Karl Young

Bio: Karl Young is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Pulse sequence & Magnetic resonance spectroscopic imaging. The author has an hindex of 20, co-authored 39 publications receiving 3242 citations. Previous affiliations of Karl Young include San Francisco VA Medical Center & University of Miami.

Proton NMR chemical shifts and coupling constants for brain metabolites.

Abstract: Proton NMR chemical shift and J-coupling values are presented for 35 metabolites that can be detected by in vivo or in vitro NMR studies of mammalian brain. Measurements were obtained using high-field NMR spectra of metabolites in solution, under conditions typical for normal physiological temperature and pH. This information is presented with an accuracy that is suitable for computer simulation of metabolite spectra to be used as basis functions of a parametric spectral analysis procedure. This procedure is verified by the analysis of a rat brain extract spectrum, using the measured spectral parameters. In addition, the metabolite structures and example spectra are presented, and clinical applications and MR spectroscopic measurements of these metabolites are reviewed.

Skeletal muscle contractile and noncontractile components in young and older women and men

Abstract: To examine the influences of age, gender, and habitual physical activity level on human skeletal muscle composition, we developed a relatively simple magnetic resonance imaging method for the quant...

Automated spectral analysis III: application to in vivo proton MR spectroscopy and spectroscopic imaging

Abstract: An automated method for analysis of in vivo proton magnetic resonance (MR) spectra and reconstruction of metabolite distributions from MR spectroscopic imaging (MRSI) data is described. A parametric spectral model using acquisition specific, a priori information is combined with a wavelet-based, nonparametric characterization of baseline signals. For image reconstruction, the initial fit estimates were additionally modified according to a priori spatial constraints. The automated fitting procedure was applied to four different examples of MRS data obtained at 1.5 T and 4.1 T. For analysis of major metabolites at medium TE values, the method was shown to perform reliably even in the presence of large baseline signals and relatively poor signal-to-noise ratios typical of in vivo proton MRSI. Identification of additional metabolites was also demonstrated for short TE data. Automated formation of metabolite images will greatly facilitate and expand the clinical applications of MR spectroscopic imaging.

Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging.

Abstract: Image reconstruction for magnetic resonance spectroscopic imaging (MRSI) requires specialized spatial and spectral data processing methods and benefits from the use of several sources of prior information that are not commonly available, including MRI-derived tissue segmentation, morphological analysis and spectral characteristics of the observed metabolites. In addition, incorporating information obtained from MRI data can enhance the display of low-resolution metabolite images and multiparametric and regional statistical analysis methods can improve detection of altered metabolite distributions. As a result, full MRSI processing and analysis can involve multiple processing steps and several different data types. In this paper, a processing environment is described that integrates and automates these data processing and analysis functions for imaging of proton metabolite distributions in the normal human brain. The capabilities include normalization of metabolite signal intensities and transformation into a common spatial reference frame, thereby allowing the formation of a database of MR-measured human metabolite values as a function of acquisition, spatial and subject parameters. This development is carried out under the MIDAS project (Metabolite Imaging and Data Analysis System), which provides an integrated set of MRI and MRSI processing functions. It is anticipated that further development and distribution of these capabilities will facilitate more widespread use of MRSI for diagnostic imaging, encourage the development of standardized MRSI acquisition, processing and analysis methods and enable improved mapping of metabolite distributions in the human brain.

A detailed analysis of localized J-difference GABA editing: theoretical and experimental study at 4 T.

Abstract: The problem of low signal-to-noise ratio for γ-aminobutyric acid (GABA) in vivo is exacerbated by inefficient detection schemes and non-optimal experimental parameters. To analyze the mechanisms for GABA signal loss of a MEGA-PRESS J-difference sequence at 4 T, numerical simulations were performed ranging from ideal to realistic experimental implementation, including volume selection and experimental radio frequency (RF) pulse shapes with a macromolecular minimization scheme. The simulations were found to be in good agreement with phantom and in vivo data from human brain. The overall GABA signal intensity for the simulations with realistic conditions for the MEGA-PRESS difference spectrum was calculated to be almost half of the signal simulated under ideal conditions (∼43% signal loss). In contrast, creatine was reduced significantly less then GABA (∼19% signal loss). The ‘four-compartment’ distribution due to J-coupling in the PRESS-based localization was one of the most significant sources of GABA signal loss, in addition to imperfect RF profiles for volume selection and editing. An alternative strategy that reduces signal loss due to the four-compartment distribution is suggested. In summary, a detailed analysis of J-difference editing is provided with estimates of the relative amounts of GABA signal losses due to various mechanisms. The numerical simulations presented in this study should facilitate both implementation of the more efficient acquisition and quantification process of J-coupled systems. Copyright © 2007 John Wiley & Sons, Ltd.

Invited review: Aging and sarcopenia.

Abstract: Aging is associated with progressive loss of neuromuscular function that often leads to progressive disability and loss of independence. The term sarcopenia is now commonly used to describe the loss of skeletal muscle mass and strength that occurs in concert with biological aging. By the seventh and eighth decade of life, maximal voluntary contractile strength is decreased, on average, by 20-40% for both men and women in proximal and distal muscles. Although age-associated decreases in strength per unit muscle mass, or muscle quality, may play a role, the majority of strength loss can be accounted for by decreased muscle mass. Multiple factors lead to the development of sarcopenia and the associated impact on function. Loss of skeletal muscle fibers secondary to decreased numbers of motoneurons appears to be a major contributing influence, but other factors, including decreased physical activity, altered hormonal status, decreased total caloric and protein intake, inflammatory mediators, and factors leading to altered protein synthesis, must also be considered. The prevalence of sarcopenia, which may be as high as 30% for those ≥60 yr, will increase as the percentage of the very old continues to grow in our populations. The link between sarcopenia and disability among elderly men and women highlights the need for continued research into the development of the most effective interventions to prevent or at least partially reverse sarcopenia, including the role of resistance exercise and other novel pharmacological and nutritional interventions.

Proton NMR chemical shifts and coupling constants for brain metabolites.

Abstract: Proton NMR chemical shift and J-coupling values are presented for 35 metabolites that can be detected by in vivo or in vitro NMR studies of mammalian brain. Measurements were obtained using high-field NMR spectra of metabolites in solution, under conditions typical for normal physiological temperature and pH. This information is presented with an accuracy that is suitable for computer simulation of metabolite spectra to be used as basis functions of a parametric spectral analysis procedure. This procedure is verified by the analysis of a rat brain extract spectrum, using the measured spectral parameters. In addition, the metabolite structures and example spectra are presented, and clinical applications and MR spectroscopic measurements of these metabolites are reviewed.

Brain Graphs: Graphical Models of the Human Brain Connectome

Abstract: Brain graphs provide a relatively simple and increasingly popular way of modeling the human brain connectome, using graph theory to abstractly define a nervous system as a set of nodes (denoting anatomical regions or recording electrodes) and interconnecting edges (denoting structural or functional connections). Topological and geometrical properties of these graphs can be measured and compared to random graphs and to graphs derived from other neuroscience data or other (nonneural) complex systems. Both structural and functional human brain graphs have consistently demonstrated key topological properties such as small-worldness, modularity, and heterogeneous degree distributions. Brain graphs are also physically embedded so as to nearly minimize wiring cost, a key geometric property. Here we offer a conceptual review and methodological guide to graphical analysis of human neuroimaging data, with an emphasis on some of the key assumptions, issues, and trade-offs facing the investigator.