The arcuate fasciculus is a white-matter fiber tract that is involved in human language. Here we compared cortical connectivity in humans, chimpanzees and macaques (Macaca mulatta) and found a prominent temporal lobe projection of the human arcuate fasciculus that is much smaller or absent in nonhuman primates. This human specialization may be relevant to the evolution of language.
Our results illustrate that DTI can be used in evaluating the integrity of corpus callosum in alcohol-exposed individuals. If future studies support these findings, diffusion anisotropy, represented by fractional anisotropy, has the potential to be used as a clinical marker in the diagnosis of FAS.
Visual attention problems have been reported in association with prenatal alcohol exposure (PAE). With related behavioral data documented in literature, further investigation of this PAE effect would benefit from integrating functional and anatomical imaging data to ascertain its neurobiological basis. The current study investigated the possible functional and anatomical bases for the PAE-related visual sustained attention deficit. Functional magnetic resonance imaging (fMRI) data were collected while the subjects performed a sustained visual attention task. High resolution, three dimensional anatomical images were also collected for morphometric evaluation. In the alcohol-affected subjects, we observed a significant white and gray matter volume reduction in the occipital-temporal area. Meanwhile, their fMRI activations in the same region resided more superiorly than that of the controls resulting in reduced activation in the ventral occipital-temporal area. The location of this PAE functional abnormality approximately matches that of the significant structural reduction. In addition to the well documented corpus callosum abnormalities observed in PAE subjects, the present results reveal a teratogenic effect on the occipital-temporal area. Furthermore, as the occipital-temporal area plays an important role in visual attention, the current observation suggests a neurobiological underpinning for the PAE related deficit in sustained visual attention.
In this work, the effect of fluid-attenuated inversion recovery (FLAIR) on measured diffusion anisotropy was investigated in gray matter. DTI data were obtained with and without FLAIR in six normal volunteers. The application of FLAIR was experimentally demonstrated to lead to a consistent increase in fractional anisotropy (FA) in gray-matter regions, which was attributed to suppressed partial volume effects from CSF. In addition to these experimental results, Monte Carlo simulations were performed to ascertain the effect of noise on the measured FA under the experimental conditions of this study. The experimentally observed effect of noise was corroborated by the simulation, indicating that the increase in the measured FA was not due to a noise-related bias but to an actual increase in diffusion anisotropy. This enhanced measurement of diffusion anisotropy can be potentially used to differentiate directionally dependent structure and tracking fibers in gray matter. By measuring the diffusion characteristics of water in tissue along at least six noncolinear directions in space, diffusion tensor imaging (DTI) maps the directional dependence of water diffusion and is useful for visualizing microstructural tissue organization in the human brain (1). The advent of DTI in the last decade allows investigators to noninvasively visualize neural fiber tracts in vivo (2,3), which facilitates the study of brain development and the pathology of diseases associated with white-matter damage and disruption. Recent advances in tract-tracking techniques have further advanced the exploration of neural connectivity and neurological pathways in the human brain (4 -7).The application of DTI assumes a parallel relationship between the direction of maximum diffusion (i.e., the principal eigenvector of the diffusion tensor) and the direction of fiber fascicles traversing the imaged voxel. The measurement of diffusion-weighted MR signals, however, suffers from several limitations and is prone to artifacts that compromise accuracy in the derived diffusion tensor. For instance, cerebrospinal fluid (CSF) can degrade diffusion mapping, resulting in an overestimate of the trace of the diffusion tensor (8,9). Therefore, a technique that reduced the inaccuracy caused by CSF contamination in diffusion tensor measurements would allow better tract-tracking in both white and gray matter, especially in gray-matter areas such as the hippocampus, thalamus, and cortex. This would increase our understanding of the brain anatomy of neural fibers, neurological pathways, and disruptions in disease states.Recent studies have suggested that applying fluid-attenuated inversion recovery (FLAIR) to suppress CSF (10) before the diffusion weighting could reduce CSF contamination in the measured diffusion tensor (11,12). Although FLAIR has been used to address CSF contamination in ADC measurements in acute stroke (12) and epilepsy (13), and diffusion anisotropy measurements in white matter (11), the effect of CSF contamination on the measurement of diffusion anis...
In conventional diffusion tensor imaging (DTI) based on magnetic resonance data, each voxel is assumed to contain a single component having diffusion properties that can be fully represented by a single tensor. In spite of its apparent lack of generality, this assumption has been widely used in clinical and research purpose. This resulted in situations where correct interpretation of data was hampered by mixing of components and/or tractography Even though this assumption can be valid in some cases, the general case involves mixing of components resulting in significant deviation from the single tensor model. Hence, a strategy that allows the decomposition of data based on a mixture model has the potential of enhancing the diagnostic value of DTI. This work aims at developing a stable solution for the most general problem of multi-component modeling of diffusion tensor imaging data. This model does not include any assumptions about the nature or volume ratio of any of the components and utilizes a projection pursuit based strategy whereby a combination of exhaustive search and least-squares estimation is used to estimate 1D projections of the solution. Then, such solutions are combined to compute the multidimensional components in a fast and robust manner. The new method is demonstrated by both computer simulations and real diffusion-weighted data. The preliminary results indicate the success of the new method and its potential to enhance the interpretation of DTI data sets.
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