Faculty Bibliography

We present a novel parametric encoding scheme for efficiently recording white matter fiber bundle information obtained from diffusion tensor imaging. The coordinates of fiber tracts are parameterized using a cosine series expansion. For an arbitrary tract, a 19 degree expansion is found to be sufficient to reconstruct the tract with an average error of about 0.26 mm. Then each tract is fully parameterized with 60 parameters, which results in a substantial data reduction. Unlike traditional splines, the proposed method does not have internal knots and explicitly represents the tract as a linear combination of basis functions. This simplicity in the representation enables us to design statistical models, register tracts and perform subsequent analysis in a more streamlined mathematical framework. As an illustration, we apply the proposed method in characterizing abnormal tracts that pass through the splenium of the corpus callosum in autistic subjects

The Orientation Distribution Function (ODF) is used to describe the directionality of multimodal diffusion in regions with complex fiber architecture present in brain and other biological tissues. In this study, an approximation for the ODF of water diffusion from diffusional kurtosis imaging (DKI) is presented. DKI requires only a relatively limited number of diffusion measurements and, for the brain, b values no higher than 2500 s/mm(2). The DKI-based ODF approximation is decomposed into two components representing the Gaussian and non-Gaussian (NG) diffusion contributions, respectively. Simulations of multiple fiber configurations show that both the total and the NG-ODF are able to resolve the orientations of the component fibers, with the NG-ODF being the most sensitive to profiling the fibers' directions. Orientation maps obtained for in vivo brain imaging data demonstrate multiple fiber components in brain regions with complex anatomy. The results appear to be in agreement with known white matter architecture. Magn Reson Med 60:774-781, 2008. (c) 2008 Wiley-Liss, Inc

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

OBJECTIVE: Transcranial magnetic stimulation (TMS) combined with high-density electroencephalography (EEG) can be used to directly examine the properties of thalamocortical circuits in the brain without engaging an individual in cognitive or motor tasks. The authors investigated EEG responses in schizophrenia patients and healthy comparison subjects following the application of TMS to the premotor cortex. METHOD: Sixteen schizophrenia patients and 14 healthy comparison subjects were recruited to participate in the study. Participants underwent three to five TMS/high-density EEG sessions at various TMS doses. The following three aspects of TMS-evoked responses were analyzed: amplitude, synchronization, and source localization. RESULTS: Relative to healthy comparison subjects, schizophrenia patients had a marked decrease in evoked gamma oscillations that occurred within the first 100 msec after TMS, particularly in a cluster of electrodes located in a fronto-central region. These oscillations were significantly reduced in amplitude (calculated using global-mean field power and event-related spectral perturbation analysis) and synchronization (measured using intertrial coherence). Furthermore, source modeling analysis revealed that the TMS-evoked brain activation underlying these gamma oscillations in patients with schizophrenia did not propagate (as it did in healthy comparison subjects) and was mostly confined to the stimulated brain region. CONCLUSIONS: 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

Recent MRI studies have indicated that regions of the temporal lobe including the superior temporal gyrus (STG) and the temporal stem (TS) appear to be abnormal in autism. In this study, diffusion tensor imaging (DTI) measurements of white matter in the STG and the TS were compared in 43 autism and 34 control subjects. DTI measures of mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were compared between groups. In all regions, fractional anisotropy was significantly decreased and both mean diffusivity and radial diffusivity were significantly increased in the autism group. These results suggest that white matter microstructure in autism is abnormal in these temporal lobe regions, which is consistent with theories of aberrant brain connectivity in autism

Diffusion 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, the degree of anisotropy, and the orientation of directional diffusion. This review addresses the biological mechanisms, acquisition, and analysis of DTI measurements. The relationships between DTI measures and white matter pathologic features (e.g., 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, D(r); and axial diffusivity, D(a)) 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). To maximize the specificity and better characterize the tissue microstructure, future studies should use multiple diffusion tensor measures (e.g., MD and FA, or D(a) and D(r))

The corpus callosum is the largest commissural white matter pathway that connects the hemispheres of the human brain. In this study, diffusion tensor imaging (DTI) was performed on subject groups with high-functioning autism and controls matched for age, handedness, IQ, and head size. DTI and volumetric measurements of the total corpus callosum and subregions (genu, body and splenium) were made and compared between groups. The results showed that there were significant differences in volume, fractional anisotropy, mean diffusivity, and radial diffusivity between groups. These group differences appeared to be driven by a subgroup of the autism group that had small corpus callosum volumes, high mean diffusivity, low anisotropy, and increased radial diffusivity. This subgroup had significantly lower performance IQ measures than either the other individuals with autism or the control subjects. Measurements of radial diffusivity also appeared to be correlated with processing speed measured during the performance IQ tests. The subgroup of autism subjects with high mean diffusivity and low fractional anisotropy appeared to cluster with the highest radial diffusivities and slowest processing speeds. These results suggest that the microstructure of the corpus callosum is affected in autism, which may be related to nonverbal cognitive performance

Brodmann's areas are part of the common vernacular used by neuroscientists to indicate specific location of brain activity in functional brain imaging studies. Here, we have employed a template based on the Brodmann's areas as a means of compartmentalizing underlying white matter pathways. White matter tractography was performed on the diffusion tensor data of sixteen subjects using a streamline tracking technique with Runge-Kutta integration. After co-registration, the Brodmann template was utilized for ROI selection. Tracts were segmented based on their termination in a particular area of the template. Binary masks were generated based on the tractography segmentation for a given Brodmann's area in each individual subject. Following registration to a normalized coordinate space, the binary masks were averaged, generating a map that estimates the probability of tractography connectivity for particular white matter pathways to a specific Brodmann's area. The probability maps were color-coded and overlaid on anatomical images to provide perspective. In this study, particular attention was given to the areas of the frontal cortex. A composite map of these areas was generated by assigning each voxel to the Brodmann's area with the highest probability of connectivity, based on the average results. The average maps generated with this method reveal consistent patterns of connectivity across subjects. The use of a normalized template for ROI selection automates the process of segmenting tractography data, making it particularly useful for multi-subject studies. In the future, this method could be used to help elucidate relationships between function and anatomical structure

Accurate localization of white matter fiber tracts in relation to brain tumors is a goal of critical importance to the neurosurgical community. White matter fiber tractography by means of diffusion tensor magnetic resonance imaging (DTI) is the only non-invasive method that can provide estimates of brain connectivity. However, conventional tractography methods are based on data acquisition techniques that suffer from image distortions and artifacts. Thus, a large percentage of white matter fiber bundles are distorted, and/or terminated early, while others are completely undetected. This severely limits the potential of fiber tractography in pre-surgical planning and image-guided surgery. In contrast, Turboprop-DTI is a technique that provides images with significantly fewer distortions and artifacts than conventional DTI data acquisition methods. The purpose of this study was to evaluate fiber tracking results obtained from Turboprop-DTI data. It was demonstrated that Turboprop may be a more appropriate DTI data acquisition technique for tracing white matter fibers than conventional DTI methods, especially in applications such as pre-surgical planning and image-guided surgery