Maria Gavrilescu

A number of techniques have been used to provide functional connectivity estimates for a given fMRI data set. In this study we compared two methods: a ‘rest‐like’ method where the functional connectivity was estimated for the whitened residuals after regressing out the task‐induced effects, and a within‐condition method where the functional connectivity was estimated separately for each experimental condition. In both cases four pre‐processing strategies were used: 1) time courses extracted from standard pre‐processed data (standard); 2) adjusted time courses extracted using the volume of interest routines in SPM2 from standard pre‐processed data (spm); 3) time courses extracted from ICA denoised data (standard denoised); and 4) adjusted time courses extracted from ICA denoised data (spm denoised). The temporal correlation between time series extracted from two cortical regions were statistically compared with the temporal correlation between a time series extracted from a cortical region and a time series extracted form a region placed in CSF. Since the later correlation is due to physiological noise and other artifacts, we used this comparison to investigate whether rest‐like and task modulated connectivity could be estimated from the same data set. The pre‐processing strategy had a significant effect on the connectivity estimates with the standard time courses providing larger connectivity values than the spm time courses for both estimation methods. The CSF comparison indicated that for our data set only rest‐like connectivity could be estimated. The rest‐like connectivity values were similar with connectivity estimated from resting state data. Hum Brain Mapp 2008.

We investigated the effects of motion‐correction strategy and time course selection method when structural equation modeling is applied to fMRI data in the presence of task‐correlated motion. Three motion‐correction methods were employed for a group of 12 subjects performing an orthographic lexical retrieval task: (1) a rigid body realignment as implemented in SPM99, (2) a rigid body realignment combined with the inclusion of motion parameters in the statistical model, and (3) the FLIRT motion correction followed by an ICA analysis aiming to identify and remove the motion‐related components and the ghosting artifacts. For each motion correction, the time courses of the activated regions were selected in three ways: (1) using the voxels with the highest Z scores, (2) using the average across all the statistically significant voxels in the region of interest, and (3) using a within‐region, across‐subjects, singular value decomposition. The resulting models of effective connectivity were markedly different, although the activation pattern was not substantially altered by the motion‐correction method. Higher values for the path coefficients were obtained for the models fitted to the covariance matrices based on the average time courses than for the covariance matrices based on a single voxel time course. Our results suggest caution with the interpretation of task‐induced changes in effective connectivity since, for higher‐order cognitive brain functions, multiple models can be fitted to a given data set and these models cannot be rejected on an anatomical or cognitive basis. Hum. Brain Mapping 21:49–63, 2004.

We report on differences in sensitivity and false-positive rate across five methods of global normalization using resting-state fMRI data embedded with simulated activation. These methods were grand mean session scaling, proportional scaling, ANCOVA, a masking method, and an orthogonalization method. We found that global normalization by proportional scaling and ANCOVA decreased the sensitivity of the statistical analysis and induced artifactual deactivation even when the correlation between the global signal and the experimental paradigm was relatively low. The masking method and the orthogonalization method performed better from this perspective but are both restricted to certain experimental conditions. Based on the results of these simulations, we offer practical guidelines for the choice of global normalization method least likely to bias the experimental results.