Improvement in executive functioning after Goal-Oriented Attentional Self-Regulation training is associated with reduction in PTSD hyperarousal symptoms among veterans with comorbid PTSD and mild TBI.

Goal-Oriented Attentional Self-Regulation (GOALS) is a cognitive rehabilitation training program that combines mindfulness-based attention regulation with individualized goal management strategies to improve functioning in daily life after traumatic brain injury (TBI). While not a specific target of GOALS training, previous research has indicated improvements in emotional functioning following GOALS training, specifically symptoms related to depression and posttraumatic stress disorder (PTSD). The current study is based on the hypothesis that improvements in cognitive control processes related to executive functioning and attention after GOALS training generalize to improvements in emotional functioning, thereby resulting in reductions in emotional distress. The current study analyzed archival data from 33 Veteran participants with a confirmed diagnosis of PTSD and a history of mild TBI who received either GOALS training or a psychoeducational intervention matched for time, therapist attention, and participation format. Regression analysis was used to assess the strength of the relationship between improvements in Overall Attention/Executive Functioning and decreases in hyperarousal symptoms associated with PTSD. Results from the regression analysis revealed that improvements in Overall Attention/Executive Functioning after GOALS was significantly associated with reductions in hyperarousal symptoms associated with PTSD (R2 = 0.26, F(1,15) = 5.01, ß = -.51, p < .05). The current findings suggest that cognitive improvements after GOALS training may lead to changes in emotional functioning, resulting in decreased emotional distress. This is important, particularly in VA settings, because the results potentially highlight additional areas of research and focus on the treatment of comorbid mild TBI and PTSD among Veterans.

Calculation of the confidence intervals for transformation parameters in the registration of medical images.

Images from different individuals typically cannot be registered precisely because anatomical features within the images differ across the people imaged and because the current methods for image registration have inherent technological limitations that interfere with perfect registration. Quantifying the inevitable error in image registration is therefore of crucial importance in assessing the effects that image misregistration may have on subsequent analyses in an imaging study. We have developed a mathematical framework for quantifying errors in registration by computing the confidence intervals of the estimated parameters (3 translations, 3 rotations, and 1 global scale) for the similarity transformation. The presence of noise in images and the variability in anatomy across individuals ensures that estimated registration parameters are always random variables. We assume a functional relation among intensities across voxels in the images, and we use the theory of nonlinear, least-squares estimation to show that the parameters are multivariate Gaussian distributed. We then use the covariance matrix of this distribution to compute the confidence intervals of the transformation parameters. These confidence intervals provide a quantitative assessment of the registration error across the images. Because transformation parameters are nonlinearly related to the coordinates of landmark points in the brain, we subsequently show that the coordinates of those landmark points are also multivariate Gaussian distributed. Using these distributions, we then compute the confidence intervals of the coordinates for landmark points in the image. Each of these confidence intervals in turn provides a quantitative assessment of the registration error at a particular landmark point. Because our method is computationally intensive, however, its current implementation is limited to assessing the error of the parameters in the similarity transformation across images. We assessed the performance of our method in computing the error in estimated similarity parameters by applying that method to real world dataset. Our results showed that the size of the confidence intervals computed using our method decreased - i.e. our confidence in the registration of images from different individuals increased - for increasing amounts of blur in the images. Moreover, the size of the confidence intervals increased for increasing amounts of noise, misregistration, and differing anatomy. Thus, our method precisely quantified confidence in the registration of images that contain varying amounts of misregistration and varying anatomy across individuals.