NeuroImage
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Smoothly varying and multiplicative intensity variations within MR images that are artifactual, can reduce the accuracy of automated brain segmentation. Fortunately, these can be corrected. Among existing correction approaches, the nonparametric non-uniformity intensity normalization method N3 (Sled, J. ⋯ NeuroImage 39, 1752-1762.) suggests that its performance on 3 T scanners with multichannel phased-array receiver coils can be improved by optimizing a parameter that controls the smoothness of the estimated bias field. The present study not only confirms this finding, but additionally demonstrates the benefit of reducing the relevant parameter values to 30-50 mm (default value is 200 mm), on white matter surface estimation as well as the measurement of cortical and subcortical structures using FreeSurfer (Martinos Imaging Centre, Boston, MA). This finding can help enhance precision in studies where estimation of cerebral cortex thickness is critical for making inferences.
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Gradient and spin echo (GRE and SE, respectively) weighted magnetic resonance images report on neuronal activity via changes in deoxygenated hemoglobin content and cerebral blood volume induced by alterations in neuronal activity. Hence, vasculature plays a critical role in these functional signals. However, how the different blood vessels (e.g. arteries, arterioles, capillaries, venules and veins) quantitatively contribute to the functional MRI (fMRI) signals at each field strength, and consequently, how spatially specific these MRI signals are remain a source of discussion. ⋯ Furthermore, for SE, using a TE larger than the tissue T(2) enhances micro-vasculature signal relatively, though compromising SNR for spatial specificity. In addition, the intravascular SE MRI signals do not fully disappear even at high field strength as arteriolar and capillary contributions persist. The model, and the physiological considerations presented here can also be applied in contrast agent experiments and to other models, such as calibrated BOLD approach and vessel size imaging.
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Rates of brain atrophy derived from serial magnetic resonance (MR) studies may be used to assess therapies for Alzheimer's disease (AD). These measures may be confounded by changes in scanner voxel sizes. For this reason, the Alzheimer's Disease Neuroimaging Initiative (ADNI) included the imaging of a geometric phantom with every scan. ⋯ We used the registration algorithm to quantify any residual scaling errors, and found the algorithm to be unbiased, with no significant (p=0.97) difference between control (n=79) and AD subjects (n=50), but with a mean (SD) absolute volume change of 0.20 (0.20) % due to linear scalings. 9DOF registration was shown to be comparable to geometric phantom correction in terms of the effect on atrophy measurement and unbiased with respect to disease status. These results suggest that the additional expense and logistic effort of scanning a phantom with every patient scan can be avoided by registration-based scaling correction. Furthermore, based upon the atrophy rates in the AD subjects in this study, sample size requirements would be approximately 10-12% lower with (either) correction for voxel scaling than if no correction was used.
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We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). ⋯ For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.
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Previous studies have reported that the spontaneous, resting-state time course of the default-mode network is negatively correlated with that of the "task-positive network", a collection of regions commonly recruited in demanding cognitive tasks. However, all studies of negative correlations between the default-mode and task-positive networks have employed some form of normalization or regression of the whole-brain average signal ("global signal"); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the task-positive regions could be artifactually induced by global signal pre-processing. ⋯ Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative correlations within task-positive regions at the group-level (p<0.05, uncorrected; no regions at the group level were significant at FDR=0.05). Furthermore, physiological noise correction caused region-specific decreases in positive correlations within the default-mode network, reducing apparent false positives. It was observed that the low-frequency respiratory volume and cardiac rate regressors used within the physiological noise correction algorithm displayed significant (but not total) shared variance with the global signal, and constitute a model-based alternative to correcting for non-neural global noise.