A cohort of 18 patients with AD, 18 patients with amnestic mild cognitive impairment (MCI), and 18 normal controls underwent morphological and DKI MR imaging. Images were investigated using regions-of-interest-based analyses for deep gray matter and vertex-wise analyses for cortical gray matter. In deep gray matter, more regions showed DKI parametric abnormalities than atrophies at the early MCI stage. Mean kurtosis (MK) exhibited the largest number of significant abnormalities among all DKI metrics. At the later AD stage, diffusional abnormalities were observed in fewer regions than atrophies. In cortical gray matter, abnormalities in thickness were mainly in the medial and lateral temporal lobes, which fit the locations of known early pathological changes. Microstructural abnormalities were predominantly in the parietal and even frontal lobes, which fit the locations of known late pathological changes. In conclusion, MK can complement conventional diffusion metrics for detecting microstructural changes, especially in deep gray matter. This study also provides evidence supporting the notion that microstructural changes predate morphological changes.
We present a new MRI method for imaging whole brain cytoarchitecture at 10-μm isotropic resolution non-destructively in 3D.
Cell layers are visualized in the olfactory bulb, eye ball, barrel cortex, hippocampus and cerebellum.
Axonal fiber trajectories are identified in both white matter and gray matter.
A network of medium spiny neurons are demonstrated in striatum.
STI can probe tissue microstructure, but is limited by reconstruction artifacts because of absent phase information outside the tissue and noise. STI accuracy may be improved by estimating a joint eigenvector from mutually anisotropic susceptibility and relaxation tensors. Gradient-recalled echo image data were simulated using a numerical phantom and acquired from the ex vivo mouse brain, kidney, and heart. Susceptibility tensor data were reconstructed using STI, regularized STI, and the proposed algorithm of mutually anisotropic and joint eigenvector STI (MAJESTI). Fiber map and tractography results from each technique were compared with diffusion tensor data. MAJESTI estimation of the susceptibility tensors yields lower orientation errors for susceptibility-based fiber mapping and tractography in the intact brain, kidney, and heart.
Inflammation induced by innate immunity influences the development of T cell–mediated autoimmunity in multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis (EAE). We found that strong activation of innate immunity induced Nod-like receptor protein 3 (NLRP3) inflammasome–independent and interferon-β (IFNβ)-resistant EAE (termed type B EAE), whereas EAE induced by weak activation of innate immunity requires the NLRP3 inflammasome and is sensitive to IFNβ treatment. Instead, an alternative inflammatory mechanism, including membrane-bound lymphotoxin-β receptor (LTβR) and CXC chemokine receptor 2 (CXCR2), is involved in type B EAE development, and type B EAE is ameliorated by antagonizing these receptors. Relative expression of Ltbr and Cxcr2 genes was indeed enhanced in patients with IFNβ-resistant multiple sclerosis. Remission was minimal in type B EAE due to neuronal damages induced by semaphorin 6B upregulation on CD4+ T cells. Our data reveal a new inflammatory mechanism by which an IFNβ-resistant EAE subtype develops.
Diffusion tensor imaging (DTI) is known to have a limited capability of resolving multiple fiber orientations within one voxel. This is mainly because the probability density function (PDF) for random spin displacement is non-Gaussian in the confining environment of biological tissues and, thus, the modeling of self-diffusion by a second-order tensor breaks down. The statistical property of a non-Gaussian diffusion process is characterized via the higher-order tensor (HOT) coefficients (variance, skewness, kurtosis etc.) by reconstructing the PDF of the random spin displacement. Those HOT coefficients can be determined by combining a series of complex diffusion-weighted measurements. The signal equation for an MR diffusion experiment was investigated theoretically by generalizing Fick's law to a higher-order partial differential equation (PDE) obtained via Kramers-Moyal expansion. A relationship has been derived between the HOT coefficients of the PDE and the higher-order cumulants of the random spin displacement. Monte-Carlo simulations of diffusion in a restricted environment with different geometrical shapes were performed, and the strengths and weaknesses of both HOT and established diffusion analysis techniques were investigated. The generalized diffusion tensor formalism is capable of accurately resolving the underlying spin displacement for complex geometrical structures, of which neither conventional DTI nor diffusion-weighted imaging at high angular resolution (HARD) is capable. The HOT method helps illuminate some of the restrictions that are characteristic of these other methods. Furthermore, a direct relationship between HOT and q-space is also established.
A collagen tissue model was simulated and ex vivo animal cartilage experiments were conducted at 9.4 Tesla (T) to evaluate the B0 orientation-dependent magnetic susceptibility contrast observed in cartilage. Furthermore, nine volunteers (six healthy subjects without knee pain history and three patients with known knee injury, between 29 and 58 years old) were scanned using gradient-echo acquisitions on a high-field 7T MR scanner. Susceptibility values of different tissues were quantified and diseased cartilage and meniscus were compared against that of healthy volunteers. The arrangement of the collagen fibrils is significant, and likely the most dominant source of magnetic susceptibility anisotropy. Quantitative susceptibility mapping offers a means to characterize magnetic susceptibility properties of tissues in the knee joint. It is sensitive to collagen damage or degeneration and may be useful for evaluating the status of knee diseases, such as meniscal tears and cartilage disease.
Department of Electrical Engineering and Computer Sciences, and
Helen Wills Neuroscience Institute
University of California, Berkeley
B.S. Physics, Peking University
M.S. Physics, University of North Carolina, Chapel Hill
M.S. Management Science and Engineering, Stanford University
Ph.D. Electrical Engineering, Stanford University