Rajesh Ranjan Nandy

Receiver operating characteristic (ROC) methods are useful tools for evaluating the sensitivity and specificity of various postprocessing algorithms used in fMRI data analysis. New ROC methods using real fMRI data are proposed that improve a previously introduced method by Le and Hu (Le and Hu, NMR Biomed 1997;10:160-164). The proposed methods provide more accurate means of estimating the true ROC curve from real data and thereby aid in the comparative evaluation of a wide range of postprocessing tools in fMRI. The mathematical relationships between different ROC curves are explored for a comparison of different ROC methods. Examples using real and simulated data are provided to illustrate the ideas involved.

The contrast-to-noise ratio (CNR) is often very low in fMRI data, and standard univariate methods suffer from a loss of sensitivity in the context of noise. The increased power of a multivariate statistical analysis method known as canonical correlation analysis (CCA) in fMRI studies with low CNR was established previously. However, CCA in its conventional form has weak spatial specificity. In this work we propose a new assignment scheme to rectify this problem. It is shown that the new method has improved spatial specificity as well as sensitivity compared to conventional CCA for detecting activation patterns in fMRI. Magn Reson Med 52:947–952, 2004.

Detection of activation in functional MRI (fMRI) is often complicated by the low contrast-to-noise ratio (CNR) in the data. The primary source of the difficulty is the fact that for activities that are subtle the signal can be hidden inside the inherent noise in the data. Classical univariate methods based on t-test or F-test are susceptible to noise, as they fail to harness systematic correlations in evoked responses within neighboring voxels. Here the power of a multivariate statistical analysis tool known as canonical correlation analysis (CCA) in fMRI studies is demonstrated where the CNR is low. As a further illustration of the power of the method, a comparative study of CCA and ordinary correlation analysis using simulated data under various noise levels is also performed. A novel nonparametric approach is introduced to calculate the P-values from the distribution of the complicated test statistic. The circumstances under which CCA is a better performer as well as when it is not the case are discussed. As an example, this method is applied to detect hippocampal activation from memory-related tasks. Magn Reson Med 50:354–365, 2003.