NeuroImage
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A well-recognized problem with the echo-planar imaging (EPI) technique most commonly used for functional magnetic resonance imaging (fMRI) studies is geometric distortion caused by magnetic field inhomogeneity. This makes it difficult to achieve an accurate registration between a functional activation map calculated from an EPI time series and an undistorted, high resolution anatomical image. A correction method based on mapping the spatial distribution of field inhomogeneities can be used to reduce these distortions. ⋯ We propose that field maps with acceptable noise levels can be generated easily using a dual echo-time EPI sequence and demonstrate the importance of distortion correction for anatomical coregistration, even for small distortions. Using a dual echo-time series to generate a unique field map at each time point, we characterize the interaction between head motion and geometric distortion. However, we suggest that the variance between successively measured field maps introduces additional unwanted variance in the voxel time-series and is therefore not adequate to correct for time-varying distortions.
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This study describes a new technique for Diffusion Tensor Imaging (DTI) that acquires axial (transverse) images of the cervical spinal cord. The DTI images depict axonal fiber orientation, enable quantification of diffusion characteristics along the spinal cord, and have the potential to demonstrate the connectivity of cord white matter tracts. Because of the high sensitivity to motion of diffusion-weighted magnetic resonance imaging and the small size of the spinal cord, a fast imaging method with high in-plane resolution was developed. ⋯ The FA index demonstrated high anisotropy of the spinal cord with an average value of 0.61 +/- 0.05 (highest value of 0.66 +/- 0.03 at C3), comparable to white matter tracts in the brain. The diffusivity components parallel and orthogonal to the longitudinal axes of the cord were lambda( parallel) = (1648 +/- 123) x 10(-6) mm(2)s(-1) and lambda( perpendicular) = (570 +/- 47) x 10(-6) mm(2) s(-1), respectively. The high axial resolution allowed preliminary evaluation of fiber connectivity using the fast-marching tractography algorithm, which generated traces of fiber paths consistent with the well-known cord anatomy.
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The self-diffusion tensor may be calculated from several echo-planar image acquisitions preceded by different diffusion gradients. Unfortunately, these diffusion gradients cause geometric distortion that must be corrected before estimation of the tensor. In the present paper we suggest and implement a method for retrospective correction of these distortions firmly based on a physical model for the diffusion-weighted images. ⋯ We show how this notion can be formalized as a quadratic form thereby facilitating the implementation of a rapid algorithm. In addition, we suggest models for how distortions vary with slice position and gradient direction that allow us to substantially reduce the dimensionality of the parameter space. Our results indicate that we are able to estimate both eddy current-induced distortion and subject movement directly from the data without need of any additional measurements.
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Decision-making in the presence of uncertainty is a complex process that involves both affective and cognitive factors. Both error rate and predictability have been implicated in the process of response selection during decision-making. This study examined the hypothesis that the rate of errors during decision-making differentially affects the activation in prefrontal and cingulate cortex. ⋯ Second, premotor (BA 6) and parahippocampal (BA 36) areas were relatively more active at high error rates, and dorsolateral (BA 9, 46) and inferior prefrontal cortex (BA 44) as well as parietal (BA 40) and cingulate cortex (BA 25, 32) were more active during low error rates. Third, the relationship between the frequency of the dominant strategy underlying decision-making (win-stay/lose-shift) and the activation in the dorsolateral prefrontal cortex and the anterior cingulate was dependent on error rate or outcome predictability. These results support the hypothesis that error rates and predictability affect the activation patterns in the neural systems underlying decision-making because these structures maintain a representation of the reinforcement history for the available response alternatives to select an "optimal strategy."
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Most current analysis methods for fMRI data assume a priori knowledge of the time course of the hemodynamic response (HR) to experimental stimuli or events in brain areas of interest. In addition, they typically assume homogeneity of both the HR and the non-HR "noise" signals, both across brain regions and across similar experimental events. When HRs vary unpredictably, from area to area or from trial to trial, an alternative approach is needed. ⋯ Visualizing sets of BOLD response epochs with novel BOLD-image plots demonstrated that component HRs varied substantially and often systematically across trials as well as across sessions, subjects, and brain areas. Contrary to expectation, in four of the six subjects the V1 component HR contained two positive peaks in response to short-stimulus bursts, while components with nearly identical regions of activity in long-stimulus sessions from the same subjects were associated with single-peaked HRs. Thus, ICA combined with BOLD-image visualization can reveal dramatic and unforeseen HR variations not apparent to researchers analyzing their data with event-related response averaging and fixed HR templates.