NeuroImage
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This study examines the value of spin-echo-based fMRI for cognitive studies at the main magnetic field strength of 3 T using a spin-echo EPI (SE-EPI) sequence and a Stroop color-word matching task. SE-EPI has the potential advantage over conventional gradient-echo EPI (GE-EPI) that signal losses caused by dephasing through the slice are not present, and hence although image distortion will be the same as for an equivalent GE-EPI sequence, signal voids will be eliminated. The functional contrast in SE-EPI will be lower than for GE-EPI, as static dephasing effects do not contribute. ⋯ The experiments from visual cortex indicated that at 3 T the BOLD signal change has contributions from the extravascular space and larger blood vessels in roughly equal amounts. In comparison with GE-EPI the absence of static dephasing effects would seem to result in a superior intrinsic spatial resolution. In conclusion the sensitivity of SE-EPI at 3 T is sufficient to make it the method of choice for fMR studies that require a high degree of spatial localization or where the requirement is to detect activation in regions affected by strong susceptibility gradients.
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Gradient-echo echo-planar imaging is a standard technique in functional magnetic resonance imaging (fMRI) experiments based on the blood oxygenation level-dependent (BOLD) effect. A major problem is the occurrence of susceptibility gradients near air/tissue interfaces. As a consequence, the detection of neuronal activation may be greatly compromised in certain brain areas, especially in the temporal lobes and in the orbitofrontal cortex. ⋯ It is shown that these gradients influence the effective echo time TE and may reduce considerably the local BOLD sensitivity, even in the case of acceptable image intensities. A compensation method is proposed and tested in an fMRI experiment based on a hypercapnic challenge. The results suggest that the compensation method allows for the detection of activation in brain areas which are usually unavailable for BOLD studies.
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Long-memory noise is common to many areas of signal processing and can seriously confound estimation of linear regression model parameters and their standard errors. Classical autoregressive moving average (ARMA) methods can adequately address the problem of linear time invariant, short-memory errors but may be inefficient and/or insufficient to secure type 1 error control in the context of fractal or scale invariant noise with a more slowly decaying autocorrelation function. Here we introduce a novel method, called wavelet-generalized least squares (WLS), which is (to a good approximation) the best linear unbiased (BLU) estimator of regression model parameters in the context of long-memory errors. ⋯ Compared to ordinary least squares and ARMA-based estimators, WLS is shown to be more efficient and to give excellent type 1 error control. The method is also applied to some real (neurophysiological) data acquired by functional magnetic resonance imaging (fMRI) of the human brain. We conclude that wavelet-generalized least squares may be a generally useful estimator of regression models in data complicated by long-memory or fractal noise.
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This paper summarizes five major themes of discussion stemming from a recent workshop at the University of Toronto. The focus of the workshop was whether the phenomenology of cognition has a direct translation to the biological processes of the brain. The study of this translation is the goal of cognitive neuroscience. ⋯ Despite the consensus on these themes, there are several challenges for the field. Significant obstacles arise from the multidisciplinary nature of cognitive neuroscience, in which terms do not mean the same thing across disciplines (e.g., networks and systems). The imprecision of explanations for cognitive neuroscience data was also seen as a significant problem, suggesting that more principled attempts at explicit model specifications and prediction will be necessary for the field to develop.
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Neurosurgical interventions often require the presurgical determination of language dominance or mapping of language areas. Results obtained by fMRI are closely correlated with invasive procedures such as electrical stimulation mapping or the intracarotid amobarbital test. However, language fMRI is not used routinely, because postprocessing is time-consuming. ⋯ The semantic condition induced almost invariably left hemispheric activations in Broca's area, the premotor cortex, the dorsolateral prefrontal cortex, and the temporoparietal region. Although real-time analysis reduced noise less effectively than SPM99, visual ratings and lateralization indices produced highly concordant results with both methods. In conclusion, real-time fMRI, as used here, allowed reliable language lateralization and mapping in less than 15 min during routine clinical MRI investigation with no need for postprocessing.