P Chang

Family-wide analysis of poly(ADP-ribose) polymerase activity

The poly(adenosine diphosphate (ADP)-ribose) polymerase (PARP) protein family generates ADP-ribose (ADPr) modifications onto target proteins using NAD(+) as substrate. Based on the composition of three NAD(+) coordinating amino acids, the H-Y-E motif, each PARP is predicted to generate either poly(ADPr) (PAR) or mono(ADPr) (MAR). However, the reaction product of each PARP has not been clearly defined, and is an important priority since PAR and MAR function via distinct mechanisms. Here we show that the majority of PARPs generate MAR, not PAR, and demonstrate that the H-Y-E motif is not the sole indicator of PARP activity. We identify automodification sites on seven PARPs, and demonstrate that MAR and PAR generating PARPs modify similar amino acids, suggesting that the sequence and structural constraints limiting PARPs to MAR synthesis do not limit their ability to modify canonical amino-acid targets. In addition, we identify cysteine as a novel amino-acid target for ADP-ribosylation on PARPs.

High and ultra-high resolution metabolite mapping of the human brain using 1H FID MRSI at 9.4T

ABSTRACT Magnetic resonance spectroscopic imaging (MRSI) is a promising technique for mapping the spatial distribution of multiple metabolites in the human brain. These metabolite maps can be used as a diagnostic tool to gain insight into several biochemical processes and diseases in the brain. In comparison to lower field strengths, MRSI at ultra‐high field strengths benefits from a higher signal to noise ratio (SNR) as well as higher chemical shift dispersion, and hence spectral resolution. This study combines the benefits of an ultra‐high field magnet with the advantages of an ultra‐short TE and TR single‐slice FID‐MRSI sequence (such as negligible J‐evolution and loss of SNR due to T2 relaxation effects) and presents the first metabolite maps acquired at 9.4 T in the healthy human brain at both high (voxel size of 97.6 &mgr;L) and ultra‐high (voxel size of 24.4 &mgr;L) spatial resolutions in a scan time of 11 and 46 min respectively. In comparison to lower field strengths, more anatomically‐detailed maps with higher SNR from a larger number of metabolites are shown. A total of 12 metabolites including glutamate (Glu), glutamine (Gln), N‐acetyl‐aspartyl‐glutamate (NAAG), Gamma‐aminobutyric acid (GABA) and glutathione (GSH) are reliably mapped. Comprehensive description of the methodology behind these maps is provided. HIGHLIGHTSFirst metabolite maps acquired at 9.4 T from brains of healthy volunteers.Descriptive methodology of the acquisition and processing of the spectra.Comparison of high and ultra‐high spatial resolution for metabolite mapping.Anatomically detailed maps from 12 brain metabolites.

Modeling real shim fields for very high degree (and order) B0 shimming of the human brain at 9.4 T

To describe the process of calibrating a B0 shim system using high‐degree (or high order) spherical harmonic models of the measured shim fields, to provide a method that considers amplitude dependency of these models, and to show the advantage of very high‐degree B0 shimming for whole‐brain and single‐slice applications at 9.4 Tesla (T).

A Comparison of Optimization Algorithms for Localized in-vivo B0 Shimming

To compare several different optimization algorithms currently used for localized in vivo B0 shimming, and to introduce a novel, fast, and robust constrained regularized algorithm (ConsTru) for this purpose.

MultiNet PyGRAPPA: Multiple neural networks for reconstructing variable density GRAPPA (a 1H FID MRSI study)

&NA; Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool for mapping metabolite levels across the brain, however, it generally suffers from long scan times. This severely hinders its application in clinical settings. Additionally, the presence of nuisance signals (e.g. the subcutaneous lipid signals close to the skull region in brain metabolite mapping) makes it challenging to apply conventional acceleration techniques to shorten the scan times. The goal of this work is, therefore, to increase the overall applicability of high resolution metabolite mapping using 1H MRSI by introducing a novel GRAPPA acceleration acquisition/reconstruction technique. An improved reconstruction method (MultiNet) is introduced that uses machine learning, specifically neural networks, to reconstruct accelerated data. The method is further modified to use more neural networks with nonlinear hidden layers and is then combined with a variable density undersampling scheme (MultiNet PyGRAPPA) to enable higher in‐plane acceleration factors of R = 5.6 and R = 7 for a non‐lipid suppressed ultra‐short TR and TE 1H FID MRSI sequence. The proposed method is evaluated for high resolution metabolite mapping of the human brain at 9.4T. The results show that the proposed method is superior to conventional GRAPPA: there is no significant residual lipid aliasing artifact in the images when the proposed MultiNet method is used. Furthermore, the MultiNet PyGRAPPA acquisition/reconstruction method with R = 5.6 results in reproducible high resolution metabolite maps (with an in‐plane matrix size of 64 × 64) that can be acquired in 2.8 min on 9.4T. In conclusion, using multiple neural networks to predict the missing points in GRAPPA reconstruction results in a more reliable data recovery while keeping the noise levels under control. Combining this high fidelity reconstruction with variable density undersampling (MultiNet PyGRAPPA) enables higher in‐plane acceleration factors even for non‐lipid suppressed 1H FID MRSI, without introducing any structured aliasing artifact in the image.

Over-discretized SENSE reconstruction and B 0 correction for accelerated non-lipid-suppressed 1H FID MRSI of the human brain at 9.4 T

The aim of this work was to use post‐processing methods to improve the data quality of metabolite maps acquired on the human brain at 9.4 T with accelerated acquisition schemes. This was accomplished by combining an improved sensitivity encoding (SENSE) reconstruction with a B0 correction of spatially over‐discretized magnetic resonance spectroscopic imaging (MRSI) data.

Non-water-suppressed 1H FID-MRSI at 3T and 9.4T: Title Copy Needed

This study investigates metabolite concentrations using metabolite‐cycled 1H free induction decay (FID) magnetic resonance spectroscopic imaging (MRSI) at ultra‐high fields.

Compressed sensing for high-resolution nonlipid suppressed 1H FID MRSI of the human brain at 9.4T

The aim of this study was to apply compressed sensing to accelerate the acquisition of high resolution metabolite maps of the human brain using a nonlipid suppressed ultra‐short TR and TE 1H FID MRSI sequence at 9.4T.

Constrained optimization for position calibration of an NMR field camera: Position Calibration of an NMR Field Camera

Knowledge of the positions of field probes in an NMR field camera is necessary for monitoring the B0 field. The typical method of estimating these positions is by switching the gradients with known strengths and calculating the positions using the phases of the FIDs. We investigated improving the accuracy of estimating the probe positions and analyzed the effect of inaccurate estimations on field monitoring.