Right Fronto-Subcortical White Matter Microstructure Predicts Cognitive Control Ability on the Go/No-go Task in a Community Sample.

Go/no-go tasks are widely used to index cognitive control. This construct has been linked to white matter microstructure in a circuit connecting the right inferior frontal gyrus (IFG), subthalamic nucleus (STN), and pre-supplementary motor area. However, the specificity of this association has not been tested. A general factor of white matter has been identified that is related to processing speed. Given the strong processing speed component in successful performance on the go/no-go task, this general factor could contribute to task performance, but the general factor has often not been accounted for in past studies of cognitive control. Further, studies on cognitive control have generally employed small unrepresentative case-control designs. The present study examined the relationship between go/no-go performance and white matter microstructure in a large community sample of 378 subjects that included participants with a range of both clinical and subclinical nonpsychotic psychopathology. We found that white matter microstructure properties in the right IFG-STN tract significantly predicted task performance, and remained significant after controlling for dimensional psychopathology. The general factor of white matter only reached statistical significance when controlling for dimensional psychopathology. Although the IFG-STN and general factor tracts were highly correlated, when both were included in the model, only the IFG-STN remained a significant predictor of performance. Overall, these findings suggest that while a general factor of white matter can be identified in a young community sample, white matter microstructure properties in the right IFG-STN tract show a specific relationship to cognitive control. The findings highlight the importance of examining both specific and general correlates of cognition, especially in tasks with a speeded component.

Convergent individual differences in visual cortices, but not the amygdala across standard amygdalar fMRI probe tasks.

The amygdala (AMG) has been repeatedly implicated in the processing of threatening and negatively valenced stimuli and multiple fMRI paradigms have reported personality, genetic, and psychopathological associations with individual differences in AMG activation in these paradigms. Yet the interchangeability of activations in these probes has not been established, thus it remains unclear if we can interpret AMG responses on specific tasks as general markers of its reactivity. In this study we aimed to assess if different tasks that have been widely used within the Affective Neuroscience literature consistently recruit the AMG. METHOD: Thirty-two young healthy subjects completed four fMRI tasks that have all been previously shown to probe the AMG during processing of threatening stimuli: the Threat Face Matching (TFM), the Cued Aversive Picture (CAP), the Aversive and Erotica Pictures (AEP) and the Screaming Lady paradigm (SLp) tasks. Contrasts testing response to aversive stimuli relative to baseline or neutral stimuli were generated and correlations between activations in the AMG were calculated across tasks were performed for ROIs of the AMG. RESULTS: The TFM, CAP and AEP, but not the SLp, successfully recruit the AMG, among other brain regions, especially when contrasts were against baseline or nonsocial stimuli. Conjunction analysis across contrasts showed that visual cortices (VisCtx) were also consistently recruited. Correlation analysis between the extracted data for right and left AMG did not yield significant associations across tasks. By contrast, the extracted signal in VisCtx showed significant associations across tasks (range r=0.511-r=0.630). CONCLUSIONS: Three of the four paradigms revealed significant AMG reactivity, but individual differences in the magnitudes of AMG reactivity were not correlated across paradigms. By contrast, VisCtx activation appears to be a better candidate than the AMG as a measure of individual differences with convergent validity across negative emotion processing paradigms.

Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT: A multimodal data archive and processing environment.

The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has developed a database built on XNAT housing over a quarter of a million scans. The database provides framework for (1) rapid prototyping, (2) large scale batch processing of images and (3) scalable project management. The system uses the web-based interfaces of XNAT and REDCap to allow for graphical interaction. A python middleware layer, the Distributed Automation for XNAT (DAX) package, distributes computation across the Vanderbilt Advanced Computing Center for Research and Education high performance computing center. All software are made available in open source for use in combining portable batch scripting (PBS) grids and XNAT servers.

Robust optic nerve segmentation on clinically acquired computed tomography.

The optic nerve (ON) plays a critical role in many devastating pathological conditions. Segmentation of the ON has the ability to provide understanding of anatomical development and progression of diseases of the ON. Recently, methods have been proposed to segment the ON but progress toward full automation has been limited. We optimize registration and fusion methods for a new multi-atlas framework for automated segmentation of the ONs, eye globes, and muscles on clinically acquired computed tomography (CT) data. Briefly, the multi-atlas approach consists of determining a region of interest within each scan using affine registration, followed by nonrigid registration on reduced field of view atlases, and performing statistical fusion on the results. We evaluate the robustness of the approach by segmenting the ON structure in 501 clinically acquired CT scan volumes obtained from 183 subjects from a thyroid eye disease patient population. A subset of 30 scan volumes was manually labeled to assess accuracy and guide method choice. Of the 18 compared methods, the ANTS Symmetric Normalization registration and nonlocal spatial simultaneous truth and performance level estimation statistical fusion resulted in the best overall performance, resulting in a median Dice similarity coefficient of 0.77, which is comparable with inter-rater (human) reproducibility at 0.73.