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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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.
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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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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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A powerful, non-invasive technique for estimating and visualizing white matter tracts in the human brain in vivo is white matter fiber tractography that uses magnetic resonance diffusion tensor imaging. The success of this method depends strongly on the capability of the applied tracking algorithm and the quality of the underlying data set. However, DTI-based fiber tractography still lacks standardized validation. ⋯ Second, fMRI-guided DTI fiber tracking was performed to generate DTI-based somatotopic maps, using a standard line propagation and an advanced fast marching algorithm. The results show that the fiber connections reconstructed by the advanced fast marching algorithm are in good agreement with known anatomy, and that the DTI-revealed somatotopy is similar to the fMRI somatotopy. Furthermore, the study illustrates that the combination of fMRI with DTI can supply additional information in order to choose reasonable seed regions for generating functionally relevant networks and to validate reconstructed fibers.