Displaying 1 - 2 of 2
Motion correction of fMRI data is a widely used step prior to data analysis. In this study, a comparison of the motion correction tools provided by several leading fMRI analysis software packages was performed, including AFNI, AIR, BrainVoyager, FSL, and SPM2. Comparisons were performed using data from typical human studies as well as phantom data. The identical reconstruction, preprocessing, and analysis steps were used on every data set, except that motion correction was performed using various configurations from each software package. Each package was studied using default parameters, as well as parameters optimized for speed and accuracy. Forty subjects performed a Go/No-go task (an event-related design that investigates inhibitory motor response) and an N-back task (a block-design paradigm investigating working memory). The human data were analyzed by extracting a set of general linear model (GLM)-derived activation results and comparing the effect of motion correction on thresholded activation cluster size and maximum t value. In addition, a series of simulated phantom data sets were created with known activation locations, magnitudes, and realistic motion. Results from the phantom data indicate that AFNI and SPM2 yield the most accurate motion estimation parameters, while AFNI's interpolation algorithm introduces the least smoothing. AFNI is also the fastest of the packages tested. However, these advantages did not produce noticeably better activation results in motion-corrected data from typical human fMRI experiments. Although differences in performance between packages were apparent in the human data, no single software package produced dramatically better results than the others. The "accurate" parameters showed virtually no improvement in cluster t values compared to the standard parameters. While the "fast" parameters did not result in a substantial increase in speed, they did not degrade the cluster results very much either. The phantom and human data indicate that motion correction can be a valuable step in the data processing chain, yielding improvements of up to 20% in the magnitude and up to 100% in the cluster size of detected activations, but the choice of software package does not substantially affect this improvement.
Some individuals are endowed with a biology that renders them more reactive to novelty and potential threat. When extreme, this anxious temperament (AT) confers elevated risk for the development of anxiety, depression and substance abuse. These disorders are highly prevalent, debilitating and can be challenging to treat. The high-risk AT phenotype is expressed similarly in children and young monkeys and mechanistic work demonstrates that the central (Ce) nucleus of the amygdala is an important substrate. Although it is widely believed that the flow of information across the structural network connecting the Ce nucleus to other brain regions underlies primates' capacity for flexibly regulating anxiety, the functional architecture of this network has remained poorly understood. Here we used functional magnetic resonance imaging (fMRI) in anesthetized young monkeys and quietly resting children with anxiety disorders to identify an evolutionarily conserved pattern of functional connectivity relevant to early-life anxiety. Across primate species and levels of awareness, reduced functional connectivity between the dorsolateral prefrontal cortex, a region thought to play a central role in the control of cognition and emotion, and the Ce nucleus was associated with increased anxiety assessed outside the scanner. Importantly, high-resolution 18-fluorodeoxyglucose positron emission tomography imaging provided evidence that elevated Ce nucleus metabolism statistically mediates the association between prefrontal-amygdalar connectivity and elevated anxiety. These results provide new clues about the brain network underlying extreme early-life anxiety and set the stage for mechanistic work aimed at developing improved interventions for pediatric anxiety.