Bootstrap scree tests: a Monte Carlo simulation and applications to published data.

A non-parametric procedure for Cattell's scree test is proposed, using the bootstrap method. Bentler and Yuan developed parametric tests for the linear trend of scree eigenvalues in principal component analysis. The proposed method is for cases where parametric assumptions are not realistic. We define the break in the scree trend in several ways, based on linear slopes defined with two or three consecutive eigenvalues, or all eigenvalues after the k largest. The resulting scree test statistics are evaluated under various data conditions, among which Gorsuch and Nelson's bootstrap CNG performs best and is reasonably consistent and efficient under leptokurtic and skewed conditions. We also examine the bias-corrected and accelerated bootstrap method for these statistics, and the bias correction is found to be too unstable to be useful. Using seven published data sets which Bentler and Yuan analysed, we compare the bootstrap approach to the scree test with the parametric linear trend test.

What accounts for the appeal of complementary/alternative medicine, and what makes complementary/ alternative medicine "alternative"?

The goal of this study was to elucidate the basis for the appeal of complementary/alternative medicine (CAM) and the basis upon which people distinguish between CAM and conventional medicine. Undergraduates (N = 173) rated 19 approaches to the treatment of chronic back pain on 16 rating scales. Data were analyzed via 3-mode factor analysis, which extracted conceptual dimensions common to both the scales and the treatments. A 5-factor solution was judged togive the best description of the raters'perceptions. One of these 5 factors clearly reflected the distinction between conventional versus CAM approaches, and a 2nd factor clearly referred to treatment appeal. The other 3 factors were invasiveness, health care professional versus patient effort, and "druglikeness." To the extent that treatment was seen as a CAM treatment (as opposed to a conventional treatment), it was seen to be more appealing, less invasive, and less druglike. Simple and partial correlations of the dimension weights indicated that both the appeal of CAM and the distinction between CAM and conventional medicine were largely driven by the view that CAM is less invasive than conventional medicine.

Noise reduction in BOLD-based fMRI using component analysis.

Principle Component Analysis (PCA) and Independent Component Analysis (ICA) were used to decompose the fMRI time series signal and separate the BOLD signal change from the structured and random noise. Rather than using component analysis to identify spatial patterns of activation and noise, the approach we took was to identify PCA or ICA components contributing primarily to the noise. These noise components were identified using an unsupervised algorithm that examines the Fourier decomposition of each component time series. Noise components were then removed before subsequent reconstruction of the time series data. The BOLD contrast sensitivity (CS(BOLD)), defined as the ability to detect a BOLD signal change in the presence of physiological and scanner noise, was then calculated for all voxels. There was an increase in CS(BOLD) values of activated voxels after noise reduction as a result of decreased image-to-image variability in the time series of each voxel. A comparison of PCA and ICA revealed significant differences in their treatment of both structured and random noise. ICA proved better for isolation and removal of structured noise, while PCA was superior for isolation and removal of random noise. This provides a framework for using and evaluating component analysis techniques for noise reduction in fMRI.