Measurements of plaque stiffness may provide important prognostic and diagnostic information to help clinicians distinguish vulnerable plaques containing soft lipid pools from more stable, stiffer plaques. In this preliminary study, we compare in vivo ultrasonic Acoustic Radiation Force Impulse (ARFI) imaging derived measures of carotid plaque stiffness with composition determined by spatially registered Magnetic Resonance Imaging (MRI) in five human subjects with stenosis > 50%. Ultrasound imaging was implemented on a commercial diagnostic scanner with custom pulse sequences to collect spatially registered 2D longitudinal B-mode and ARFI images. A standardized, multi-contrast weighted MRI sequence was used to obtain 3D Time of Flight (TOF), T1 weighted (T1W), T2 weighted (T2W), and Proton Density Weighted (PDW) transverse image stacks of volumetric data. The MRI data was segmented to identify lipid, calcium, and normal loose matrix components using commercially available software. 3D MRI segmented plaque models were rendered and spatially registered with 2D B-mode images to create fused ultrasound and MRI volumetric images for each subject. ARFI imaging displacements in regions of interest (ROIs) derived from MRI segmented contours of varying composition were compared. Regions of calcium and normal loose matrix components identified by MRI presented as homogeneously stiff regions of similarly low (typically ≈ 1 μm) displacement in ARFI imaging. MRI identified lipid pools > 2 mm(2), found in three out of five subjects, presented as softer regions of increased displacement that were on average 1.8 times greater than the displacements in adjacent regions of loose matrix components in spatially registered ARFI images. This work provides early evidence supporting the use of ARFI imaging to noninvasively identify lipid regions in carotid artery plaques in vivo that are believed to increase the propensity of a plaque to rupture. Additionally, the results provide early training data for future studies and aid in the interpretation and possible clinical utility of ARFI imaging for identifying the elusive vulnerable plaque.
Activation of the ventral tegmental area (VTA) and mesolimbic networks is essential to motivation, performance, and learning. Humans routinely attempt to motivate themselves, with unclear efficacy or impact on VTA networks. Using fMRI, we found untrained participants' motivational strategies failed to consistently activate VTA. After real-time VTA neurofeedback training, however, participants volitionally induced VTA activation without external aids, relative to baseline, Pre-test, and control groups. VTA self-activation was accompanied by increased mesolimbic network connectivity. Among two comparison groups (no neurofeedback, false neurofeedback) and an alternate neurofeedback group (nucleus accumbens), none sustained activation in target regions of interest nor increased VTA functional connectivity. The results comprise two novel demonstrations: learning and generalization after VTA neurofeedback training and the ability to sustain VTA activation without external reward or reward cues. These findings suggest theoretical alignment of ideas about motivation and midbrain physiology and the potential for generalizable interventions to improve performance and learning.
Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) allow in vivo investigation of molecular motion of tissue water at a microscopic level in cerebral gray matter (GM) and white matter (WM). DWI/DTI measure of water diffusion has been proven to be invaluable for the study of many neurodegenerative diseases (e.g., Alzheimer's disease and Creutzfeldt-Jakob disease) that predominantly involve GM. Thus, quantitative analysis of GM diffusivity is of scientific interest and is promised to have a clinical impact on the investigation of normal brain aging and neuropathology. In this paper, we propose an automated framework for analysis of GM diffusivity in 76 standard anatomic subdivisions of gray matter to facilitate studies of neurodegenerative and other gray matter neurological diseases. The computational framework includes three enabling technologies: (1) automatic parcellation of structural MRI GM into 76 precisely defined neuroanatomic subregions ("76-space"), (2) automated segmentation of GM, WM and CSF based on DTI data, and (3) automatic measurement of the average apparent diffusion coefficient (ADC) in each segmented GM subregion. We evaluate and validate this computational framework for 76-space GM diffusivity analysis using data from normal volunteers and from patients with Creutzfeldt-Jakob disease.
Functional connectivity (FC) reflects the coherence of spontaneous, low-frequency fluctuations in functional magnetic resonance imaging (fMRI) data. We report a behavior-based connectivity analysis method, in which whole-brain data are used to identify behaviorally relevant, intrinsic FC networks. Nineteen younger adults (20-28 years) and 19 healthy, older adults (63-78 years) were assessed with fMRI and diffusion tensor imaging (DTI). Results indicated that FC involving a distributed network of brain regions, particularly the inferior frontal gyri, exhibited age-related change in the correlation with perceptual-motor speed (choice reaction time; RT). No relation between FC and RT was evident for younger adults, whereas older adults exhibited a significant age-related slowing of perceptual-motor speed, which was mediated by decreasing FC. Older adults' FC values were in turn associated positively with white matter integrity (from DTI) within the genu of the corpus callosum. The developed FC analysis illustrates the value of identifying connectivity by combining structural, functional, and behavioral data.
Recent emergence of human connectome imaging has led to a high demand on angular and spatial resolutions for diffusion magnetic resonance imaging (MRI). While there have been significant growths in high angular resolution diffusion imaging, the improvement in spatial resolution is still limited due to a number of technical challenges, such as the low signal-to-noise ratio and high motion artifacts. As a result, the benefit of a high spatial resolution in the whole-brain connectome imaging has not been fully evaluated in vivo. In this brief report, the impact of spatial resolution was assessed in a newly acquired whole-brain three-dimensional diffusion tensor imaging data set with an isotropic spatial resolution of 0.85 mm. It was found that the delineation of short cortical association fibers is drastically improved as well as the definition of fiber pathway endings into the gray/white matter boundary-both of which will help construct a more accurate structural map of the human brain connectome.
