SummaryDiffusion tensor imaging (DTI) is a promising method for characterizing microstructural changes or differences with neuropathology and treatment. The diffusion tensor may be used to characterize the magnitude, anisotropy and orientation of the diffusion tensor. This paper reviews the biological mechanisms, acquisition and analysis methodology of DTI measurements. The relationships between DTI measures and white matter pathologic features (ischemia, myelination, axonal damage, inflammation, and edema) are summarized. Applications of DTI to tissue characterization in neurotherapeutic applications are reviewed. The interpretations of common DTI measures -mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (Dr) and axial diffusivity (Da) -are discussed. In particular, FA is highly sensitive to microstructural changes, but not very specific to the type of changes (e.g., radial or axial). In order to maximize the specificity, it is recommended that future studies use multiple diffusion tensor measures (e.g., MD and FA, or Da and Dr) to better characterize the tissue microstructure.

The diffusion tensor is currently the accepted model of diffusion in biological tissues. The measured diffusion behavior may be more complex when two or more distinct tissues with different diffusion tensors occupy the same voxel. In this study, a partial volume model of MRI signal behavior for two diffusion-tensor compartments is presented. Simulations using this model demonstrate that the conventional single diffusion tensor model could lead to highly variable and inaccurate measurements of diffusion behavior. The differences between the single and twotensor models depend on the orientations, fractions, and exchange between the two diffusion tensor compartments, as well as the diffusion-tensor encoding technique and diffusionweighting that is used in the measurements. The current single compartment model's inaccuracies could cause diffusionbased characterization of cerebral ischemia and white matter connectivity to be incorrect. A diffusion-tensor MRI imaging experiment on a normal human brain revealed significant partial volume effects between oblique white matter regions when using very large voxels and large diffusion-weighting (b ϳ 2.69 ؋ 10 3 sec/mm 2 ). However, the apparent partial volume effects in white matter decreased significantly when smaller voxel dimensions were used. The diffusion-tensor is a mathematically elegant description of diffusion as a function of direction. Basser and Pierpaoli (1) applied the tensor formalism to diffusion measurements of biological tissues obtained by MRI and NMR spectroscopy. One of the most important observations is that organized fibrous tissues, such as muscle and cerebral white matter, demonstrate anisotropic diffusion. The direction of greatest diffusivity corresponds to the fiber axis direction. The diffusion tensor describes the magnitude of the water diffusion, the degree of diffusion anisotropy, and the orientation of the anisotropy.Measurements of the diffusion tensor and its components (i.e., the trace) have been found to have several applications in the human brain (2,3). The trace of the diffusion tensor has been found to be valuable for detecting and evaluating brain ischemia and stroke (4,5). Measures of diffusion tensor anisotropy have been used to study white matter in terms of morphology (6), disease and trauma (8,9), brain development (10,11), and neurosurgical planning (12). Several investigators have recently proposed using the principal eigenvectors of the diffusion tensor to estimate white matter connectivity (13)(14)(15). Each of these applications will be influenced by the accuracy of the measurements of the diffusion tensor. Recent studies have investigated the effects of measurement noise (16,17) and the tensor encoding strategy (18) on the accuracy of the diffusion tensor and its derived parameters.Partial volume effects can also significantly influence the accuracy of diffusion tensor measurements. This is particularly true for most DT-MRI studies that use EPI techniques with relatively large voxels (ϳ1.5-5.0 mm on a side). Previous stud...

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Diffusion tensor MRI provides unique directional diffusion information that can be used to estimate the patterns of white matter connectivity in the human brain. In this study, the behavior of an algorithm for white matter tractography is examined. The algorithm, called TEND, uses the entire diffusion tensor to deflect the estimated fiber trajectory. Simulations and imaging experiments on in vivo human brains were performed to investigate the behavior of the tractography algorithm. The simulations show that the deflection term is less sensitive than the major eigenvector to image noise. In the human brain imaging experiments, estimated tracts were generated in corpus callosum, corticospinal tract, internal capsule, corona radiata, superior longitudinal fasciculus, inferior longitudinal fasciculus, fronto-occipital fasciculus, and uncinate fasciculus. This approach is promising for mapping the organizational patterns of white matter in the human brain as well as mapping the relationship between major fiber trajectories and the location and extent of brain lesions.

Traumatic brain injury (TBI) is associated with brain volume loss, but there is little information on the regional gray matter (GM) and white matter (WM) changes that contribute to overall loss. Since axonal injury is a common occurrence in TBI, imaging methods that are sensitive to WM damage such as diffusion-tensor imaging (DTI) may be useful for characterizing microstructural brain injury contributing to regional WM loss in TBI. High-resolution T1-weighted imaging and DTI were used to evaluate regional changes in TBI patients compared to matched controls. Patients received neuropsychological testing and were imaged approximately 2 months and 12.7 months post-injury. Paradoxically, neuropsychological function improved from Visit 1 to Visit 2, while voxel-based analyses of fractional anisotropy (FA), and mean diffusivity (MD) from the DTI images, and voxel-based analyses of the GM and WM probability maps from the T1-weighted images, mainly revealed significantly greater deleterious GM and WM change over time in patients compared to controls. Cross-sectional comparisons of the DTI measures indicated that patients have decreased FA and increased MD compared to controls over large regions of the brain. TBI affected virtually all of the major fiber bundles in the brain including the corpus callosum, cingulum, the superior and inferior longitudinal fascicules, the uncinate fasciculus, and brain stem fiber tracts. The results indicate that both GM and WM degeneration are significant contributors to brain volume loss in the months following brain injury, and also suggest that DTI measures may be more useful than high-resolution anatomical images in assessment of group differences.

Schizophrenia patients showed a decrease in EEG-evoked responses in the gamma band when TMS was applied to directly stimulate the frontal cortex while these responses were recorded. Since EEG responses to direct cortical stimulation are not affected by an individual's motivation, attention, or cognitive capacity and are not relayed through peripheral afferent pathways, these findings suggest that there might be an intrinsic dysfunction in frontal thalamocortical circuits in individuals with schizophrenia.

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Structural brain change and concomitant cognitive decline are the seemingly unavoidable escorts of aging. Despite accumulating studies detailing the effects of age on the brain and cognition, the relationship between white matter features and cognitive function in aging have only recently received attention and remain incompletely understood. White matter microstructure can be measured with diffusion tensor imaging (DTI), but whether DTI can provide unique information on brain aging that is not explained by white matter volume is not known. In the current study, the relationship between white matter microstructure, age and neuropsychological function was assessed using DTI in a statistical framework that employed white matter volume as a voxel-wise covariate in a sample of 120 healthy adults across a broad age range (18-83). Memory function and executive function were modestly correlated with the DTI measures while processing speed showed the greatest extent of correlation. The results suggest that age-related white matter alterations underlie age-related declines in cognitive function. Mean diffusivity and fractional anisotropy in several white matter brain regions exhibited a non-linear relationship with age, while white matter volume showed a primarily linear relationship with age. The complex relationships between cognition, white matter microstructure, and white matter volume still require further investigation.