NeuroImage
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Semantic dementia (SD) is a rare clinical syndrome, assigned to the group of frontotemporal lobar degenerations (FTLD). Histopathological analysis has not revealed the deposition of amyloid plaques in the majority of SD cases, in contrast to dementia of the Alzheimer type (AD). However, based on clinical examination alone a reliable differentiation of the underlying pathology cannot be guaranteed, i.e. ⋯ This difference in amyloid plaque deposition could be reproduced in direct statistical comparison of AD and SD and clearly extended the metabolic differences between the patient groups. These findings support the notion that SD can be diagnosed in vivo as a separate entity from AD using amyloid plaque imaging. In general, amyloid plaque PET may complement neuropsychological assessment regarding reliable differential diagnosis of AD and FTLD dementias based on characterization of underlying pathology and may improve the definition of individual prognosis and the selection of patients for scientific trials.
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The optic chiasm with its complex fiber micro-structure is a challenge for diffusion tensor models and tractography methods. Likewise, it is an ideal candidate for evaluation of diffusion tensor imaging tractography approaches in resolving inter-regional connectivity because the macroscopic connectivity of the optic chiasm is well known. Here, high-resolution (156 microm in-plane) diffusion tensor imaging of the human optic chiasm was performed ex vivo at ultra-high field (9.4 T). ⋯ Errors made by the tractography algorithm at high resolution were shown to increase at lower resolutions closer to those used in vivo. This study shows that increases in resolution, made possible by higher field strengths, improve the accuracy of DTI-based tractography. More generally, post-mortem investigation of fixed tissue samples with diffusion imaging at high field strengths is important in the evaluation of MR-based diffusion models and tractography algorithms.
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Comparative Study
Analyzing consistency of independent components: an fMRI illustration.
Independent component analysis (ICA) is a powerful data-driven signal processing technique. It has proved to be helpful in, e.g., biomedicine, telecommunication, finance and machine vision. Yet, some problems persist in its wider use. ⋯ Related methods have been recently published either for analyzing algorithmic stability or reducing the variability. The presented approach targets the extraction of additional information related to the independent components, by focusing on the nature of the variability. Practical implications are illustrated through a functional magnetic resonance imaging (fMRI) experiment.
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Q-ball imaging has the ability to discriminate multiple intravoxel fiber populations within regions of complex white matter architecture. This information can be used for fiber tracking; however, diffusion MR is susceptible to noise and multiple other sources of uncertainty affecting the measured orientation of fiber bundles. The proposed residual bootstrap method utilizes a spherical harmonic representation for high angular resolution diffusion imaging (HARDI) data in order to estimate the uncertainty in multimodal q-ball reconstructions. ⋯ The residual bootstrap method was then used in combination with q-ball imaging to construct a probabilistic streamline fiber tracking algorithm. The residual bootstrap q-ball fiber tracking algorithm is capable of following the corticospinal tract and corpus callosum through regions of crossing white matter tracts in the centrum semiovale. This fiber tracking algorithm is an improvement upon prior diffusion tensor methods and the q-ball data can be acquired in a clinically feasible time frame.
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Curvilinear reformatting of three-dimensional (3D) MRI data of the cerebral cortex is a well-established tool which improves the display of the gyral structure, permits a precise localization of lesions, and helps to identify subtle abnormalities difficult to detect in planar slices due to the brain's complex convolutional pattern. However, the method is time consuming because it requires interactive manual delineation of the brain surface contour. Therefore, a novel technique for automatic curvilinear reformatting is presented. ⋯ Compared to cross-sectional images, curvilinear reformatting offers a markedly superior visualization of topographic relations between lesions and cortical structures, helps to detect subtle cortical malformations and to assess the spatial extent of lesions, thus allowing a better planning of neurosurgical procedures. Compared to alternative methods, it is largely based on freely available software and does not require observer-dependent manual input. In conclusion, we present a simple, easy-to-use and fully automated method for curvilinear reformatting of 3D MRI.