitting ROC Curves , 1988, Medical decision making : an international journal of the Society for Medical Decision Making.

[11]  J. Frahm,et al.  Functional MRI of human brain activation at high spatial resolution , 1993, Magnetic resonance in medicine.

[12]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[13]  R. S. Hinks,et al.  Time course EPI of human brain function during task activation , 1992, Magnetic resonance in medicine.

[14]  C. Metz.  ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[15]  E C Wong,et al.  Processing strategies for time‐course data sets in functional mri of the human brain , 1993, Magnetic resonance in medicine.