Biomedical subjects

Marnie E Shaw

Publications and source records attributed to Marnie E Shaw.

3 recordsLinked to original sources

PubMed2003

Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics.

This study investigated the possible benefit of subject specific optimization of preprocessing strategies in functional magnetic resonance imaging (fMRI) experiments. The optimization was performed using the data-driven performance metrics developed recently [Neuroimage 15 (2002), 747]. We applied numerous preprocessing strategies and a multivariate statistical analysis to each of the 20 subjects in our two example fMRI data sets. We found that the optimal preprocessing strategy varied, in general, from subject to subject. For example, in one data set, optimum smoothing levels varied from 16 mm (4 subjects), 10 mm (5 subjects), to no smoothing at all (1 subject). This strongly suggests that group-specific preprocessing schemes may not give optimum results. For both studies, optimizing the preprocessing for each subject resulted in an increased number of suprathresholded voxels in within-subject analyses. Furthermore, we demonstrated that we were able to aggregate the optimized data with a random effects group analysis, resulting in improved sensitivity in one study and the detection of interesting, previously undetected results in the other.

Adult↗

PubMed2002

Abnormal functional connectivity in posttraumatic stress disorder.

This study investigated the efficacy of a combined multivariate/resampling procedure for the analysis of PET activation studies. The covariance-based multivariate analysis was used to investigate distributed brain systems in posttraumatic stress disorder (PTSD) patients and matched controls during performance of a working memory task. The results were compared to univariate results obtained in an earlier study. We also examined whether the PTSD patients demonstrated a breakdown in functional connectivity that may be associated with working memory difficulties often experienced by these patients. A resampling procedure was used specifically to test the reliability of measured between-group effects, to avoid mistaken inference on the basis of random intersubject differences. Significant and reproducible differences in network connectivity were obtained for the two groups. The functional connectivity pattern of the patient group was characterized by relatively more activation in the bilateral inferior parietal lobes and the left precentral gyrus than the control group, and less activation in the inferior medial frontal lobe, bilateral middle frontal gyri and right inferior temporal gyrus. The resampling procedure provided direct evidence that working memory updating was abnormal in PTSD patients relative to matched controls. This work focuses on the need to identify extended brain networks (in addition to regionally specific changes) for the full characterization of brain responses in neuroimaging experiments. Our multivariate analysis explicitly measures the reliability of the patterns of functional connectivity we obtain and demonstrates the potential of such analyses for the study of brain network dysfunction in psychopathology.

Adult↗

PubMed2002

Simulation of the effects of global normalization procedures in functional MRI.

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.

Algorithms↗