Biology subjects

Barker, P. B.

Publications and source records attributed to Barker, P. B..

8 recordsLinked to original sources

bioRxiv2026

Fully automated open-source analysis and interactive visualization of magnetic resonance spectroscopic imaging (MRSI) data in Osprey-MRSI

Purpose: Magnetic resonance spectroscopic imaging (MRSI) is a versatile technique to investigate the spatial distribution of in vivo metabolism. However, processing MRSI data is demanding, and only a few software packages support end-to-end analysis. The goal of this study was to implement fully automated, end-to-end MRSI analysis into the open-source 'Osprey-MRSI' software package. Methods: MRSI-specific analysis and visualization capabilities were implemented, building on the existing Osprey workflow. Modifications included spatial transformation and filtering operations, automated brain masking and tissue segmentation of the MRSI data, improved lipid filtering, rapid integral maps, linear-combination modeling with explicit B0 frequency-shift correction, and generation of quality-control maps and metabolic images. A fully interactive GUI and semi-interactive HTML reports provide a user-friendly way to inspect each step of the analysis. All analysis derivatives are also exported in NIfTI and NIfTI-MRS format for easy visualization and synergies with other toolboxes and modalities. Results: The automated MRSI workflow was successfully used to analyze short- and medium-TE 3T in vivo MRSI datasets from all major vendors (Philips, GE, Siemens) across multiple sites. Correct coregistration of MRSI data and MR images was validated using phantom data from each vendor and existing MRSI processing tools. Conclusion: Osprey-MRSI offers state-of-the-art methods with minimal user interaction available for non-expert users. The modularity of the workflow and the modeling algorithm will foster innovation and development of novel MRSI-specific analysis methods.

neuroscience↗

bioRxiv2024

The Proton Resonance Enhancement for CEST imaging and Shift Exchange (PRECISE) family of RF pulse shapes for Chemical Exchange Saturation Transfer MRI

PurposeTo optimize a 100 msec pulse for producing CEST MRI contrast and evaluate in mice. MethodsA gradient ascent algorithm was employed to generate a family of 100 point, 100 msec pulses for use in CEST pulse trains ( PRECISE). Gradient ascent optimizations were performed for exchange rates (kca) = 500 s-1, 1,500 s-1, 2,500 s-1, 3,500 s-1 and 4,500 s-1 and offsets ({Delta}{omega}) = 9.6, 7.8, 4.2 and 2.0 ppm. 7 PRECISE pulse shapes were tested on an 11.7 T scanner using a phantom containing three representative CEST agents with peak saturation B1 = 4 T. The pulse producing the most contrast in phantoms was then evaluated for CEST MRI pH mapping of the kidneys in healthy mice after iopamidol administration. ResultsThe most promising pulse in terms of contrast performance across all three phantoms was the 9.6 ppm, 2500 s-1 optimized pulse with [~]2.7 x improvement over Gaussian and [~]1.3xs over Fermi pulses. This pulse also displayed a large improvement in contrast over the Gaussian pulse after administration of iopamidol in live mice. ConclusionA new 100 msec pulse was developed based on gradient ascent optimizations which produced better contrast compared to standard Gaussian and Fermi pulses in phantoms. This shape also showed a substantial improvement for CEST MRI pH mapping in live mice over the Gaussian shape and appears promising for a wide range of CEST applications.

bioengineering↗

bioRxiv2023

In vivo proton MR Spectroscopy of the healthy and diseased human brain

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, weve identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

neuroscience↗

bioRxiv2023

Downfield Proton MRSI with whole-brain coverage at 3T

PurposeTo develop a 3D downfield magnetic resonance spectroscopic imaging (DF-MRSI) protocol with whole brain coverage and post-processing pipeline for creation of metabolite maps. MethodsA 3D, circularly phase-encoded version of the previously developed 2D DF-MRSI sequence with [Formula] spectral-spatial excitation and frequency selective refocusing was implemented and tested in 5 healthy volunteers at 3T. Downfield metabolite maps with a nominal spatial resolution of 0.7 cm3 were recorded in 8 slices at 3T in a scan time of 22m 40s. An MRSI post-processing pipeline was developed to create DF metabolite maps. Metabolite concentrations and uncertainty estimates were compared between region differences for nine downfield peaks. ResultsLCModel analysis showed CRLB average values of 3-4% for protein amide resonances in the three selected regions (anterior cingulate (ACC), dorsolateral prefrontal cortex (DLPFC), and centrum semiovale (CSO)); CRLBs were somewhat higher for individual peaks but for the most part were less than 20%. While DF concentration maps were visually quite homogeneous throughout the brain, general linear regression analysis corrected for multiple comparisons found significant differences between CSO and DLPFC for peaks at 7.09 ppm (p= 0.014), 7.90 ppm (p=0.009), 8.18 ppm (p=0.009), combined amides (p=0.009), and between ACC and DLPFC for the 7.30 ppm peak (p=0.020). CRLB values were not significantly different between brain regions for any of the DF peaks. Conclusion3D DF-MRSI of the human brain at 3T with wide spatial coverage for the mapping of exchangeable amide and other resonances is feasible at a nominal spatial resolution of 0.7 cm3.

bioengineering↗

bioRxiv2022

The Value of Zero-filling in In Vivo MRS

Two opinions currently exist on the role of zero-filling in data processing for in vivo MRS: that it results in a purely cosmetic interpolation; or that it confers a benefit. Most commonly, in vivo MRS data are acquired as complex time-domain half-echoes, that are Fourier transformed to the give a real spectrum that is modeled for quantification. In this manuscript, we highlight that performing zero-filling draws the independent information from the imaginary part of the spectrum into the real spectrum, improving modeling accuracy. In order to demonstrate this, 10,000 time-domain datasets were simulated as decaying exponentials and noise was added. Data were then Fourier transformed with no-zero filling, 2x, 4x and 8x zero-filling. All spectra were then modeled using a simple single-Lorentzian frequency-domain model. It was demonstrated that 2x zero-filling results in a [~]{surd}2 benefit in modeling accuracy, compared to no zero-filling. There was no additional advantage for further zero-filling.

neuroscience↗

bioRxiv2021

Comparison of linear combination modeling strategies for GABA-edited MRS at 3T

PurposeJ-difference-edited spectroscopy is a valuable approach for the in vivo detection of {gamma}-aminobutyric-acid (GABA) with MRS. A recent expert consensus article recommends linear combination modeling (LCM) of edited MRS but does not give specific details of implementation. This study explores different modeling strategies to adapt LCM for GABA-edited MRS. Methods61 medial parietal lobe GABA-edited MEGA-PRESS spectra from a recent 3T multi-site study were modeled using 102 different strategies combining six different approaches to account for co-edited macromolecules, three modeling ranges, three baseline knot spacings, and the use of basis sets with or without homocarnosine. The resulting GABA and GABA+ estimates (quantified relative to total creatine), the residuals at different ranges, SDs and CVs, and Akaike information criteria, were used to evaluate the models performance. ResultsSignificantly different GABA+ and GABA estimates were found when a well-parameterized MM3co basis function was included in the model. The mean GABA estimates were significantly lower when modeling MM, while the CVs were similar. A sparser spline knot spacing led to lower variation in the GABA and GABA+ estimates, and a narrower modeling range - only including the signals of interest - did not substantially improve or degrade modeling performance. Additionally, results suggest that LCM can separate GABA and the underlying co-edited MM3co. Incorporating homocarnosine into the modeling did not significantly improve variance in GABA+ estimates. ConclusionGABA-edited MRS is most appropriately quantified by LCM with a well-parameterized co-edited MM3co basis function with a constraint to the non-overlapped MM0.93, in combination with a sparse spline knot spacing (0.55 ppm) and a modeling range between 0.5 and 4 ppm. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/445817v3_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@129ce60org.highwire.dtl.DTLVardef@1ac3797org.highwire.dtl.DTLVardef@1759d69org.highwire.dtl.DTLVardef@b190a3_HPS_FORMAT_FIGEXP M_FIG C_FIG 102 strategies to model GABA-edited MRS with linear combination modeling were evaluated to quantify GABA and GABA+ in Osprey. Significantly different GABA and GABA+ estimates were found when a well-parameterized macro-molecule at 3 ppm was included. The findings suggest that linear combination modeling needs to be adapted for quantification of GABA-edited MRS.

biochemistry↗

bioRxiv2021

A multimodal study of a first episode psychosis cohort: potential markers of antipsychotic treatment resistance

Treatment resistant (TR) psychosis is considered to be a significant cause of disability and functional impairment. Numerous efforts have been made to identify the clinical predictors of TR. However, the exploration of molecular and biological markers is still at an early stage. To understand the TR condition and identify potential molecular and biological markers, we analyzed demographic information, clinical data, structural brain imaging data, and molecular brain imaging data in 7 Tesla magnetic resonance spectroscopy, from a first episode psychosis cohort that includes 138 patients. Age, gender, race, smoking status, duration of illness, and antipsychotic dosages were controlled in the analyses. We found that TR patients had a younger age at onset, more hospitalizations, more severe negative symptoms, a significant reduction in the volumes of the hippocampus (HP) and superior frontal gyrus (SFG), and a significant reduction in glutathione (GSH) levels in the anterior cingulate cortex (ACC), when compared to non-TR patients. The combination of multiple markers provided a better classification between TR and non-TR patients compared to any individual marker. Our study shows that ACC GSH, HP and SFG volumes, and age at onset could potentially be trait biomarkers for TR diagnosis, while hospitalization and negative symptoms could be used to evaluate the progression of the disease. Multimodal cohorts are essential in obtaining a comprehensive understanding of brain disorders.

neuroscience↗

bioRxiv2020

Longitudinal changes in brain metabolites in healthy subjects and patients with first episode psychosis (FEP): a 7-Tesla MRS study

Objective7 Tesla (T) longitudinal magnetic resonance spectroscopy (MRS) offers a precise measurment of metabolic levels in human brain via a non-invasive approach. Studying longitudinal changes in neurometabolites could help identify trait and state markers for diseases and understand inconsistent findings from different researchers due to differences in the age of study participants and duration of illness. This study is the first to report novel longitudinal patterns in young adulthood from both physiological and pathological viewpoints using 7T MRS. MethodsUtilizing a four-year longitudinal cohort with 38 first episode psychosis (FEP) patients (onset within 2 years) and 48 healthy controls (HC), the authors examined the annual percentage changes of 9 neurometabolites in 5 brain regions. ResultsBoth FEP patients and HC subjects were found to have significant longitudinal reductions in glutamate (Glu) in the anterior cingulate cortex (ACC). Only FEP patients were found to have a significant decrease over time in {gamma}-aminobutyric acid (GABA), N-acetyl aspartate (NAA), myo-inositol (mI), and total choline (tCho: phosphocholine plus glycerophosphocholine) in the ACC. Uniquely, glutathione (GSH) was found to have a near zero annual percentage change in both FEP patients and HC subjects in all 5 brain regions over a four-year timespan in young adulthood. ConclusionsGSH could be a trait marker for diagnostic applications at least in young adulthood. Glu, GABA, NAA, mI, and tCho in the ACC are associated with the patients status and could be state markers for mechanistic studies of psychotic disorders, including those for progressive pathological changes and medication effects in young adulthood.

neuroscience↗