NeuroImage
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Understanding how ageing affects brain structure is an important challenge for medical science. By allowing segmentation of fasciculi-of-interest from diffusion magnetic resonance imaging (dMRI) data, tractography provides a promising tool for assessing white matter connectivity in old age. However, the output from tractography algorithms is usually strongly dependent on the subjective location of user-specified seed points, with the result that it can be both difficult and time consuming to identify the same tract reliably in cross-sectional studies. ⋯ For the fasciculi investigated (genu and splenium of corpus callosum, cingulum cingulate gyri, corticospinal tracts and uncinate fasciculi), PNT was able to provide anatomically plausible representations of the tract in question in 70 to 90% of subjects compared with 2.5 to 60% if single seed points were simply transferred directly from standard to native space. In corpus callosum genu there was a significant negative correlation between a PNT-derived measure of tract shape similarity to a young brain reference tract and age, and a trend towards a significant negative correlation between tract-averaged fractional anisotropy and age; results that are consistent with previous dMRI studies of normal ageing. These data show that it is possible automatically to segment comparable tracts in the brains of older subjects using single seed point tractography, if the seed point is carefully chosen.
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A noninvasive technique for assessing tumor tissue characteristics is required to assist preoperative surgical planning for malignant brain tumors. Preoperative information on tumor cell density within a tumor would help better define the target for tumor biopsy, resulting in more accurate diagnosis and grading of malignant brain tumors. One possible source of this information is diffusion tensor imaging (DTI), although to date studies have focused on its ability to delineate white matter fiber tracks by fiber-tracking and to detect tumor infiltration around the tumor and normal white matter interface. ⋯ Similar correlation was observed between the Ki-67 labeling index and FA (R=0.71) and MD (R=0.62). Thus, measurement of both FA and MD within the tumor core has a potential to provide detailed information on tumor cell density within the tumor. Although data obtained from DTI should be interpreted carefully and comprehensively with other imaging modalities such as positron emission tomography, DTI seems to be informative for planning the best biopsy target containing the highest cell density.
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In functional brain mapping, pattern recognition methods allow detecting multivoxel patterns of brain activation which are informative with respect to a subject's perceptual or cognitive state. The sensitivity of these methods, however, is greatly reduced when the proportion of voxels that convey the discriminative information is small compared to the total number of measured voxels. To reduce this dimensionality problem, previous studies employed univariate voxel selection or region-of-interest-based strategies as a preceding step to the application of machine learning algorithms. ⋯ Furthermore, we apply our method to high resolution (2 x 2 x 2 mm(3)) data from an auditory fMRI experiment in which subjects were stimulated with sounds from four different categories. With these real data, our recursive algorithm proves able to detect and accurately classify multivoxel spatial patterns, highlighting the role of the superior temporal gyrus in encoding the information of sound categories. In line with the simulation results, our method outperforms univariate statistical analysis and statistical learning without feature selection.
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Probability mapping of connectivity is a powerful tool to determine the fibre structure of white matter in the brain. Probability maps are related to the degree of connectivity to a chosen seed area. In many applications, however, it is necessary to isolate a fibre bundle that connects two areas. ⋯ By combining two of these extended visiting maps arising from different seed points, two independent parameters are determined for each voxel: the first quantifies the uncertainty that a voxel is connected to both seed points; the second represents the directional information and estimates the proportion of fibres running in the direction of the other seed point (connecting fibre) or face a third area (merging fibre). Both parameters are used to calculate the probability that a voxel is part of the bundle connecting both seed points. The performance and limitations of this DTI-based method are demonstrated using simulations as well as in vivo measurements.
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Comparative Study
A tractography comparison between turboprop and spin-echo echo-planar diffusion tensor imaging.
The development of accurate, non-invasive methods for mapping white matter fiber-tracts is of critical importance. However, fiber-tracking is typically performed on diffusion tensor imaging (DTI) data obtained with echo-planar-based imaging techniques (EPI), which suffer from susceptibility-related image artifacts, and image warping due to eddy-currents. Thus, a number of white matter fiber-bundles mapped using EPI-based DTI data are distorted and/or terminated early. ⋯ Thus, Turboprop may be a more appropriate DTI data acquisition technique for tracing white matter fibers near regions with significant magnetic susceptibility differences, as well as in longitudinal studies of such fibers. However, the intra-session reproducibility of tractography results was higher for EPI-based than Turboprop DTI data. Thus, EPI-based DTI may be more advantageous for tracing fibers minimally affected by field inhomogeneities.