Yu-Chien Wu

The Representation of Biological Classes in the Human Brain

Evidence of category specificity from neuroimaging in the human visual system is generally limited to a few relatively coarse categorical distinctions—e.g., faces versus bodies, or animals versus artifacts—leaving unknown the neural underpinnings of fine-grained category structure within these large domains. Here we use fMRI to explore brain activity for a set of categories within the animate domain, including six animal species—two each from three very different biological classes: primates, birds, and insects. Patterns of activity throughout ventral object vision cortex reflected the biological classes of the stimuli. Specifically, the abstract representational space—measured as dissimilarity matrices defined between species-specific multivariate patterns of brain activity—correlated strongly with behavioral judgments of biological similarity of the same stimuli. This biological class structure was uncorrelated with structure measured in retinotopic visual cortex, which correlated instead with a dissimilarity matrix defined by a model of V1 cortex for the same stimuli. Additionally, analysis of the shape of the similarity space in ventral regions provides evidence for a continuum in the abstract representational space—with primates at one end and insects at the other. Further investigation into the cortical topography of activity that contributes to this category structure reveals the partial engagement of brain systems active normally for inanimate objects in addition to animate regions.

Diffusion tensor eigenvector directional color imaging patterns in the evaluation of cerebral white matter tracts altered by tumor

To categorize the varied appearances of tumor‐altered white matter (WM) tracts on diffusion tensor eigenvector directional color maps.

Computation of Diffusion Function Measures in

The distribution of water diffusion in biological tissues may be estimated by a 3-D Fourier transform (FT) of diffusion-weighted measurements in q-space. In this study, methods for estimating diffusion spectrum measures (the zero-displacement probability, the mean-squared displacement, and the orientation distribution function) directly from the q-space signals are described. These methods were evaluated using both computer simulations and hybrid diffusion imaging (HYDI) measurements on a human brain. The HYDI method obtains diffusion-weighted measurements on concentric spheres in q-space. Monte Carlo computer simulations were performed to investigate effects of noise, q-space truncation, and sampling interval on the measures. This new direct computation approach reduces HYDI data processing time and image artifacts arising from 3-D FT and regridding interpolation. In addition, it is less sensitive to the noise and q-space truncation effects than conventional approach. Although this study focused on data using the HYDI scheme, this computation approach may be applied to other diffusion sampling schemes including Cartesian diffusion spectrum imaging.

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Diffusion tensor imaging has been widely used to study brain diseases, disorders, development, and aging. However, few studies have explored the effects of aging on diffusion imaging measures with higher b values. Further, the water diffusion in biological tissues appears biexponential, although this also has not been explored with aging. In this study, hybrid diffusion imaging (HYDI) was used to study 52 healthy subjects with an age range from 18 to 72 years. The HYDI diffusion-encoding scheme consisted of five concentric q-space shells with b values ranging from 0 to 9375 s/mm(2). Quantitative diffusion measures were investigated as a function of age and gender using both region-of-interest (whole-brain white matter, genu and splenium of corpus callosum, posterior limb of the internal capsule) and whole-brain voxel-based analyses. Diffusion measures included measures of the diffusion probability density function (zero displacement probability and mean-squared displacement), biexponential diffusion (i.e., volume fractions of fast/slow diffusion compartments and fast/slow diffusivities), and DTI measures (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity). The biexponential volume fraction, the fast diffusivity, and the axial diffusivity measures (f(1), D(1), and D(a)) were found to be more sensitive to normal aging than the restricted, slow and radial diffusion measures (P(0), D(2), and D(r)). The biexponential volume fraction, f(1), showed the most widespread age dependence in the voxel-based analyses, although both FA and mean diffusivity did show changes in frontal white matter regions that may be associated with age-related decline.

-Space Using Magnetic Resonance Hybrid Diffusion Imaging

Recent studies in rodents have demonstrated that diffusion imaging is highly sensitive to differences in myelination. These studies suggest that demyelination/dysmyelination cause increases in the radial diffusivity from diffusion tensor imaging (DTI) measurements and decreases in the restricted diffusion component from high b-value diffusion-weighted imaging experiments. In this study, the shaking pup (sh pup), a canine model of dysmyelination, was studied on a clinical MRI scanner using a combination of conventional diffusion tensor imaging and high b-value diffusion-weighted imaging methods. Diffusion measurements were compared between control dogs and sh pups in the age range 3 months to 16 months, which is similar to the period of early childhood through adolescence in humans. The study revealed significant group differences in nearly all diffusion measures with the largest differences in the zero-displacement probability (Po) from high b-value DWI and the radial diffusivity from DTI, which are consistent with the observations from the published rodent studies. Age-related changes in Po, FA, mean diffusivity, radial diffusivity and axial diffusivity were observed in whole brain white matter for the control dogs, but not the sh pups. Regionally, age-related changes in the sh pup white matter were observed for Po, mean diffusivity and radial diffusivity in the internal capsule, which may be indicative of mild myelination. These studies demonstrate that DWI may be used to study myelin abnormalities and brain development in large animal models on clinical MRI scanners, which are more amenable to translation to human studies.

Age- and gender-related changes in the normal human brain using hybrid diffusion imaging (HYDI)

Diffusion‐tensor MRI (DT‐MRI) yields information about the magnitude, anisotropy, and orientation of water diffusion of brain tissues. Although white matter tractography and eigenvector color maps provide visually appealing displays of white matter tract organization, they do not easily lend themselves to quantitative and statistical analysis. In this study, a set of visual and quantitative tools for the investigation of tensor orientations in the human brain was developed. Visual tools included rose diagrams, which are spherical coordinate histograms of the major eigenvector directions, and 3D scatterplots of the major eigenvector angles. A scatter matrix of major eigenvector directions was used to describe the distribution of major eigenvectors in a defined anatomic region. A measure of eigenvector dispersion was developed to describe the degree of eigenvector coherence in the selected region. These tools were used to evaluate directional organization and the interhemispheric symmetry of DT‐MRI data in five healthy human brains and two patients with infiltrative diseases of the white matter tracts. In normal anatomical white matter tracts, a high degree of directional coherence and interhemispheric symmetry was observed. The infiltrative diseases appeared to alter the eigenvector properties of affected white matter tracts, showing decreased eigenvector coherence and interhemispheric symmetry. This novel approach distills the rich, 3D information available from the diffusion tensor into a form that lends itself to quantitative analysis and statistical hypothesis testing. Magn Reson Med 52:1146–1155, 2004.

High b-value and diffusion tensor imaging in a canine model of dysmyelination and brain maturation

Microstructural changes in human brain white matter of young to middle-aged adults were studied using advanced diffusion Magnetic Resonance Imaging (dMRI). Multiple shell diffusion-weighted data were acquired using the Hybrid Diffusion Imaging (HYDI). The HYDI method is extremely versatile and data were analyzed using Diffusion Tensor Imaging (DTI), Neurite Orientation Dispersion and Density Imaging (NODDI), and q-space imaging approaches. Twenty-four females and 23 males between 18 and 55years of age were included in this study. The impact of age and sex on diffusion metrics were tested using least squares linear regressions in 48 white matter regions of interest (ROIs) across the whole brain and adjusted for multiple comparisons across ROIs. In this study, white matter projections to either the hippocampus or the cerebral cortices were the brain regions most sensitive to aging. Specifically, in this young to middle-aged cohort, aging effects were associated with more dispersion of white matter fibers while the tissue restriction and intra-axonal volume fraction remained relatively stable. The fiber dispersion index of NODDI exhibited the most pronounced sensitivity to aging. In addition, changes of the DTI indices in this aging cohort were correlated mostly with the fiber dispersion index rather than the intracellular volume fraction of NODDI or the q-space measurements. While men and women did not differ in the aging rate, men tend to have higher intra-axonal volume fraction than women. This study demonstrates that advanced dMRI using a HYDI acquisition and compartmental modeling of NODDI can elucidate microstructural alterations that are sensitive to age and sex. Finally, this study provides insight into the relationships between DTI diffusion metrics and advanced diffusion metrics of NODDI model and q-space imaging.

Quantitative Analysis of Diffusion Tensor Orientation: Theoretical Framework

Objective: To calibrate and correct the gradient errors including gradient amplitude scaling errors, background/imaging gradients, and residual gradients in diffusion tensor imaging (DTI). Methods: A calibration protocol using an isotropic phantom was proposed. Gradient errors were estimated by using linear regression analyses on quadratic functions of diffusion gradients along 3 orthogonal directions. A 6-element total effective scaling vector is generated from the calibration protocol to retrospectively correct gradient errors in DTI experiments. Results: The accuracy of the calibration protocol was within 1% or less in estimating gradient scaling errors. On both the brain study and the computer simulations, the retrospective correction minimized undesirable estimate biases of DTI measurements due to gradient errors. Conclusion: The protocol and retrospective correction are shown to be effective. The method may be used for prospective correction if actual diffusion-gradient waveforms are available. The methodology is expandable to general diffusion imaging schemes.

Age effects and sex differences in human brain white matter of young to middle-aged adults: A DTI, NODDI, and q-space study

An undersampled diffusion‐weighted stack‐of‐stars acquisition is combined with iterative highly constrained back‐projection to perform hyperpolarized helium‐3 MR q‐space imaging with combined regional correction of radiofrequency‐ and T1‐related signal loss in a single breath‐held scan. The technique is tested in computer simulations and phantom experiments and demonstrated in a healthy human volunteer with whole‐lung coverage in a 13‐sec breath‐hold. Measures of lung microstructure at three different lung volumes are evaluated using inhaled gas volumes of 500 mL, 1000 mL, and 1500 mL to demonstrate feasibility. Phantom results demonstrate that the proposed technique is in agreement with theoretical values, as well as with a fully sampled two‐dimensional Cartesian acquisition. Results from the volunteer study demonstrate that the root mean squared diffusion distance increased significantly from the 500‐mL volume to the 1000‐mL volume. This technique represents the first demonstration of a spatially resolved hyperpolarized helium‐3 q‐space imaging technique and shows promise for microstructural evaluation of lung disease in three dimensions. Magn Reson Med, 2010.

A method for calibrating diffusion gradients in diffusion tensor imaging.

The ability of diffusion tensor imaging (DTI) to probe the ultrastructural properties of biological tissues presents new possibilities for DTI‐based tissue characterization, with the potential for greater pathologic specificity than conventional imaging methods. This is urgently needed in the diagnosis and treatment of cerebral neoplasms, where clinical decisions depend on the ability to discriminate tumor‐involved from uninvolved tissue, a major shortcoming of conventional imaging. Several investigators have attempted to make this determination on the basis of the apparent diffusion coefficient (ADC) or the fractional anisotropy (FA), with mixed results. The directionally encoded color map, with hues reflecting tensor orientation and intensity weighted by FA, provides an aesthetic and informative summary of DTI features throughout the brain in an easily interpreted format. The use of these maps is becoming increasingly common in both basic and clinical research, as well as in purely clinical settings. These examples serve to demonstrate our approach to the quantitation of regional diffusion tensor distributions using directional statistical methods.