NeuroImage
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Comparative Study
Evaluation of automated techniques for the quantification of grey matter atrophy in patients with multiple sclerosis.
Several methods exist and are frequently used to quantify grey matter (GM) atrophy in multiple sclerosis (MS). Fundamental to all available techniques is the accurate segmentation of GM in the brain, a difficult task confounded even further by the pathology present in the brains of MS patients. ⋯ Results demonstrate that, although the algorithms perform similarly to manual segmentations of cortical GM, severe shortcomings are present in the segmentation of deep GM structures. This deficiency is particularly relevant given the current interest in the role of GM in MS and the numerous reports of atrophy in deep GM structures.
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Functional magnetic resonance imaging with readouts at multiple echo times is useful for optimizing sensitivity across a range of tissue T2* values as well as for quantifying T2*. With single-shot acquisitions, both the minimum TE value and the number of TEs which it is possible to collect within a single TR are limited by the long echo-planar imaging readout duration (20-40 ms). In the present work, a multi-shot 3D radial acquisition which allows rapid whole-brain imaging at a range of echo times is proposed. ⋯ It is demonstrated that whole-brain images at 5 echo times (TEs from 10 to 46 ms) can be acquired at a temporal rate as rapid as 400 ms/volume (3.75 mm isotropic resolution). Activation maps for a simultaneous motor/visual task consistent across multiple acceleration factors are obtained. Weighted combination of the echoes results in Z-scores that are significantly (p=0.016) higher than those resulting from any of the individual echo time images.
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The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. ⋯ By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI.
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A least angle regression method for fMRI activation detection in phase-encoded experimental designs.
This paper presents a new regression method for functional magnetic resonance imaging (fMRI) activation detection. Unlike general linear models (GLM), this method is based on selecting models for activation detection adaptively which overcomes the limitation of requiring a predefined design matrix in GLM. This limitation is because GLM designs assume that the response of the neuron populations will be the same for the same stimuli, which is often not the case. ⋯ This paper found that GLM with fixed design matrix was inferior compared to the described LARS method for fMRI activation detection in a phased-encoded experimental design. In addition, the proposed method has the advantage of increasing the degrees of freedom in the regression analysis. We conclude that the method described provides a new and novel approach to the detection of fMRI activation which is better than GLM based analyses.
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We examined age-related changes in the cerebral white matter. Structural magnetic resonance images (MRIs) and diffusion tensor images (DTIs) were acquired from 69 healthy subjects aged 22-84 years. Quantitative DTI tractography was performed for nine different white matter tracts to determine tract volume, fractional anisotropy (FA), mean diffusivity (MD), axial, and radial diffusivities. ⋯ The results showed significant effects of aging on WM, GM, CSF volumes, and selective effects of aging on structural integrity of different white matter tracts. WM of the prefrontal region was the most vulnerable to aging, while temporal lobe connections, cingulum, and parieto-occipital commissural connections showed relative preservation with age. This study was cross-sectional, and therefore, additional longitudinal studies are needed to confirm our findings.