Biology subjects

Gudmundson, A. T.

Publications and source records attributed to Gudmundson, A. T..

17 recordsLinked to original sources

bioRxiv2026

Metabolite Relaxation Times at 3T From Birth to 7 Years

PurposeWith growing interest in pediatric MRS to understand the neurochemistry of brain development, it is essential to establish pediatric-specific metabolite T1 and T2 relaxation times for reliable quantification of metabolites. Furthermore, characterization of age-related changes in relaxation times may provide insight into developmental differences in tissue microstructure and cellular composition. Therefore, the goals of this study were two-fold: (1) to estimate metabolite T1 and T2 relaxation times in a pediatric cohort; and (2) to assess linear relationships between age and relaxation times. MethodsTI- and TE-series data were acquired from the bilateral thalamus in 20 and 25 participants aged between 0 and 7 years, respectively. Data were processed and modelled in Osprey 2.5.0. Metabolite T1 and T2 relaxation times of total N-acetylaspartate at 2.0 ppm (tNAA2.0), total creatine at 3.0 ppm (tCr3.0), total creatine at 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI) and the combined glutamate and glutamine (Glx) were estimated by fitting the modelled signal amplitudes to TI-series inversion recovery signal and TE-series exponential decay equations, respectively. Linear relationships with age were tested for metabolite T1 and T2 relaxation times using Spearman correlations. ResultstNAA2.0 and Glx showed significant changes in T1 with age. tNAA2.0, tCr3.0, tCho and mI showed significant decrease in T2 with age. ConclusionSeveral metabolites showed significant changes in relaxation times with age in the thalamus region. Therefore, use of age-specific relaxation correction for pediatric studies using the linear equations provided is encouraged for reliable quantification of metabolites.

neuroscience↗

bioRxiv2026

Age-related differences in neurometabolite concentrations during early infancy: A cross-sectional analysis of the HBCD Release 1.0

Background: Developmental trajectories of low-concentration neurometabolites such as the neurotransmitter {gamma}-aminobutyric acid (GABA), and the antioxidants glutathione (GSH) and ascorbate (Asc) across early infancy remain unexplored. Advances in spectral editing enabled the measurement of these key molecules together with high-concentration metabolites like N-acetylaspartate (NAA) and glutamate (Glu) in the HEALthy Brain and Child Development (HBCD) study, the largest longitudinal study of early brain development in the United States. Purpose: To determine the age-associated trajectories of 14 key neurometabolites during early infancy from a cross-sectional 1H-MRS dataset. Materials and Methods: HBCD utilizes ISTHMUS, an integrated MRS sequence that includes both an unedited short-echo-time PRESS acquisition and an advanced 4-step Hadamard-encoded sequence, HERCULES to enable measurement of both high- and low-concentration metabolites. Metabolite quantification was carried out by the HBCD Data Coordinating Center (HDCC) using an automated Osprey pipeline, with data from 201 infants ages 0 - 10 weeks adjusted age included in the tabular imaging results in HBCD data release 1.0. After excluding preterm born infants and data with poor linewidth or model quality metric, we tested for linear associations with adjusted age for each metabolite. Results: Concentration estimates of total NAA (tNAA), total creatine (tCr) and glutamate (Glu) as well as the combined sum of glutamate and glutamine (Glx) significantly increased across ages 0 - 10 weeks, while myo-inositol (mI) decreased. GABA and GSH showed age-related trends, but did not reach significance. Levels of the lipid precursor phosphorylethanolamine (PE) and Asc are higher in the first months than established adult values. Conclusion: Multiple metabolites showed significant age-related changes during early infancy. While GABA and GSH did not, future work will establish whether the trends suggested here contribute to linear or non-linear patterns across the first years of life.

neuroscience↗

bioRxiv2026

Real-time AI integration for MR to detect artifacts and guide pulse sequence adaptations

This work presents a first-of-its-kind artificial intelligence (AI-)integrated MR pulse sequence that detects out-of-voxel (OOV) artifacts in real-time (within-TR) and responds prospectively by updating the crusher gradient scheme. Per Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation (PEREGRINE), allows for deployment of deep learning models and pulse sequence updates. In this study, PEREGRINE operated a time-domain (TD) and frequency-domain (FD) convolutional autoencoder that detect OOV artifacts. Scans without (AI-off) and with (AI-on) updates were collected from the medial prefrontal cortex of healthy volunteers using a MEGA-edited MRS experiment. The degree of OOV contamination (OOV Score) was quantified per transient based upon the prevalence of OOV signals in the TD and FD data. OOV Scores above a user-defined threshold triggered an update of the crusher gradient scheme, iterating through 48 permutations (6 axis transpositions x 8 polarity flips). Within each 2-second TR, PEREGRINE successfully provided single-transient OOV Scores and updated gradients accordingly. No difference was observed between the OOV Scores from the full ("Full" condition) AI-on and AI-off sessions due to the AI-on scan cycling over better and worse gradient permutations relative to the AI-off scan. However, the AI-on scan had significantly lower OOV Scores than the AI-off scan when selecting the transients where PEREGRINE persisted ("Dwell" condition) on a given gradient permutation. Ultimately, Fit Quality Number (FQN) from linear combination modeling improved significantly for the AI-on compared to the AI-off scan. PEREGRINE enabled an adaptive and AI-integrated sequence allowing for real-time evaluation and response to OOV artifacts, identifying gradient modifications that produced less OOV contamination. Graphical AbstractO_ST_ABSGraphical Abstract SummaryC_ST_ABSPEREGRINE, Per Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation, allows for pulse sequence updates using on-scanner deployed neural networks. To demonstrate the utility, PEREGRINE was used to operate two neural networks (time/frequency domain), trained to detect out-of-voxel artifacts, during a MEGA-edited MRS experiment in the prefrontal cortex. When artifacts were detected, PEREGRINE updated the crusher gradients within the same TR. Real-time, adaptive, and AI-driven MR will provide a long-awaited solution for combatting artifacts and poor data quality. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/722724v2_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@cc01e0org.highwire.dtl.DTLVardef@18f4083org.highwire.dtl.DTLVardef@1d48c8corg.highwire.dtl.DTLVardef@1574ba7_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract Figure C_FIG

neuroscience↗

bioRxiv2025

Gradient scheme optimization for PRESS-localized edited MRS using weighted pathway suppression

This study aimed to design and implement an optimized gradient scheme for PRESS-localized edited magnetic resonance spectroscopy (MRS) to enhance suppression of out-of-voxel (OOV) artifacts. These artifacts, which originate from insufficient crushing of unwanted coherence transfer pathways (CTPs), are particularly challenging in editing schemes for metabolites like gamma-aminobutyric acid and glutathione. To address this, a volume-based likelihood model was developed to guide gradient scheme optimization, prioritizing suppression of CTPs based on likelihood. A volume-based likelihood model for CTP weighting was integrated into a Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient optimization. Using a genetic algorithm with a new dual-penalty cost function, gradient schemes were optimized to maximize pathway-specific suppression. Hardware and sequence constraints, maximum gradient amplitudes and delay durations respectively, informed the optimization. Validation of the optimized scheme was performed with simulations by calculating the k-space crushing efficiency analytically with k-space trajectory. and in vivo using an edited MRS sequence in three brain regions (posterior cingulate cortex PCC, thalamus, and medial prefrontal cortex (mPFC)), with particular focus on OOV artifact reduction and spectral quality improvements. A three-way Analysis of Variance was used to assess the significance level of OOV artifact reduction. The optimized gradient scheme demonstrated improved k-space crushing efficiency (by an average of 197%). OOV artifacts were reduced in all brain regions, particularly in highly OOV-susceptible regions (thalamus and mPFC). Improvements were most notable around 4.3 ppm with significant OOV artifact amplitude reductions (p < 0.001). By using a volume-based likelihood model for CTP prioritization, the optimized DOTCOPS scheme ensures robust and region-agnostic performance in reducing OOV artifacts.

neuroscience↗

bioRxiv2025

Identifying out-of-voxel echoes in edited MRS with phase cycle inversion

1.PurposeTo identify the origin of out-of-voxel (OOV) signals based on the coherence transfer pathway (CTP) formalism using signal phase conferred by the acquisition phase cycling scheme. Knowing the CTP driving OOV artifacts enables optimization of crusher gradients to improve their suppression without additional data acquisition. Theory and MethodsA phase cycle systematically changes the phase of RF pulses across the transients of an experiment, encoding phase shifts into the data that can be used to suppress unwanted CTPs. We present a new approach, phase cycle inversion (PCI), which removes the receiver phase originally applied to the stored transients, replacing it with new receiver phases, matching the phase evolutions associated with each unwanted CTP, to identify the OOV signals. We demonstrated the efficacy of PCI using the MEGA-edited PRESS sequence in simulations, phantom and in vivo experiments. Based on these findings, the crusher gradient scheme was optimized. ResultsThe simulation results demonstrated that PCI can fully separate signals originating from different CTPs using a complete phase cycling scheme. PCI effectively identified the CTP responsible for OOV signals in phantom experiments and in vivo, though with reduced specificity in vivo due to phase instabilities. Re-optimization of the gradient scheme based on the identified OOV-associated CTP to suppress these signals, resulted in cleaner spectra in six volunteers. ConclusionPCI can be broadly applied across pulse sequences and voxel locations, making it a flexible and generalizable approach for diagnosing the CTP origin of OOV signals.

neuroscience↗

bioRxiv2024

Age dependency of neurometabolite T1 relaxation times

PurposeTo measure T1 relaxation times of metabolites at 3T in a healthy aging population and investigate age dependence. MethodsA cohort of 101 healthy adults were recruited with approximately 10 male and 10 female participants in each decade band: 18-29, 30-39, 40-49, 50-59, and 60+ years old. Inversion-recovery PRESS data (TE/TR: 30/2000 ms) were acquired at 8 inversion times (TIs) (300, 400, 511, 637, 780, 947, 1148 and 1400 ms) from voxels in white-matter-rich centrum semiovale (CSO) and gray-matter-rich posterior cingulate cortex (PCC). Modeling of TI-series spectra was performed in Osprey 2.5.0. Quantified metabolite amplitudes for total N-acetylaspartate (tNAA2.0), total creatine at 3.0 ppm (tCr3.0) and 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI), and the sum of glutamine and glutamate (Glx) were modeled to calculate T1 relaxation times of metabolites. ResultsT1 relaxation times of tNAA2.0 in CSO and tNAA2.0, tCr3.0, mI and Glx in PCC decreased with age. These correlations remained significant when controlling for cortical atrophy. T1 relaxation times were significantly different between PCC and CSO for all metabolites except tCr3.0. We also propose linear models for predicting metabolite T1s at 3T to be used in future aging studies. ConclusionMetabolite T1 relaxation times change significantly with age, an effect that will be important to consider for accurate quantitative MRS, particularly in studies of aging.

neuroscience↗

bioRxiv2024

A Water Relaxation Atlas for Age and Region specific Metabolite Concentration Correction

Metabolite concentration estimates from Magnetic Resonance Spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time differences between metabolites and water. One common approach is to correct the water reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T1 and T2) vary between brain locations, in pathology, and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location- and age-appropriate relaxation values into the MRS analysis workflow. 101 volunteers (51 men, 50 women; [~]10 male and 10 female participants per decade (from the 20s to 60s), were recruited. T1-weighted MPRAGE images ((1-mm)3 isotropic resolution) were acquired. Whole-brain water T1 and T2 measurements were made with DESPOT ((1.4 mm)3 isotropic resolution). T1 and T2 maps were registered to the JHU MNI-SS/EVE atlas using affine and LDDMM transformation. The atlass 268 parcels were reduced to 130 by combining homologous parcels. Mean T1 and T2 values were calculated for each parcel in each subject. Linear models of T1 and T2 as functions of age were computed, using age - 30 as the predictor. Reference atlases of "age-30-intercept" and age-slope for T1 and T2 were generated. The atlas-based workflow was integrated into Osprey, which co-registers MRS voxels to the atlas and calculates location- and age-appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location- and subject-appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water-referenced tissue correction, especially for studies of aging.

neuroscience↗

bioRxiv2024

Comparison of test-retest reproducibility of DESPOT and 3D-QALAS for water T1 and T2 mapping

PurposeRelaxometry, specifically T1 and T2 mapping, has become an essential technique for assessing the properties of biological tissues related to various physiological and pathological conditions. Many techniques are being used to estimate T1 and T2 relaxation times, ranging from the traditional inversion or saturation recovery and spin-echo sequences to more advanced methods. Choosing the appropriate method for a specific application is critical since the precision and accuracy of T1 and T2 measurements are influenced by a variety of factors including the pulse sequence and its parameters, the inherent properties of the tissue being examined, the MRI hardware, and the image reconstruction. The aim of this study is to evaluate and compare the test-retest reproducibility of two advanced MRI relaxometry techniques (Driven Equilibrium Single Pulse Observation of T1 and T2, DESPOT, and 3D Quantification using an interleaved Look-Locker acquisition Sequence with a T2 preparation pulse, QALAS), for T1 and T2 mapping in a healthy volunteer cohort. Methods10 healthy volunteers underwent brain MRI at 1.3 mm3 isotropic resolution, acquiring DESPOT and QALAS data ([~]11.8 and [~]5 minutes duration, including field maps, respectively), test-retest with subject repositioning, on a 3.0 Tesla Philips Ingenia Elition scanner. To reconstruct the T1 and T2 maps, we used an equation-based algorithm for DESPOT and a dictionary-based algorithm that incorporates inversion efficiency and B1-field inhomogeneity for QALAS. The test-retest reproducibility was assessed using the coefficient of variation (CoV), intraclass correlation coefficient (ICC) and Bland-Altman plots. ResultsOur results indicate that both the DESPOT and QALAS techniques demonstrate good levels of test-retest reproducibility for T1 and T2 mapping across the brain. Higher whole-brain voxel-to-voxel ICCs are observed in QALAS for T1 (0.84 {+/-} 0.039) and in DESPOT for T2 (0.897 {+/-} 0.029). The Bland-Altman plots show smaller bias and variability of T1 estimates for QALAS (mean of -0.02 s, and upper and lower limits of -0.14 and 0.11 s, 95% CI) than for DESPOT (mean of -0.02 s, and limits of -0.31 and 0.27 s). QALAS also showed less variability (mean 1.08 ms, limits -1.88 to 4.04 ms) for T2 compared to DESPOT (mean of 2.56 ms, and limits -17.29 to 22.41 ms). The within-subject CoVs for QALAS range from 0.6% (T2 in CSF) to 5.8% (T2 in GM), while for DESPOT they range from 2.1% (T2 in CSF) to 6.7% (T2 in GM). The between-subject CoVs for QALAS range from 2.5% (T2 in GM) to 12% (T2 in CSF), and for DESPOT they range from 3.7% (T2 in WM) to 9.3% (T2 in CSF). ConclusionOverall, QALAS demonstrated better reproducibility for T1 and T2 measurements than DESPOT, in addition to reduced acquisition time.

neuroscience↗

bioRxiv2024

Metabolite T2 relaxation times decrease across the adult lifespan in a large multi-site cohort

PurposeRelaxation correction is crucial for accurately estimating metabolite concentrations measured using in vivo magnetic resonance spectroscopy (MRS). However, the majority of MRS quantification routines assume that relaxation values remain constant across the lifespan, despite prior evidence of T2 changes with aging for multiple of the major metabolites. Here, we comprehensively investigate correlations between T2 and age in a large, multi-site cohort. MethodsWe recruited approximately 10 male and 10 female participants from each decade of life: 18-29, 30-39, 40-49, 50-59, and 60+ years old (n=101 total). We collected PRESS data at 8 TEs (30, 50, 74, 101, 135, 179, 241, and 350 ms) from voxels placed in white-matter-rich centrum semiovale (CSO) and gray-matter-rich posterior cingulate cortex (PCC). We quantified metabolite amplitudes using Osprey and fit exponential decay curves to estimate T2. ResultsOlder age was correlated with shorter T2 for tNAA, tCr3.0, tCr3.9, tCho, Glx, and tissue water in CSO and PCC; rs = -0.21 to -0.65, all p<0.05, FDR-corrected for multiple comparisons. These associations remained statistically significant when controlling for cortical atrophy. T2 values did not differ across the adult lifespan for mI. By region, T2 values were longer in the CSO for tNAA, tCr3.0, tCr3.9, Glx, and tissue water and longer in the PCC for tCho and mI. ConclusionThese findings underscore the importance of considering metabolite T2 changes with aging in MRS quantification. We suggest that future 3T work utilize the equations presented here to estimate age-specific T2 values instead of relying on uniform default values.

neuroscience↗

bioRxiv2024

Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for Advanced MRS

BackgroundTo examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study. MethodsISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5{+/-}6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T2 correction based on the dual-TE water integrals were compared with: 1) T2 correction based the default white matter and gray matter T2 reference values in Osprey; 2) shorter WM and GM T2 values from recent literature; and 3) reduced CSF fractions. ResultsNo significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T2 correction based on shorter T2 values showed better agreement to the dual-TE water integral ratio. ConclusionsISTHMUS facilitated and standardized acquisition and post-processing and reduced operator workload to eliminate potential human error. HighlightsO_LIISTHMUS has been implemented into the HBCD study protocol. C_LIO_LIIt acquires both short-TE and Hadamard-edited transients. C_LIO_LIISTHMUS reduces operator workload. C_LIO_LIISTHMUS potentially allows improved T2 relaxation correction C_LI

bioengineering↗

bioRxiv2023

"Surviving and Thriving": Evidence for Cortical GABA Stabilization in Cognitively-Intact Oldest-Old Adults

Cortical GABA levels are reduced in older age; age-related differences in GABA may be associated with age-related cognitive change. The nature of age-related GABA differences in the highest-functioning stratum of the oldest-old (85+) population is not yet known. We extend our previously-reported Individual Participant Data Meta-Analysis of GABA levels (Porges et al., 2021) across the lifespan with four novel datasets sampling the cognitively-intact oldest-old. The slope of age-related GABA differences in cognitively-intact oldest-old adults flattens after roughly age 80. We interpret these findings as an effect of survivorship: inclusion in the study required intact cognition, and too great a reduction of GABA levels may not be compatible with neurophysiological function needed for intact cognition. This work contributes to a growing body of evidence suggesting that successful cognitive aging may require intact GABAergic function, as well as further characterizing successful aging amongst oldest-old adults.

neuroscience↗

bioRxiv2023

Reduced brain glutathione levels during normal aging are associated with visuospatial memory

During aging, the brain is subject to greater oxidative stress (OS), which is thought to play a critical role in cognitive impairment. Glutathione (GSH), as a major antioxidant in the brain, can be used to combatting OS. However, how brain GSH levels vary with age and their associations with cognitive function remain unclear. In this study, we combined point-resolved spectroscopy and edited spectroscopy sequences to investigate GSH levels in the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and occipital cortex (OC) of 276 healthy participants (166 females, age range 20-70 years) and examined their relationships with age and cognitive function. The results revealed decreased GSH levels with age in the PCC among all participants. Notably, the timecourse of GSH level changes in the PCC and ACC differed between males and females. Additionally, positive correlations were observed between GSH levels in the PCC and OC and visuospatial memory. Taken together, these findings enhance our understanding of the brain GSH timecourse during normal aging and associations with sex and memory, which is an essential first step for understanding the neurochemical underpinnings of OS-related diseases.

neuroscience↗

bioRxiv2023

The AGNOSTIC MRS Benchmark Dataset: Deep Learning for Out-of-voxel Artifacts

Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.

neuroscience↗

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

Metabolite T1 relaxation times differ across the adult lifespan

PurposeTo investigate the age-dependence of metabolite T1 relaxation times at 3T in both gray- and white-matter-rich voxels. MethodsThis manuscript analyzes publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3T using PRESS localization. Voxels were placed in posterior cingulate cortex and centrum semiovale in 102 healthy volunteers across 5 decades of life (20s to 60s). All spectra were analyzed in Osprey v2.4.0. To estimate T1 relaxation times for tNAA2.0 and tCr3.0, the ratio of modeled metabolite residual amplitudes in the metabolite-nulled spectrum to the full metabolite signal was calculated using the single inversion recovery signal equation. Correlations between T1 and subject age were evaluated. ResultsSpearman correlations revealed that estimated T1 relaxation times of tNAA2.0 (rs = -0.43; p < 0.001) and tCr3.0 (rs = -0.23; p = 0.021) decreased significantly with age in white-matter-rich CSO, and less steeply (and not significantly) for tNAA2.0 (rs = -0.15; p = 0.136) and tCr3.0 (rs = -0.10; p = 0.319) in gray-matter-rich PCC. ConclusionThe analysis harnessed a large publicly available cross-sectional dataset to test an important hypothesis, that metabolite T1 relaxation times change with age. This preliminary study stresses the importance of further work to measure age-normed metabolite T1 relaxation times for accurate quantification of metabolite levels in studies of aging.

neuroscience↗

bioRxiv2022

Continuous automated MRS data analysis workflow for applied studies

BackgroundMagnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review. New MethodThe proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps. ResultsThe automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory. Comparison with Existing Method(s)The workflow presented here is the first implementation of a continuous automated MRS analysis ecosystem based on NIfTI-MRS and BIDS-MRS standards. Traditional MRS workflows are non-standardized, often require manual input, and frequently noy compatible with established neuroimaging workflows. This workflow should therefore facilitate integration of MRS into large-scale and multi-center studies. ConclusionsContinuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies.

neuroscience↗

bioRxiv2022

Neurometabolic timecourse of healthy aging

PurposeThe neurometabolic timecourse of healthy aging is not well-established, in part due to diversity of quantification methodology. In this study, a large structured cross-sectional cohort of male and female subjects throughout adulthood was recruited to investigate neurometabolic changes as a function of age, using consensus-recommended magnetic resonance spectroscopy quantification methods. Methods102 healthy volunteers, with approximately equal numbers of male and female participants in each decade of age from the 20s, 30s, 40s, 50s, and 60s, were recruited with IRB approval. MR spectroscopic data were acquired on a 3T MRI scanner. Metabolite spectra were acquired using PRESS localization (TE = 30 ms; 96 transients) in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Water-suppressed spectra were modeled using the Osprey algorithm, employing a basis set of 18 simulated metabolite basis functions and a cohort-mean measured macromolecular spectrum. Pearson correlations were conducted to assess relationships between metabolite concentrations and age for each voxel; paired t-tests were run to determine whether metabolite concentrations differed between the PCC and CSO. ResultsTwo datasets were excluded (1 ethanol; 1 unacceptably large lipid signal). Statistically significant age-by-metabolite correlations were seen for tCr (R2=0.36; p<0.001), tCho (R2=0.11; p<0.001), sI (R2=0.11; p=0.004), and mI (R2=0.10; p<0.001) in the CSO, and tCr (R2=0.15; p<0.001), tCho (R2=0.11; p<0.001), and GABA (R2=0.11; p=0.003) in the PCC. No significant correlations were seen between tNAA, NAA, GSH, Glx or Glu and age in either region (all p>0.25). Levels of sI were significantly higher in the PCC in female subjects (p<0.001) than in male subjects. There was a significant positive correlation between linewidth and age. ConclusionThe results indicated age correlations for tCho, tCr, sI, and mI in CSO and for tCr, tCho and GABA in PCC, while no age-related changes were found for NAA, tNAA, GSH, Glu or Glx. Our results provide a normative foundation for future work investigating the neurometabolic time course of healthy aging using MRS. HighlightsO_LIA large structured cross-sectional cohort of neurometabolic aging dataset is presented; C_LIO_LIAge correlations were observed for tCho, tCr, sI, and mI in CSO and for tCr, tCho and GABA in PCC; C_LIO_LINo age correlations were found for NAA, tNAA, GSH, Glu or Glx in either region. C_LI

neuroscience↗