Correlation In Group Research Articles

Non-invasive prediction of the tumor growth rate using advanced diffusion models in head and neck squamous cell carcinoma patients.

We assessed parameters of advanced diffusion weighted imaging (DWI) models for the prediction of the tumor growth rate in 55 head and neck squamous cell carcinoma (HNSCC) patients. The DWI acquisition used single-shot spin-echo echo-planar imaging with 12 b-values (0−2000). We calculated 14 DWI parameters using mono-exponential, bi-exponential, tri-exponential, stretched exponential and diffusion kurtosis imaging models. We directly measured the tumor growth rate from two sets of different-date imaging data. We divided the patients into a discovery group (n = 40) and validation group (n = 15) based on their MR acquisition dates. In the discovery group, we performed univariate and multivariate regression analyses to establish the multiple regression equation for the prediction of the tumor growth rate using diffusion parameters. The equation obtained with the discovery group was applied to the validation group for the confirmation of the equation's accuracy. After the univariate and multivariate regression analyses in the discovery-group patients, the estimated tumor growth rate equation was established by using the significant parameters of intermediate diffusion coefficient D2 and slow diffusion coefficient D3 obtained by the tri-exponential model. The discovery group's correlation coefficient between the estimated and directly measured tumor growth rates was 0.74. In the validation group, the correlation coefficient (r = 0.66) and intra-class correlation coefficient (0.65) between the estimated and directly measured tumor growth rates were respectively good. In conclusion, advanced DWI model parameters can be a predictor for determining HNSCC patients’ tumor growth rate.

A novel group analysis for functional MRI of the human brain based on a two-threshold correlation (TTC) method

This work presents a new group analysis for functional MRI of human brain activation. The two-threshold correlation (TTC) method determines two statistical thresholds by estimating the noise distribution underlying the summed histogram of correlation coefficients (CC) from all sections and subjects. The probabilistic CC thresholds ( p < 0.0001 for the identification of highly significant activation centers and p < 0.05 for limiting the iterative addition of directly neighboring voxels to these centers) are applied to the group CC maps for each section. These maps may be reconstructed by taking the maximum (MAX) or mean (MEAN) CC value of all subjects for a particular voxel. Experimental analyses involved functional echo-planar imaging of sequential finger-to-thumb opposition and silent word generation at 3 T (eight subjects). Preprocessing included motion correction, spatial filtering, and normalization to MNI space. While the results for the TTC MAX approach were very similar to those obtained for a standard SPM analysis, the TTC MEAN approach turned out to be more conservative emphasizing voxels that are activated in most rather than in only a few subjects. The new method is simple, fast, and robust by linking two thresholds in a physiologically meaningful manner.

A Note on a Common Error in Attraction and Marriage Research

A recent article by Centers (1975b) illustrates an error consistently made by many researchers. Those who immediately come to mind are Blazer, 1963; 1 Centers, 1975a; and Murstein, 1961.2 A more extensive survey of the literature would increase this list considerably. The error has to do with the attempt to establish a control group to compare against an actual group's correlation. For example, Centers (1975b), correlates men's and women's scores who were in different relation-