Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts' consensus recommendations

Chemical shift and relaxation regularization improve the accuracy of 1H MR spectroscopy analysis

PURPOSE

Accurate analysis of metabolite levels from 1H MRS data is a significant challenge, typically requiring the estimation of approximately 100 parameters from a single spectrum. Signal overlap, spectral noise, and common artifacts further complicate the analysis, leading to instability and reports of poor agreement between different analysis approaches. One inconsistently used method to improve analysis stability is known as regularization, where poorly determined parameters are partially constrained to take a predefined value. In this study, we examine how regularization of frequency and linewidth parameters influences analysis accuracy.

METHODS

The accuracy of three MRS analysis methods was compared: (1) ABfit, (2) ABfit-reg, and (3) LCModel, where ABfit-reg is a modified version of ABfit incorporating regularization. Accuracy was assessed on synthetic MRS data generated with random variability in the frequency shift and linewidth parameters applied to each basis signal. Spectra (N = 1000 $$ N=1000 $$ ) were generated across a range of SNR values (10, 30, 60, 100) to evaluate the impact of variable data quality.

RESULTS

Comparison between ABfit and ABfit-reg demonstrates a statistically significant (p <  0.0005) improvement in accuracy associated with regularization for each SNR regime. An approximately 10% reduction in the mean squared metabolite errors was found for ABfit-reg compared to LCModel for SNR >10 (p <  0.0005). Furthermore, Bland-Altman analysis shows that incorporating regularization into ABfit enhances its agreement with LCModel.

CONCLUSION

Regularization is beneficial for MRS fitting and accurate characterization of the frequency and linewidth variability in vivo may yield further improvements.

Age dependency of neurometabolite T1 relaxation times

PURPOSE

To measure T1 relaxation times of metabolites at 3 T in a healthy aging population and investigate age dependence.

METHODS

A cohort of 101 healthy adults was recruited with approximately 10 male and 10 female participants in each "decade" band: 18 to 29, 30 to 39, 40 to 49, 50 to 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.

RESULTS

T1 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 3 T to be used in future aging studies.

CONCLUSION

Metabolite T1 relaxation times change significantly with age, an effect that will be important to consider for accurate quantitative MRS, particularly in studies of aging.

Pons metabolite alterations in narcolepsy type 1

INTRODUCTION

Narcolepsy type 1 (NT1) is a rare central sleep disorder characterized by a selective loss of hypocretin/orexin (hcrt)-producing neurons in the postero-lateral hypothalamus that project to widespread areas of the brain and brainstem. The aim of this study was to explore in a group of NT1 patients the metabolic alterations in the pons and their associations with disease features.

METHODS

Twenty-one NT1 patients (16 M) and twenty age-matched healthy controls (10 M) underwent a brain 1H MRS on a 1.5 T GE Medical Systems scanner. Metabolite content of N-acetyl-aspartate (NAA), choline (Cho), and myo-inositol (mI) were estimated relative to creatine (Cr), using LCModel 6.3. Clinical data were also collected with validated questionnaires, polysomnography, the Multiple Sleep Latency Test (MSLT), Cerebrospinal fluid hypocretin-1 (CSF hcrt-1) concentration and genetic markers.

RESULTS

NT1 patients compared with healthy controls showed lower NAA/Cr ratio (p = 0.007) and NAA/mI ratio (p = 0.011) in the pons. The Epworth Sleepiness Scale score showed a significant negative correlation with NAA/Cr content (p = 0.023), MSLT sleep latency a negative correlation with the mI/Cr ratio (p = 0.008), and sleep onset REM periods a positive correlation with the mI/Cr ratio (p = 0.027). CSF hcrt-1 levels were positively correlated with the NAA/Cr ratio (p = 0.039) and negatively with the mI/Cr ratio (p = 0.045) and the Cho/Cr ratio (p = 0.026).

CONCLUSION

The metabolic alterations found in the pons of NT1 patients using the MR Spectroscopy technique were associated with subjective and objective disease severity measures, highlighting the crucial role of this biomarker in the pathophysiology of the disease.

Deep neural network modeling for brain tumor classification using magnetic resonance spectroscopic imaging

This study is driven by the complex and specialized nature of magnetic resonance spectroscopy imaging (MRSI) data processing, particularly within the scope of brain tumor assessments. Traditional methods often involve intricate manual procedures that demand considerable expertise. In response, we investigate the application of deep neural networks directly to raw MRSI data in the time domain. Given the significant health risks associated with brain tumors, the necessity for early and accurate detection is crucial for effective treatment. While conventional MRI techniques encounter limitations in the rapid and precise spatial evaluation of diffuse gliomas, both accuracy and efficiency are often compromised. MRSI presents a promising alternative by providing detailed insights into tissue chemical composition and metabolic changes. Our proposed model, which utilizes deep neural networks, is specifically designed for the analysis and classification of spectral time series data. Trained on a dataset that includes both synthetic and real MRSI data from brain tumor patients, the model aims to distinguish MRSI voxels that indicate pathological conditions from healthy ones. Our findings demonstrate the model's robustness in classifying glioma-related MRSI voxels from those of healthy tissue, achieving an area under the receiver operating characteristic curve of 0.95. Overall, these results highlight the potential of deep learning approaches to harness raw MR data for clinical applications, signaling a transformative impact on diagnostic and prognostic assessments in brain tumor examinations. Ongoing research is focused on validating these approaches across larger datasets, to establish standardized guidelines and enhance their clinical utility.

Feasibility of using Gramian angular field for preprocessing MR spectroscopy data in AI classification tasks: Differentiating glioblastoma from lymphoma

OBJECTIVES

To convert 1D spectra into 2D images using the Gramian angular field, to be used as input for convolutional neural network for classification tasks such as glioblastoma versus lymphoma.

MATERIALS AND METHODS

Retrospective study including patients with histologically confirmed glioblastoma and lymphoma between 2009-2020 who underwent preoperative MR spectroscopy, using single voxel spectroscopy acquired with a short echo time (TE 30). We compared: 1) the Fourier-transformed raw spectra, and 2) the fitted spectra generated during post-processing. Both spectra were independently converted into images using the Gramian angular field, and then served as inputs for a pretrained neural network. We compared the classification performance using data from the Fourier-transformed raw spectra and the post-processed fitted spectra.

RESULTS

This feasibility study included 98 patients, of whom 65 were diagnosed with glioblastomas and 33 with lymphomas. For algorithm testing, 20 % of the cases (19 in total) were randomly selected. By applying the Gramian angular field technique to the Fourier-transformed spectra, we achieved an accuracy of 73.7 % and a sensitivity of 92 % in classifying glioblastoma versus lymphoma, slightly higher than the fitted spectra pathway.

CONCLUSION

Spectroscopy data can be effectively transformed into distinct color graphical images using the Gramian angular field technique, enabling their use as input for deep learning algorithms. Accuracy tends to be higher when utilizing data derived from Fourier-transformed spectra compared to fitted spectra. This finding underscores the potential of using MR spectroscopy data in neural network-based classification purposes.

Quantification of NAD+ T1 and T2 Relaxation Times Using Downfield 1H MRS at 7 T in Human Brain In Vivo

The purpose of this study was to measure T1 and T2 relaxation times of NAD+ proton resonances in the downfield 1H MRS spectrum in human brain at 7 T in vivo and to assess the propagation of relaxation time uncertainty in NAD+ quantification. Downfield spectra from eight healthy volunteers were acquired at multiple echo times to measure T2 relaxation times, and saturation recovery data were acquired to measure T1 relaxation times. The downfield acquisition used a spectrally selective 90° sinc pulse for excitation centered at 9.1 ppm with a bandwidth of 2 ppm, followed by a 180° spatially selective Shinnar-Le Roux refocusing pulse for localization. Uncertainty propagation analysis on metabolite quantification was performed analytically and with Monte Carlo simulation. [NAD+] was quantified in five participants. The mean ± standard deviation of T1 relaxation times of the H2, H6, and H4 NAD+ protons were 205.6 ± 25.7, 211.6 ± 33.5, and 237.3 ± 42.4 ms, respectively. The mean ± standard deviation of T2 relaxation times of the H2, H6, and H4 protons were 33.6 ± 7.4, 29.1 ± 4.7, and 42.3 ± 11.6 ms, respectively. The relative uncertainty in NAD+ concentration due to relaxation time uncertainty was 8.4%-11.4%, and measured brain [NAD+] (N = 5) was 0.324 ± 0.050 mM. Using downfield spectrally selective spectroscopy with single-slice localization, we found T1 and T2 relaxation times averaged across all NAD+ resonances to be approximately 218 and 35 ms, respectively, in the human brain in vivo at 7 T.

Toward quantitative CEST imaging of glutamate in the mouse brain using a multi-pool exchange model calibrated by 1H-MRS

PURPOSE

To develop a CEST quantification model to map glutamate concentration in the mouse brain at 11.7 T, overcoming the limitations of conventional glutamate-weighted CEST (gluCEST) contrast (magnetization transfer ratio with asymmetric analysis).

METHODS

1H-MRS was used as a gold standard for glutamate quantification to calibrate a CEST-based quantitative pipeline. Joint localized measurements of Z-spectra at B1 = 5 μT and quantitative 1H-MRS were carried out in two voxels of interest in the mouse brain. A six-pool Bloch-McConnell model was found appropriate to fit experimental data. Glutamate exchange rate was estimated in both regions with this dedicated multi-pool fitting model and using glutamate concentration determined by 1H-MRS.

RESULTS

Glutamate exchange rate was estimated to be ˜1300 Hz in the mouse brain. Using this calibrated value, maps of glutamate concentration in the mouse brain were obtained by pixel-by-pixel fitting of Z-spectra at B1 = 5 μT. A complementary study of simulations, however, showed that the quantitative model has high sensitivity to noise, and therefore, requires high-SNR acquisitions. Interestingly, fitted [Glu] seemed to be overestimated compared to 1H-MRS measurements, although it was estimated with simulations that the model has no intrinsic fitting bias with our experimental level of noise. The hypothesis of an unknown proton-exchanging pool contributing to gluCEST signal is discussed.

CONCLUSION

High-resolution mapping of glutamate in the brain was made possible using the proposed calibrated quantification model of gluCEST data. Further studying of the in vivo molecular contributions to gluCEST signal could improve modeling.

Topographical mapping of metabolic abnormalities in multiple sclerosis using rapid echo-less 3D-MR spectroscopic imaging at 7T

OBJECTIVES

To assess topographical patterns of metabolic abnormalities in the cerebrum of multiple sclerosis (MS) patients and their relationship to clinical disability using rapid echo-less 3D-MR spectroscopic imaging (MRSI) at 7T.

MATERIALS AND METHODS

This study included 26 MS patients (13 women; median age 34) and 13 age- and sex-matched healthy controls (7 women; median age 33). Metabolic maps were obtained using echo-less 3D-MRSI at 7T with a 64 × 64 × 33 matrix and a nominal voxel size of 3.4 × 3.4 × 4 mm³ in an 8-minute scan. After spatial normalization, voxel-wise comparisons between MS and controls were conducted to identify clusters of metabolic abnormalities, while correlations with clinical disability were analyzed using Expanded Disability Status Scale (EDSS) scores.

RESULTS

Statistical mapping (FWE-corrected; P<.05) revealed elevated myo-inositol to total creatine (mI/tCr) ratios in the bilateral periventricular white matter and reduced N-acetylaspartate to total creatine (NAA/tCr) within and beyond lesions, notably near the lateral ventricles, cingulate gyrus, and superior frontal gyrus. Patients with sustained disability (EDSS≥2) showed additional reductions in the posterior parietal lobe. A strong negative association was found between NAA/tCr and EDSS in the precentral gyrus (Spearman's rank ρ=-0.58, P=.005), and a moderate positive association between mI/NAA and EDSS in the precentral and superior frontal gyri (ρ=0.47, P=.015).

CONCLUSIONS

This study highlights the ability of 3D-MRSI at 7T to map widespread metabolic abnormalities in MS, with NAA reductions in prefrontal, motor, and sensory areas, linked to neuroaxonal damage and disability progression, and elevated mI in periventricular regions, reflecting gliosis.

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

PURPOSE

Relaxation correction is crucial for accurately estimating metabolite concentrations measured using in vivo 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.

METHODS

We recruited approximately 10 male and 10 female participants from each decade of life: 18-29, 30-39, 40-49, 50-59, and 60+ y old (n = 101 total). We collected PRESS data at eight 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.

RESULTS

Older age was correlated with shorter T2 for tNAA2.0, tCr3.0, tCr3.9, tCho, and tissue water (CSO and PCC), as well as mI and Glx (PCC only); rs = -0.22 to -0.63, all p < 0.05, false discovery rate (FDR)-corrected. These associations largely remained statistically significant when controlling for cortical atrophy. By region, T2 values were longer in the CSO for tNAA2.0, tCr3.9, Glx, and tissue water and longer in the PCC for tCho and mI. T2 did not differ by region for tCr3.0.

CONCLUSION

These findings underscore the importance of considering metabolite T2 differences 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.

A data-driven algorithm to determine 1H-MRS basis set composition

Purpose

Metabolite amplitude estimates derived from linear combination modeling of MR spectra depend upon the precise list of constituent metabolite basis functions used (the "basis set"). The absence of clear consensus on the "ideal" composition or objective criteria to determine the suitability of a particular basis set contributes to the poor reproducibility of MRS. In this proof-of-concept study, we demonstrate a novel, data-driven approach for deciding the basis-set composition using Bayesian information criteria (BIC).

Methods

We have developed an algorithm that iteratively adds metabolites to the basis set using iterative modeling, informed by BIC scores. We investigated two quantitative "stopping conditions", referred to as max-BIC and zero-amplitude, and whether to optimize the selection of basis set on a per-spectrum basis or at the group level. The algorithm was tested using two groups of synthetic in-vivo-like spectra representing healthy brain and tumor spectra, respectively, and the derived basis sets (and metabolite amplitude estimates) were compared to the ground truth.

Results

All derived basis sets correctly identified high-concentration metabolites and provided reasonable fits of the spectra. At the single-spectrum level, the two stopping conditions derived the underlying basis set with 77-87% accuracy. When optimizing across a group, basis set determination accuracy improved to 84-92%.

Conclusion

Data-driven determination of the basis set composition is feasible. With refinement, this approach could provide a valuable data-driven way to derive or refine basis sets, reducing the operator bias of MRS analyses, enhancing the objectivity of quantitative analyses, and increasing the clinical viability of MRS.

Simultaneous Concentration and T2 Mapping of Brain Metabolites by Fast Multi-Echo Spectroscopic Imaging

The purpose of this study was to produce metabolite-specific T2 and concentration maps in a clinically compatible time frame. A multi-TE 2D MR spectroscopic imaging (MRSI) experiment (multi-echo single-shot MRSI [MESS-MRSI]) deployed truncated and partially sampled multi-echo trains from single scans and was combined with simultaneous multiparametric model fitting. It was tested in vivo for the brain in five healthy subjects. Cramér-Rao lower bounds (CRLB) were used as the measure of performance. The novel method was compared with (1) traditional multi-echo multi-shot (MEMS) MRSI and, as proof of concept, with (2) a truncated version of MEMS, which mimics the MESS acquisition (MESS-mocked) on the original fully sampled MEMS dataset. MESS-MRSI simultaneously yields concentration and T2 maps with a nominal voxel size of ~2 cm3 with a 16 × 16 FOV matrix in 7 min scan time. The estimated values not only align well with the equivalent mocked experiment but are also in good agreement with the traditional threefold longer MEMS acquisition. The MESS-MRSI scheme extends former findings for single-voxel MESS, with improvements in CRLB ranging from 17% to 45% for concentrations and 10% to 23% for T2s when compared to traditional MEMS. This finding suggests that concentrations and T2 times can be reliably estimated in a multi-echo spectroscopic imaging exam by trading off spectral resolution (for some of the acquired TEs) with a significant reduction in scan time, as long as (1) an appropriate bidimensional frequency-TE model is deployed and (2) one TE is sampled in full. Thus, high spectral resolution information can be injected to the partially sampled TEs during fitting by prior knowledge from the one fully sampled TE. Tissue-type and regional distributions of 16 metabolite concentrations align well with the literature, and T2 distributions for five major metabolites are described by region and tissue. The novel MRSI acquisition strategy, based on partially sampled single-shot multi-echo trains twinned to multiparametric fitting, is optimally suited to provide simultaneous 2D concentration and T2 maps in clinic-compatible scan times. MESS principles allow embedding advanced MRSI techniques to further improve speed, coverage, or resolution. Preliminary findings from a cohort of five subjects reveal correlations between T2 relaxation times and the relative fraction of gray/white matter, suggesting tissue-type-dependent microstructural changes.

Neurotransmitter imbalance, glutathione depletion and concomitant susceptibility increase in Parkinson's disease

BACKGROUND

Emerging insights into the pathophysiology of Parkinson's disease (PD) underscore the involvement of dysregulated neurotransmission, iron accumulation and oxidative stress. Nonetheless, the excitatory and inhibitory neurometabolites, the antioxidant glutathione (GSH), and magnetic susceptibility are seldom studied together in the clinical PD literature.

METHODS

We acquired MEGA-PRESS and multi-echo gradient echo sequences from 60 PD patients and 47 healthy controls (HCs). Magnetic resonance spectroscopy voxels were respectively positioned in the midbrain to quantify neurotransmitter including γ-aminobutyric acid (GABA) and glutamate plus glutamine, and in the left striatum to estimate GSH levels. Group differences in metabolite levels normalized to total creatine (Cr) and their clinical relevance were determined. Furthermore, relationships among GSH levels, neurotransmitter estimates and susceptibility values were explored in both PD patients and HCs.

RESULTS

PD patients exhibited reduced midbrain GABA levels (P = 0.034, PFDR = 0.136), diminished GSH in the left striatum (P = 0.032, PFDR = 0.096), and increased susceptibility values in the substantia nigra (PFDR < 0.001). Mesencephalic choline levels were correlated with the severity of rapid eye movement sleep behavior disorders symptoms, whereas striatal N-acetylaspartate levels were linked to Hoehn-Yahr stage and motor symptom severity. Notably, the disruption of associations between striatal GSH levels and susceptibility values in globus pallidus, as well as midbrain GABA levels, were evident in PD.

CONCLUSIONS

These findings offer compelling evidence for metabolic dysregulation in PD, characterized by a concomitant reduction in GABA and GSH levels, alongside iron deposition.

The Role of Proton Magnetic Resonance Spectroscopy in Neonatal and Fetal Brain Research

The biochemical composition and structure of the brain are in a rapid change during the exuberant stage of fetal and neonatal development. 1H-MRS is a noninvasive tool that can evaluate brain metabolites in healthy fetuses and infants as well as those with neurological diseases. This review aims to provide readers with an understanding of 1) the basic principles and technical considerations relevant to 1H-MRS in the fetal-neonatal brain and 2) the role of 1H-MRS in early fetal-neonatal development brain research. We performed a PubMed search to identify original studies using 1H-MRS in neonates and fetuses to establish the clinical applications of 1H-MRS. The eligible studies for this review included original research with 1H-MRS applications to the fetal-neonatal brain in healthy and high-risk conditions. We ran our search between 2000 and 2023, then added in several high-impact landmark publications from the 1990s. A total of 366 results appeared. After, we excluded original studies that did not include fetuses or neonates, non-proton MRS and non-neurological studies. Eventually, 110 studies were included in this literature review. Overall, the function of 1H-MRS in healthy fetal-neonatal brain studies focuses on measuring the change of metabolite concentrations during neurodevelopment and the physical properties of the metabolites such as T1/T2 relaxation times. For high-risk neonates, studies in very low birth weight preterm infants and full-term neonates with hypoxic-ischemic encephalopathy, along with examining the associations between brain biochemistry and cognitive neurodevelopment are most common. Additional high-risk conditions included infants with congenital heart disease or metabolic diseases, as well as fetuses of pregnant women with hypertensive disorders were of specific interest to researchers using 1H-MRS. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Simultaneous frequency and phase corrections of single-shot MRS data using cross-correlation

PURPOSE

The objective of this study was to propose a novel preprocessing approach to simultaneously correct for the frequency and phase drifts in MRS data using cross-correlation technique.

METHODS

The performance of the proposed method was first investigated at different SNR levels using simulation. Random frequency and phase offsets were added to a previously acquired STEAM human data at 7 T, simulating two different noise levels with and without baseline artifacts. Alongside the proposed spectral cross-correlation (SC) method, three other simultaneous alignment approaches were evaluated. Validation was performed on human brain data at 3 T and mouse brain data at 16.4 T.

RESULTS

The results showed that the SC technique effectively corrects for both small and large frequency and phase drifts, even at low SNR levels. Furthermore, the mean square measurement error of the SC algorithm was comparable to the other three methods used, with much faster processing time. The efficacy of the proposed technique was successfully demonstrated in both human brain MRS data and in a noisy MRS dataset acquired from a small volume-of-interest in the mouse brain.

CONCLUSION

The study demonstrated the availability of a fast and robust technique that accurately corrects for both small and large frequency and phase shifts in MRS.

Optimization of 1H-MRS methods for large-volume acquisition of low-concentration downfield resonances at 3 T and 7 T

PURPOSE

This goal of this study was to optimize spectrally selective 1H-MRS methods for large-volume acquisition of low-concentration metabolites with downfield resonances at 7 T and 3 T, with particular attention paid to detection of nicotinamide adenine dinucleotide (NAD+) and tryptophan.

METHODS

Spectrally selective excitation was used to avoid magnetization-transfer effects with water, and various sinc pulses were compared with a band-selective, uniform response, pure-phase (E-BURP) pulse. Localization using a single-slice selective pulse was compared with voxel-based localization that used three orthogonal refocusing pulses, and low bandwidth refocusing pulses were used to take advantage of the chemical shift displacement of water. A technique for water sideband removal was added, and a method of coil channel combination for large volumes was introduced.

RESULTS

Proposed methods were compared qualitatively with previously reported techniques at 7 T. Sinc pulses resulted in reduced water signal excitation and improved spectral quality, with a symmetric, low bandwidth-time product pulse performing best. Single-slice localization allowed shorter TEs with large volumes, enhancing signal, whereas low-bandwidth slice-selective localization greatly reduced the observed water signal. Gradient cycling helped remove water sidebands, and frequency aligning and pruning individual channels narrowed spectral linewidths. High-quality brain spectra of NAD+ and tryptophan are shown in 4 subjects at 3 T.

CONCLUSION

Improved spectral quality with higher downfield signal, shorter TE, lower nuisance signal, reduced artifacts, and narrower peaks was realized at 7 T. These methodological improvements allowed for previously unachievable detection of NAD+ and tryptophan in human brain at 3 T in under 5 min.

Macromolecule Modelling for Improved Metabolite Quantification Using Short Echo Time Brain 1H-MRS at 3 T and 7 T: The PRaMM Model

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single-component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared with those other methods was investigated. The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. Although the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p ≤ 0.0001). Minimally detectable changes are in the range 0.5-1.9 mM, and the percentage coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Here, the PRaMM model, a method for an improved quantification of metabolites, was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

Assessment of cardiac and skeletal muscle metabolites using 1H-MRS and chemical-shift encoded magnetic resonance imaging: Impact of diabetes, RAGE, and DIAPH1

Diabetes affects metabolism and metabolite concentrations in multiple organs. Previous preclinical studies have shown that receptor for advanced glycation end products (RAGE, gene symbol Ager) and its cytoplasmic domain binding partner, Diaphanous-1 (DIAPH1), are key mediators of diabetic micro- and macro-vascular complications. In this study, we used 1H-Magnetic Resonance Spectroscopy (MRS) and chemical shift encoded (CSE) Magnetic Resonance Imaging (MRI) to investigate the metabolite and water-fat fraction in the heart and hind limb muscle in a murine model of type 1 diabetes (T1D) and to determine if the metabolite changes in the heart and hind limb are influenced by (a) deletion of Ager or Diaph1 and (b) pharmacological blockade of RAGE-DIAPH1 interaction in mice. Nine cohorts of male mice, with six mice per cohort, were used: wild type non-diabetic control mice (WT-NDM), WT-diabetic (WT-DM) mice, Ager knockout non-diabetic (RKO-NDM) and diabetic mice (RKO-DM), Diaph1 knockout non-diabetic (DKO-NDM), and diabetic mice (DKO-DM), WT-NDM mice treated with vehicle, WT-DM mice treated with vehicle, and WT-DM mice treated with RAGE229 (antagonist of RAGE-DIAPH1 interaction). A Point Resolved Spectroscopy (PRESS) sequence for 1H-MRS, and multi-echo gradient recalled echo (GRE) for CSE were employed. Triglycerides, and free fatty acids in the heart and hind limb obtained from MRS and MRI were compared to those measured using biochemical assays. Two-sided t-test, non-parametric Kruskal-Wallis Test, and one-way ANOVA were employed for statistical analysis. We report that the results were well-correlated with significant differences using MRI and biochemical assays between WT-NDM and WT-DM, as well as within the non-diabetic groups, and within the diabetic groups. Deletion of Ager or Diaph1, or treatment with RAGE229 attenuated diabetes-associated increases in triglycerides in the heart and hind limb in mice. These results suggest that the employment of 1H-MRS/MRI is a feasible non-invasive modality to monitor metabolic dysfunction in T1D and the metabolic consequences of interventions that block RAGE and DIAPH1.

Sub-Microliter 1H Magnetic Resonance Spectroscopy for In Vivo High-Spatial Resolution Metabolite Quantification in the Mouse Brain

Proton magnetic resonance spectroscopy (MRS) offers a non-invasive, repeatable, and reproducible method for in vivo metabolite profiling of the brain and other tissues. However, metabolite fingerprinting by MRS requires high signal-to-noise ratios for accurate metabolite quantification, which has traditionally been limited to large volumes of interest, compromising spatial fidelity. In this study, we introduce a new optimized pipeline that combines LASER MRS acquisition at 11.7 T with a cryogenic coil and advanced offline pre- and post-processing. This approach achieves a signal-to-noise ratio sufficient to reliably quantify 19 distinct metabolites in a volume as small as 0.7 μL within the mouse brain. The resulting high spatial resolution and spectral quality enable the identification of distinct metabolite fingerprints in small, specific regions, as demonstrated by characteristic differences in N-acetylaspartate, glutamate, taurine, and myo-inositol between the motor and somatosensory cortices. We demonstrated a decline in taurine and glutamate in the primary motor cortex between 5 and 11 months of age, against the stability of other metabolites. Further exploitation to cortical layer-specific metabolite fingerprinting of layer I-III to layer VI-V in the primary motor cortex, with the latter showing reduced taurine and phosphoethanolamine levels, demonstrates the potential of this pipeline for detailed in vivo metabolite fingerprinting of cortical areas and subareas.

Exploring the Effects of Prefrontal Transcranial Direct Current Stimulation on Brain Metabolites: A Concurrent tDCS-MRS Study

Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique used to modulates cortical brain activity. However, its effects on brain metabolites within the dorsolateral prefrontal cortex (DLPFC), a crucial area targeted for brain stimulation in mental disorders, remain unclear. This study aimed to investigate whether prefrontal tDCS over the left and right DLPFC modulates levels of key metabolites, including gamma-aminobutyric acid (GABA), glutamate (Glu), glutamine/glutamate (Glx), N-acetylaspartate (NAA), near to the target region and to explore potential sex-specific effects on these metabolite concentrations. A total of 41 healthy individuals (19 female, M_age = 25 years, SD = 3.15) underwent either bifrontal active (2 mA for 20 min) or sham tDCS targeting the left (anode: F3) and right (cathode: F4) DLPFC within a 3 Tesla MRI scanner. Magnetic resonance spectroscopy (MRS) was used to monitor neurometabolic changes before, during, and after 40 min of tDCS, with measurements of two 10-min intervals during stimulation. A single voxel beneath F3 was used for metabolic quantification. Results showed a statistically significant increase in Glx levels under active tDCS compared to the sham condition, particularly during the second 10-min window and persisting into the post-stimulation phase. No significant changes were observed in other metabolites, but consistent sex differences were detected. Specifically, females showed lower levels of NAA and GABA under active tDCS compared to the sham condition, while no significant changes were observed in males. E-field modeling showed no significant differences in field magnitudes between sexes, and the magnitude of the e-fields did not correlate with changes in Glx levels between active and sham stimulation during the second interval or post-stimulation. This study demonstrates that a single session of prefrontal tDCS significantly elevates Glx levels in the left DLPFC, with effects persisting post-stimulation. However, the observed sex differences in the neurochemical response to tDCS were not linked to specific stimulation intervals or variations in e-field magnitudes, highlighting the complexity of tDCS effects and the need for personalized neuromodulation strategies.

XGBoost algorithm assisted multi-component quantitative analysis with Raman spectroscopy

To improve prediction performance and reduce artifacts in Raman spectra, we developed an eXtreme Gradient Boosting (XGBoost) preprocessing method to preprocess the Raman spectra of glucose, glycerol and ethanol mixtures. To ensure the robustness and reliability of the XGBoost preprocessing method, an X-LR model (which combined XGBoost preprocessing and a linear regression (LR) model) and a X-MLP model (which combined XGBoost preprocessing and a multilayer perceptron (MLP) model) were developed. These two models were used to quantitatively analyze the concentrations of glucose, glycerol and ethanol in the Raman spectra of mixed solutions. The proportion map of hyperparameters was firstly used to narrow down the search range of hyperparameters in the X-LR and the X-MLP models. Then the correlation coefficients (R2), root mean square of calibration (RMSEC), and root mean square error of prediction (RMSEP) were used to evaluate the models' performance. Experimental results indicated that the XGBoost preprocessing method achieved higher accuracy and generalization capability, with better performance than those of other preprocessing methods for both LR and MLP models.

Neurochemical dynamics during two hypnotic states evidenced by magnetic resonance spectroscopy

This study explores neurochemical changes in the brain during hypnosis, targeting the parieto-occipital (PO) and posterior superior temporal gyrus (pSTG) regions using proton magnetic resonance spectroscopy (MRS). We examined 52 healthy, hypnosis experienced participants to investigate how two different hypnotic states of varying depth impacted brain neurochemistry in comparison to each other and to their respective non-hypnagogic control conditions. Alongside neurochemical assessments, we recorded respiration and heart rate variability (HRV) to further explore possible associations between physiological correlates of hypnotic depth. Significant changes in myo-Inositol concentration relative to total creatine were observed in the PO region during the deeper hypnosis state, possibly indicating reduced neuronal activity. No significant neurochemical shifts were detected in the pSTG region. Additionally, our findings revealed notable physiological changes during hypnosis. Respiratory rates were significantly slowed in both hypnotic states compared to the respective controls, with more pronounced slowing in the deeper hypnotic state. This study contributes a first-time insight into neurochemical responses during hypnotic states. We hope offering a foundation for further research in understanding the neurobiological correlates of hypnosis in both, basic science and-down the line-clinical applications.

Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

2-Hydroxyglutarate as an MR spectroscopic predictor of an IDH mutation in gliomas

The mutated enzyme isocitrate dehydrogenase (IDH) 1 and 2 has been detected in various tumor entities such as gliomas and can convert α-ketoglutarate into the oncometabolite 2-hydroxyglutarate (2-HG). This neuro-oncologically significant metabolic product can be detected by MR spectroscopy and is therefore suitable for noninvasive glioma classification and therapy monitoring.This paper provides an up-to-date overview of the methodology and relevance of 1H-MR spectroscopy (MRS) in the oncological primary and follow-up diagnosis of gliomas. The possibilities and limitations of this MR spectroscopic examination are evaluated on the basis of the available literature.By detecting 2-HG, MRS can in principle offer a noninvasive alternative to immunohistological analysis thus avoiding surgical intervention in some cases. However, in addition to an adapted and optimized examination protocol, the individual measurement conditions in the examination region are of decisive importance. Due to the inherently small signal of 2-HG, unfavorable measurement conditions can influence the reliability of detection. · MR spectroscopy enables the non-invasive detection of 2-hydroxyglutarate.. · The measurement of this metabolite allows the detection of an IDH mutation in gliomas.. · The choice of MR examination method is particularly important.. · Detection reliability is influenced by glioma size, necrotic tissue and the existing measurement conditions.. · Bauer J, Raum HN, Kugel H et al. 2-Hydroxyglutarate as an MR spectroscopic predictor of an IDH mutation in gliomas. Fortschr Röntgenstr 2024; DOI 10.1055/a-2285-4923.

Improvements in precision and accuracy of complex- relative to real-domain linear combination model spectral fitting not necessarily recovered by zero filling

Although the information obtained from in vivo proton magnetic resonance spectroscopy (1H MRS) presents a complex-valued spectrum, spectral quantification generally employs linear combination model (LCM) fitting using the real spectrum alone. There is currently no known investigation comparing fit results obtained from LCM fitting over the full complex data versus the real data and how these results might be affected by common spectral preprocessing procedure zero filling. Here, we employ linear combination modeling of simulated and measured spectral data to examine two major ideas: first, whether use of the full complex rather than real-only data can provide improvements in quantification by linear combination modeling and, second, to what extent zero filling might influence these improvements. We examine these questions by evaluating the errors of linear combination model fits in the complex versus real domains against three classes of synthetic data: simulated Lorentzian singlets, simulated metabolite spectra excluding the baseline, and simulated metabolite spectra including measured in vivo baselines. We observed that complex fitting provides consistent improvements in fit accuracy and precision across all three data types. While zero filling obviates the accuracy and precision benefit of complex fitting for Lorentzian singlets and metabolite spectra lacking baselines, it does not necessarily do so for complex spectra including measured in vivo baselines. Overall, performing linear combination modeling in the complex domain can improve metabolite quantification accuracy relative to real fits alone. While this benefit can be similarly achieved via zero filling for some spectra with flat baselines, this is not invariably the case for all baseline types exhibited by measured in vivo data.

Plasma p-tau181 and GFAP reflect 7T MR-derived changes in Alzheimer's disease: A longitudinal study of structural and functional MRI and MRS

BACKGROUND

Associations between longitudinal changes of plasma biomarkers and cerebral magnetic resonance (MR)-derived measurements in Alzheimer's disease (AD) remain unclear.

METHODS

In a study population (n = 127) of healthy older adults and patients within the AD continuum, we examined associations between longitudinal plasma amyloid beta 42/40 ratio, tau phosphorylated at threonine 181 (p-tau181), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and 7T structural and functional MR imaging and spectroscopy using linear mixed models.

RESULTS

Increases in both p-tau181 and GFAP showed the strongest associations to 7T MR-derived measurements, particularly with decreasing parietal cortical thickness, decreasing connectivity of the salience network, and increasing neuroinflammation as determined by MR spectroscopy (MRS) myo-inositol.

DISCUSSION

Both plasma p-tau181 and GFAP appear to reflect disease progression, as indicated by 7T MR-derived brain changes which are not limited to areas known to be affected by tau pathology and neuroinflammation measured by MRS myo-inositol, respectively.

HIGHLIGHTS

This study leverages high-resolution 7T magnetic resonance (MR) imaging and MR spectroscopy (MRS) for Alzheimer's disease (AD) plasma biomarker insights. Tau phosphorylated at threonine 181 (p-tau181) and glial fibrillary acidic protein (GFAP) showed the largest changes over time, particularly in the AD group. p-tau181 and GFAP are robust in reflecting 7T MR-based changes in AD. The strongest associations were for frontal/parietal MR changes and MRS neuroinflammation.

PyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data

Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications.

Altered markers of brain metabolism and excitability are associated with executive functioning in young children exposed to alcohol in utero

Prenatal alcohol exposure (PAE) is the leading known cause of birth defects and cognitive disabilities, with impacts on brain development and executive functioning. Abnormalities in structural and functional brain features are well-documented in children with PAE, but the effects of PAE on brain metabolism in children have received less attention. Levels of brain metabolites can be measured non-invasively using magnetic resonance spectroscopy (MRS). Here, we present the first study of PAE-related brain metabolite differences in early childhood (ages 3-8 years) and their associations with cognitive performance, including executive functioning (EF) and pre-reading skills. We measured metabolites in two cohorts of children with PAE and unexposed children using MRS in the anterior cingulate cortex (ACC; cohort 1) and left temporo-parietal cortex (LTP; cohort 2). Total choline (tCho), a marker of membrane/myelin metabolism, was elevated in both regions in children with PAE compared to unexposed children, and glutamate + glutamine (Glx), a marker of excitability, was elevated in the ACC. The PAE group exhibited more difficulties with EF, and higher tCho was associated with better EF in both PAE and unexposed groups. In addition, elevated Glx in the ACC was associated with poorer inhibitory control within the PAE group only. LTP metabolites were not significantly associated with pre-reading skills in PAE or unexposed groups. Together, these findings point to altered membrane metabolism and excitability in young children with PAE. These findings provide new insight to potential mechanisms by which PAE disrupts brain development and cognitive functioning in early childhood.

Neurometabolic Profile in Obese Patients: A Cerebral Multi-Voxel Magnetic Resonance Spectroscopy Study

Background and Objectives: Obesity-related chronic inflammation may lead to neuroinflammation and neurodegeneration. This study aimed to evaluate the neurometabolic profile of obese patients using cerebral multivoxel magnetic resonance spectroscopy (mvMRS) and assess correlations between brain metabolites and obesity markers, including body mass index (BMI), waist circumference, waist-hip ratio, body fat percentage, and indicators of metabolic syndrome (e.g., triglycerides, HDL cholesterol, fasting blood glucose, insulin, and insulin resistance index (HOMA-IR)). Materials and Methods: This prospective study involved 100 participants, stratified into two groups: 50 obese individuals (BMI ≥ 30 kg/m2) and 50 controls (18.5 ≤ BMI < 25 kg/m2). Anthropometric measurements, body fat percentage, and biochemical markers were evaluated. All subjects underwent long- and short-echo mvMRS analysis of the frontal and parietal supracallosal subcortical and deep white matter, as well as the cingulate gyrus, analyzing NAA/Cr, Cho/Cr, and mI/Cr ratios, along with absolute concentrations of NAA and Cho. Results: Obese participants exhibited significantly decreased NAA/Cr and Cho/Cr ratios in the deep white matter of the right cerebral hemisphere (p < 0.001), while absolute concentrations of NAA and Cho did not differ significantly between groups (p > 0.05). NAA levels showed negative correlations with more reliable obesity parameters (waist circumference and waist-to-hip ratio) but not with BMI, particularly in the deep frontal white matter and dorsal anterior cingulate gyrus of the left cerebral hemisphere. Notably, insulin demonstrated a significant negative impact on NAA (ρ = -0.409 and ρ = -0.410; p < 0.01) and Cho levels (ρ = -0.403 and ρ = -0.392; p < 0.01) at these locations in obese individuals. Conclusions: Central obesity and hyperinsulinemia negatively affect specific brain regions associated with cognitive and emotional processing, while BMI is not a reliable parameter for assessing brain metabolism.

Transient brain structure changes after high phenylalanine exposure in adults with phenylketonuria

Phenylketonuria is a rare metabolic disease resulting from a deficiency of the enzyme phenylalanine hydroxylase. Recent cross-sectional evidence suggests that early-treated adults with phenylketonuria exhibit alterations in cortical grey matter compared to healthy peers. However, the effects of high phenylalanine exposure on brain structure in adulthood need to be further elucidated. In this double-blind, randomized, placebo-controlled crossover trial, we investigated the impact of a 4-week high phenylalanine exposure on the brain structure and its relationship to cognitive performance and metabolic parameters in early-treated adults with phenylketonuria. Twenty-eight adult patients with early-treated classical phenylketonuria (19-48 years) underwent magnetic resonance imaging before and after the 4-week phenylalanine and placebo interventions (four time points). Structural T1-weighted images were preprocessed and evaluated using Direct Cortical Thickness Estimation using Deep Learning-based Anatomy Segmentation and Cortex Parcellation (DL+DiReCT), a deep-learning-based tool for brain morphometric analysis. Cortical thickness, white matter volume and ventricular volume were compared between the phenylalanine and placebo periods. Brain phenylalanine levels were measured using 1H spectroscopy. Blood levels of phenylalanine, tyrosine, and tryptophan were assessed at each of the four time points, along with performance in executive functions and attention. Blood phenylalanine levels were significantly higher after the phenylalanine period (1441 µmol/l) than after the placebo period (873 µmol/l, P < 0.001). Morphometric analyses revealed a statistically significant decrease in cortical thickness in 17 of 60 brain regions after the phenylalanine period compared to placebo. The largest decreases were observed in the right pars orbitalis (point estimate = -0.095 mm, P < 0.001) and the left lingual gyrus (point estimate = -0.070 mm, P < 0.001). Bilateral white matter and ventricular volumes were significantly increased after the phenylalanine period. However, the structural alterations in the phenylalanine-placebo group returned to baseline measures following the washout and placebo period. Additionally, elevated blood and brain phenylalanine levels were related to increased bilateral white matter volume (rs = 0.43 to 0.51, P ≤ 0.036) and decreased cortical thickness [rs = -0.62 to -0.39, not surviving false discovery rate (FDR) correction] after the phenylalanine and placebo periods. Moreover, decreased cortical thickness was correlated with worse cognitive performance after both periods (rs = -0.54 to -0.40, not surviving FDR correction). These findings provide evidence that a 4-week high phenylalanine exposure in adults with phenylketonuria results in transient reductions of the cortical grey matter and increases in white matter volume. Further research is needed to determine the potential long-term impact of high phenylalanine levels on brain structure and function in adults with phenylketonuria.

Glutamate levels and symptom burden in high-risk and first-episode schizophrenia: a dual-voxel study of the anterior cingulate cortex

BACKGROUND

Reduced glutamatergic excitability of the anterior cingulate cortex (ACC) has been long suspected in schizophrenia; recent observations support low glutamatergic tone as the primary pathophysiology contributing to subtle early features of this illness, with a secondary disinhibition (higher glutamate tone) resulting in more prominent clinical symptoms later in its course. We sought to investigate whether people with genetic high risk (GHR) for schizophrenia have lower glutamate levels in the ACC than those at later stages of clinical high risk (CHR) and those with first-episode schizophrenia (FES), among whom symptoms are already prominent.

METHODS

We recruited people with CHR, GHR, or FES, as well as healthy controls. Using proton magnetic resonance spectroscopy, we determined glutamate levels in the perigenual ACC (pACC) and dorsal ACC (dACC) using a 3 T scanner.

RESULTS

We recruited 302 people across multiple stages of psychosis, including 63 with CHR, 76 with GHR, and 96 with FES, as well as 67 healthy controls. Those with GHR had lower glutamate levels in the dACC than those with CHR, while those with CHR had higher glutamate levels in the pACC than those with FES. Higher disorganization, but not any other symptom domain, was associated with lower levels of glutamate in the GHR group (dACC and pACC) and in the CHR group (pACC).

LIMITATIONS

The cross-sectional design precluded inferences regarding individual clinical trajectory and resolution at 3 T was insufficient to separate spectra of glutamine from glutamate.

CONCLUSION

Reduced glutamatergic tone among people genetically predisposed to schizophrenia supports diminished excitability as an early feature of schizophrenia, contributing to the subtle symptom of disorganization across high-risk states. Higher glutamate levels become apparent when psychotic symptoms become prominent, possibly as a disinhibitory effect and, at the full-blown stage of psychosis, the relationship between glutamate concentrations and symptoms ceases to be simply linear.

Spectro-ViT: A vision transformer model for GABA-edited MEGA-PRESS reconstruction using spectrograms

This study investigated the use of a Vision Transformer (ViT) for reconstructing GABA-edited Magnetic Resonance Spectroscopy (MRS) data from a reduced number of transients. Transients refer to the samples collected during an MRS acquisition by repeating the experiment to generate a signal of sufficient quality. Specifically, 80 transients were used instead of the typical 320 transients, aiming to reduce scan time. The 80 transients were pre-processed and converted into a spectrogram image representation using the Short-Time Fourier Transform (STFT). A pre-trained ViT, named Spectro-ViT, was fine-tuned and then tested using in-vivo GABA-edited MEGA-PRESS data. Its performance was compared against other pipelines in the literature using quantitative quality metrics and estimated metabolite concentration values, with the typical 320-transient scans serving as the reference for comparison. The Spectro-ViT model exhibited the best overall quality metrics among all other pipelines against which it was compared. The metabolite concentrations from Spectro-ViT's reconstructions for GABA+ achieved the best average R2 value of 0.67 and the best average Mean Absolute Percentage Error (MAPE) value of 9.68%, with no significant statistical differences found compared to the 320-transient reference. The code to reproduce this research is available at https://github.com/MICLab-Unicamp/Spectro-ViT.

Comparison of baseline correction algorithms for in vivo 1H-MRS

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data. In short-echo-time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long-echo-time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi-LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm-QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm-QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting.

Probing diffusion of water and metabolites to assess white matter microstructure in Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a progressive X-linked neuromuscular disorder caused by the absence of functional dystrophin protein. In addition to muscle, dystrophin is expressed in the brain in both neurons and glial cells. Previous studies have shown altered white matter microstructure in patients with DMD using diffusion tensor imaging (DTI). However, DTI measures the diffusion properties of water, a ubiquitous molecule, making it difficult to unravel the underlying pathology. Diffusion-weighted spectroscopy (DWS) is a complementary technique which measures diffusion properties of cell-specific intracellular metabolites. Here we performed both DWS and DTI measurements to disentangle intra- and extracellular contributions to white matter changes in patients with DMD. Scans were conducted in patients with DMD (15.5 ± 4.6 y/o) and age- and sex-matched healthy controls (16.3 ± 3.3 y/o). DWS measurements were obtained in a volume of interest (VOI) positioned in the left parietal white matter. Apparent diffusion coefficients (ADCs) were calculated for total N-acetylaspartate (tNAA), choline compounds (tCho), and total creatine (tCr). The tNAA/tCr and tCho/tCr ratios were calculated from the non-diffusion-weighted spectrum. Mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and fractional anisotropy of water within the VOI were extracted from DTI measurements. DWS and DTI data from patients with DMD (respectively n = 20 and n = 18) and n = 10 healthy controls were included. No differences in metabolite ADC or in concentration ratios were found between patients with DMD and controls. In contrast, water diffusion (MD, t = -2.727, p = 0.011; RD, t = -2.720, p = 0.011; AD, t = -2.715, p = 0.012) within the VOI was significantly higher in patients compared with healthy controls. Taken together, our study illustrates the potential of combining DTI and DWS to gain a better understanding of microstructural changes and their association with disease mechanisms in a clinical setting.

Regional variations in cingulate cortex glutamate levels: a magnetic resonance spectroscopy study at 3 T

Regional variations in glutamate levels across the cingulate cortex, decreasing rostral to caudal, have been observed previously in healthy volunteers with proton magnetic resonance spectroscopy (1H-MRS) at 7 T. This study sought to explore cingulate cortex glutamate trends further by investigating whether a similar gradient could be detected at 3 T, the effect of sex, as well as whether individual variations gave rise to more than one regional glutamate pattern. 1H-MRS at 3 T [Phillips Elition; semi-localization by adiabatic selective refocusing, echo time (TE)/repetition time (TR) = 32/5,000] was acquired in four cingulate regions: the anterior, midanterior, midposterior, and posterior cortices, in 50 healthy participants (26 F) scanned at a fixed time of day and with controlled food intake. K-means clustering was used to characterize the presence of distinct regional patterns, which were then compared between sex and clusters. In addition, cortical thickness was compared between clusters and in relation to glutamate. Aligned with 7 T findings, we demonstrated that average glutamate levels decreased rostral to caudal in the healthy cingulate cortex. No effect of sex was found, suggesting similar resting glutamate levels in both sexes. Interestingly, the majority of participants were characterized by glutamate levels that did not significantly change across the cingulate (65%). Different regional patterns in cortical thickness between clusters offer further evidence into these distinct glutamate variations and suggest that both a neuroanatomical and a functional role may lead to these findings. This study provides a much-needed foundation for further research to determine the implications of neurotransmission patterns in health and disease.NEW & NOTEWORTHY In a large, sex-balanced sample of healthy individuals, we demonstrate that average regional differences (rostral to caudal) in cingulate cortex glutamate exist, using optimized experimental conditions and 3 T magnetic resonance spectroscopy techniques. Results align with observations from 7 T. A novel clustering approach was introduced to determine the number of patterns for glutamate in the healthy adult brain for the first time. These findings demonstrate that regional differences are detectable at 3 T when present and suggest the occurrence of multiple glutamate metabolism patterns in the cingulate.

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling

PURPOSE

Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear-combination model (2D-LCM) of individual transients ("model-based FPC"). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates.

METHODS

We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground-truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data.

RESULTS

2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of FPC and amplitudes performed substantially better at low-to-very-low SNR.

CONCLUSION

Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, for example, long TEs or strong diffusion weighting.

GABA-edited MEGA-PRESS at 3 T: Does a measured macromolecule background improve linear combination modeling?

PURPOSE

The J-difference edited γ-aminobutyric acid (GABA) signal is contaminated by other co-edited signals-the largest of which originates from co-edited macromolecules (MMs)-and is consequently often reported as "GABA+." MM signals are broader and less well-characterized than the metabolites, and are commonly approximated using a Gaussian model parameterization. Experimentally measured MM signals are a consensus-recommended alternative to parameterized modeling; however, they are relatively under-studied in the context of edited MRS.

METHODS

To address this limitation in the literature, we have acquired GABA-edited MEGA-PRESS data with pre-inversion to null metabolite signals in 13 healthy controls. An experimental MM basis function was derived from the mean across subjects. We further derived a new parameterization of the MM signals from the experimental data, using multiple Gaussians to accurately represent their observed asymmetry. The previous single-Gaussian parameterization, mean experimental MM spectrum and new multi-Gaussian parameterization were compared in a three-way analysis of a public MEGA-PRESS dataset of 61 healthy participants.

RESULTS

Both the experimental MMs and the multi-Gaussian parameterization exhibited reduced fit residuals compared to the single-Gaussian approach (p = 0.034 and p = 0.031, respectively), suggesting they better represent the underlying data than the single-Gaussian parameterization. Furthermore, both experimentally derived models estimated larger MM fractional contribution to the GABA+ signal for the experimental MMs (58%) and multi-Gaussian parameterization (58%), compared to the single-Gaussian approach (50%).

CONCLUSIONS

Our results indicate that single-Gaussian parameterization of edited MM signals is insufficient and that both experimentally derived GABA+ spectra and their parameterized replicas improve the modeling of GABA+ spectra.

Altered brain metabolites in male nonhuman primate offspring exposed to maternal immune activation

Converging data show that exposure to maternal immune activation (MIA) in utero alters brain development in animals and increases the risk of neurodevelopmental disorders in humans. A recently developed non-human primate MIA model affords opportunities for studies with uniquely strong translational relevance to human neurodevelopment. The current longitudinal study used 1H-MRS to investigate the developmental trajectory of prefrontal cortex metabolites in male rhesus monkey offspring of dams (n = 14) exposed to a modified form of the inflammatory viral mimic, polyinosinic:polycytidylic acid (Poly IC), in the late first trimester. Brain metabolites in these animals were compared to offspring of dams that received saline (n = 10) or no injection (n = 4). N-acetylaspartate (NAA), glutamate, creatine, choline, myo-inositol, taurine, and glutathione were estimated from PRESS and MEGA-PRESS acquisitions obtained at 6, 12, 24, 36, and 45 months of age. Prior investigations of this cohort reported reduced frontal cortical gray and white matter and subtle cognitive impairments in MIA offspring. We hypothesized that the MIA-induced neurodevelopmental changes would extend to abnormal brain metabolite levels, which would be associated with the observed cognitive impairments. Prefrontal NAA was significantly higher in the MIA offspring across all ages (p < 0.001) and was associated with better performance on the two cognitive measures most sensitive to impairment in the MIA animals (both p < 0.05). Myo-inositol was significantly lower across all ages in MIA offspring but was not associated with cognitive performance. Taurine was elevated in MIA offspring at 36 and 45 months. Glutathione did not differ between groups. MIA exposure in male non-human primates is associated with altered prefrontal cortex metabolites during childhood and adolescence. A positive association between elevated NAA and cognitive performance suggests the hypothesis that elevated NAA throughout these developmental stages reflects a protective or resilience-related process in MIA-exposed offspring. The potential relevance of these findings to human neurodevelopmental disorders is discussed.

Inhibitory mechanisms in the prefrontal-cortex differentially mediate Putamen activity during valence-based learning

Learning from appetitive and aversive stimuli involves interactions between the prefrontal cortex and subcortical structures. Preclinical and theoretical studies indicate that inhibition is essential in regulating the relevant neural circuitry. Here, we demonstrate that GABA, the main inhibitory neurotransmitter in the central nervous system, differentially affects how the dACC interacts with subcortical structures during appetitive and aversive learning in humans. Participants engaged in tasks involving appetitive and aversive learning, while using functional magnetic resonance spectroscopy (MRS) at 7T to track GABA concentrations in the dACC, alongside whole-brain fMRI scans to assess BOLD activation. During appetitive learning, dACC GABA concentrations were negatively correlated with learning performance and BOLD activity measured from the dACC and the Putamen. These correlations were absent during aversive learning, where dACC GABA concentrations negatively correlated with the connectivity between the dACC and the Putamen. Our results show that inhibition in the dACC mediates appetitive and aversive learning in humans through distinct mechanisms.

Repeatability of Lac+ measurements in healthy human brain at 3 T

PURPOSE

In vivo quantification of lactate has numerous applications in studying the pathology of both cerebral and musculoskeletal systems. Due to its low concentration (~0.5-1 mM), and overlap with lipid signals, traditional 1H MR spectra acquired in vivo using a small voxel and short echo time often result in an inadequate signal to detect and resolve the lactate peak, especially in healthy human volunteers.

METHODS

In this study, using a semi-LASER acquisition with long echo time (TE = 288 ms) and large voxel size (80 × 70 × 20 mm3), we clearly visualize the combined signal of lactate and threonine. Therefore, we call the signal at 1.33 ppm Lac+ and quantify Lac+ concentration from water suppressed spectra in healthy human brains in vivo. Four participants (22-37 years old; mean age = 28 ± 5.4; three male, one female) were scanned on four separate days, and on each day four measurements were taken. Intra-day values are calculated for each participant by comparing the four measurements on a single day. Inter-day values were calculated using the mean intra-day measurements.

RESULTS

The mean intra-participant Lac+ concentration, standard deviation (SD), and coefficient of variation (CV) ranged from 0.49 to 0.61 mM, 0.02 to 0.07 mM, and 4% to 13%, respectively, across four volunteers. The inter-participant Lac+ concentration, SD, and CV was 0.53 mM, ±0.06 mM, and 11%.

CONCLUSION

Repeatability is shown in Lac+ measurement in healthy human brain using a long echo time semi-LASER sequence with a large voxel in about 3.5 min at 3 T.

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation

PURPOSE

The interest in applying and modeling dynamic MRS has recently grown. Two-dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one-dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches.

METHODS

Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear-combination modeling (LCM) with 1D-LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64-transient MRS experiment at six signal-to-noise levels for two separate spin systems (scyllo-inositol and gamma-aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér-Rao lower bounds (CRLBs).

RESULTS

Amplitude estimates for 1D- and 2D-LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground-truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM.

CONCLUSION

Our results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling.

Absolute Metabolite Quantification in Individuals with Glioma and Healthy Individuals Using Whole-Brain Three-dimensional MR Spectroscopic and Echo-planar Time-resolved Imaging

BACKGROUND

MR spectroscopic imaging (MRSI) can be used to quantify an extended brain metabolic profile but is confounded by changes in tissue water levels due to disease.

PURPOSE

To develop a fast absolute quantification method for metabolite concentrations combining whole-brain MRSI with echo-planar time-resolved imaging (EPTI) relaxometry in individuals with glioma and healthy individuals.

MATERIALS AND METHODS

In this prospective study performed from August 2022 to August 2023, using internal water as concentration reference, the MRSI-EPTI quantification method was compared with the conventional method using population-average literature relaxation values. Healthy participants and participants with mutant IDH1 gliomas underwent imaging at 3 T with a 32-channel coil. Real-time navigated adiabatic spiral three-dimensional MRSI scans were acquired in approximately 8 minutes and reconstructed with a super-resolution pipeline to obtain brain metabolic images at 2.4-mm isotropic resolution. High-spatial-resolution multiparametric EPTI was performed in 3 minutes, with 1-mm isotropic resolution, to correct the relaxation and proton density of the water reference signal. Bland-Altman analysis and the Wilcoxon signed rank test were used to compare absolute quantifications from the proposed and conventional methods.

RESULTS

Six healthy participants (four male; mean age, 37 years ± 11 [SD]) and nine participants with glioma (six male; mean age, 41 years ± 15; one with wild-type IDH1 and eight with mutant IDH1) were included. In healthy participants, there was good agreement (+4% bias) between metabolic concentrations derived using the two methods, with a CI of plus or minus 26%. In participants with glioma, there was large disagreement between the two methods (+39% bias) and a CI of plus or minus 55%. The proposed quantification method improved tumor contrast-to-noise ratio (median values) for total N-acetyl-aspartate (EPTI: 0.541 [95% CI: 0.217, 0.910]; conventional: 0.484 [95% CI: 0.199, 0.823]), total choline (EPTI: 1.053 [95% CI: 0.681, 1.713]; conventional: 0.940 [95% CI: 0.617, 1.295]), and total creatine (EPTI: 0.745 [95% CI: 0.628, 0.909]; conventional: 0.553 [95% CI: 0.444, 0.828]) (P = .03 for all).

CONCLUSION

The whole-brain MRSI-EPTI method provided fast absolute quantification of metabolic concentrations with individual-specific corrections at 2.4-mm isotropic resolution, yielding concentrations closer to the true value in disease than the conventional literature-based corrections. © RSNA, 2024 Supplemental material is available for this article.

Metabolite T1 relaxation times decrease across the adult lifespan

Relaxation correction is an integral step in quantifying brain metabolite concentrations measured by in vivo magnetic resonance spectroscopy (MRS). While most quantification routines assume constant T1 relaxation across age, it is possible that aging alters T1 relaxation rates, as is seen for T2 relaxation. Here, we investigate the age dependence of metabolite T1 relaxation times at 3 T in both gray- and white-matter-rich voxels using publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3 T using Point RESolved Spectroscopy (PRESS) localization. Data were acquired from voxels in the posterior cingulate cortex (PCC) and centrum semiovale (CSO) in 102 healthy volunteers across 5 decades of life (aged 20-69 years). All spectra were analyzed in Osprey v.2.4.0. To estimate T1 relaxation times for total N-acetyl aspartate at 2.0 ppm (tNAA2.0) and total creatine at 3.0 ppm (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. Spearman correlations revealed that estimated T1 relaxation times of tNAA2.0 (rs = -0.27; p < 0.006) and tCr3.0 (rs = -0.40; p < 0.001) decreased significantly with age in white-matter-rich CSO, and less steeply for tNAA2.0 (rs = -0.228; p = 0.005) and (not significantly for) tCr3.0 (rs = -0.13; p = 0.196) in graymatter-rich PCC. The 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.

A Multimodal Magnetic Resonance Imaging Study on Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Feasibility and Clinical Correlation

Background/Objectives: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a neurological disorder characterized by post-exertional malaise. Despite its clinical relevance, the disease mechanisms of ME/CFS are not fully understood. The previous studies targeting brain function or metabolites have been inconclusive in understanding ME/CFS complexity. We combined single-voxel magnetic resonance spectroscopy (SV-MRS) and functional magnetic resonance imaging (fMRI). Our objectives were to examine the feasibility of the multimodal MRI protocol, identify possible differences between ME/CFS and healthy controls (HCs), and relate MRI findings with clinical symptoms. Methods: We enrolled 18 female ME/CFS participants (mean age: 39.7 ± 12.0 years) and five HCs (mean age: 45.6 ± 14.5 years). SV-MRS spectra were acquired from three voxels of interest: the anterior cingulate gyrus (ACC), brainstem (BS), and left dorsolateral prefrontal cortex (L-DLPFC). Whole-brain fMRI used n-back task testing working memory and executive function. The feasibility was assessed as protocol completion rate and time. Group differences in brain metabolites and fMRI activation between ME/CFS and HCs were compared and correlated with behavioral and symptom severity measurements. Results: The completion rate was 100% regardless of participant group without causing immediate fatigue. ME/CFS appeared to show a higher N-Acetylaspartate in L-DLPFC compared to HCs (OR = 8.49, p = 0.040), correlating with poorer fatigue, pain, and sleep quality scores (p's = 0.001-0.015). An increase in brain activation involving the frontal lobe and the brainstem was observed in ME/CFS compared to HCs (Z > 3.4, p's < 0.010). Conclusions: The study demonstrates the feasibility of combining MRS and fMRI to capture neurochemical and neurophysiological features of ME/CFS in female participants. Further research with larger cohorts of more representative sampling and follow-ups is needed to validate these apparent differences between ME/CFS and HCs.

Retrospective analysis of Braak stage- and APOE4 allele-dependent associations between MR spectroscopy and markers of tau and neurodegeneration in cognitively unimpaired elderly

PURPOSE

The pathological hallmarks of Alzheimer's disease (AD), amyloid, tau, and associated neurodegeneration, are present in the cortical gray matter (GM) years before symptom onset, and at significantly greater levels in carriers of the apolipoprotein E4 (APOE4) allele. Their respective biomarkers, A/T/N, have been found to correlate with aspects of brain biochemistry, measured with magnetic resonance spectroscopy (MRS), indicating a potential for MRS to augment the A/T/N framework for staging and prediction of AD. Unfortunately, the relationships between MRS and A/T/N biomarkers are unclear, largely due to a lack of studies examining them in the context of the spatial and temporal model of T/N progression. Advanced MRS acquisition and post-processing approaches have enabled us to address this knowledge gap and test the hypotheses, that glutamate-plus-glutamine (Glx) and N-acetyl-aspartate (NAA), metabolites reflecting synaptic and neuronal health, respectively, measured from regions on the Braak stage continuum, correlate with: (i) cerebrospinal fluid (CSF) p-tau181 level (T), and (ii) hippocampal volume or cortical thickness of parietal lobe GM (N). We hypothesized that these correlations will be moderated by Braak stage and APOE4 genotype.

METHODS

We conducted a retrospective imaging study of 34 cognitively unimpaired elderly individuals who received APOE4 genotyping and lumbar puncture from pre-existing prospective studies at the NYU Grossman School of Medicine between October 2014 and January 2019. Subjects returned for their imaging exam between April 2018 and February 2020. Metabolites were measured from the left hippocampus (Braak II) using a single-voxel semi-adiabatic localization by adiabatic selective refocusing sequence; and from the bilateral posterior cingulate cortex (PCC; Braak IV), bilateral precuneus (Braak V), and bilateral precentral gyrus (Braak VI) using a multi-voxel echo-planar spectroscopic imaging sequence. Pearson and Spearman correlations were used to examine the relationships between absolute levels of choline, creatine, myo-inositol, Glx, and NAA and CSF p-tau181, and between these metabolites and hippocampal volume or parietal cortical thicknesses. Covariates included age, sex, years of education, Fazekas score, and months between CSF collection and MRI exam.

RESULTS

There was a direct correlation between hippocampal Glx and CSF p-tau181 in APOE4 carriers (Pearson's r = 0.76, p = 0.02), but not after adjusting for covariates. In the entire cohort, there was a direct correlation between hippocampal NAA and hippocampal volume (Spearman's r = 0.55, p = 0.001), even after adjusting for age and Fazekas score (Spearman's r = 0.48, p = 0.006). This relationship was observed only in APOE4 carriers (Pearson's r = 0.66, p = 0.017), and was also retained after adjustment (Pearson's r = 0.76, p = 0.008; metabolite-by-carrier interaction p = 0.03). There were no findings in the PCC, nor in the negative control (late Braak stage) regions of the precuneus and precentral gyrus.

CONCLUSIONS

Our findings are in line with the spatially- and temporally-resolved Braak staging model of pathological severity in which the hippocampus is affected earlier than the PCC. The correlations, between MRS markers of synaptic and neuronal health and, respectively, T and N pathology, were found exclusively within APOE4 carriers, suggesting a connection with AD pathological change, rather than with normal aging. We therefore conclude that MRS has the potential to augment early A/T/N staging, with the hippocampus serving as a more sensitive MRS target compared to the PCC.

TensorFit: A torch-based tool for ultrafast metabolite fitting of large MRSI data sets

PURPOSE

To introduce a tool (TensorFit) for ultrafast and robust metabolite fitting of MRSI data based on Torch's auto-differentiation and optimization framework.

METHODS

TensorFit was implemented in Python based on Torch's auto-differentiation to fit individual metabolites in MRS spectra. The underlying time domain and/or frequency domain fitting model is based on a linear combination of metabolite spectroscopic response. The computational time efficiency and accuracy of TensorFit were tested on simulated and in vivo MRS data and compared against TDFDFit and QUEST.

RESULTS

TensorFit demonstrates a significant improvement in computation speed, achieving a 165-times acceleration compared with TDFDFit and 115 times against QUEST. TensorFit showed smaller percentual errors on simulated data compared with TDFDFit and QUEST. When tested on in vivo data, it performed similarly to TDFDFit with a 2% better fit in terms of mean squared error while obtaining a 169-fold speedup.

CONCLUSION

TensorFit enables fast and robust metabolite fitting in large MRSI data sets compared with conventional metabolite fitting methods. This tool could boost the clinical applicability of large 3D MRSI by enabling the fitting of large MRSI data sets within computation times acceptable in a clinical environment.

Chronic neuropathic pain components in whiplash-associated disorders correlate with metabolite concentrations in the anterior cingulate and dorsolateral prefrontal cortex: a consensus-driven MRS re-examination

Introduction

Whiplash injury (WHI) is characterised by a forced neck flexion/extension, which frequently occurs after motor vehicle collisions. Previous studies characterising differences in brain metabolite concentrations and correlations with neuropathic pain (NP) components with chronic whiplash-associated disorders (WAD) have been demonstrated in affective pain-processing areas such as the anterior cingulate cortex (ACC). However, the detection of a difference in metabolite concentrations within these cortical areas with chronic WAD pain has been elusive. In this study, single-voxel magnetic resonance spectroscopy (MRS), following the latest MRSinMRS consensus group guidelines, was performed in the anterior cingulate cortex (ACC), left dorsolateral prefrontal cortex (DLPFC), and occipital cortex (OCC) to quantify differences in metabolite concentrations in individuals with chronic WAD with or without neuropathic pain (NP) components.

Materials and methods

Healthy individuals (n = 29) and participants with chronic WAD (n = 29) were screened with the Douleur Neuropathique 4 Questionnaire (DN4) and divided into groups without (WAD-noNP, n = 15) or with NP components (WAD-NP, n = 14). Metabolites were quantified with LCModel following a single session in a 3 T MRI scanner within the ACC, DLPFC, and OCC.

Results

Participants with WAD-NP presented moderate pain intensity and interference compared with the WAD-noNP group. Single-voxel MRS analysis demonstrated a higher glutamate concentration in the ACC and lower total choline (tCho) in the DLPFC in the WAD-NP versus WAD-noNP group, with no intergroup metabolite difference detected in the OCC. Best fit and stepwise multiple regression revealed that the normalised ACC glutamate/total creatine (tCr) (p = 0.01), DLPFC n-acetyl-aspartate (NAA)/tCr (p = 0.001), and DLPFC tCho/tCr levels (p = 0.02) predicted NP components in the WAD-NP group (ACC r 2 = 0.26, α = 0.81; DLPFC r 2 = 0.62, α = 0.98). The normalised Glu/tCr concentration was higher in the healthy than the WAD-noNP group within the ACC (p < 0.05), but not in the DLPFC or OCC. Neither sex nor age affected key normalised metabolite concentrations related to WAD-NP components when compared to the WAD-noNP group.

Discussion

This study demonstrates that elevated glutamate concentrations within the ACC are related to chronic WAD-NP components, while higher NAA and lower tCho metabolite levels suggest a role for increased neuronal-glial signalling and cell membrane dysfunction in individuals with chronic WAD-NP components.

Local variation in brain temperature explains gender-specificity of working memory performance

Introduction

Exploring gender differences in cognitive abilities offers vital insights into human brain functioning.

Methods

Our study utilized advanced techniques like magnetic resonance thermometry, standard working memory n-back tasks, and functional MRI to investigate if gender-based variations in brain temperature correlate with distinct neuronal responses and working memory capabilities.

Results

We observed a significant decrease in average brain temperature in males during working memory tasks, a phenomenon not seen in females. Although changes in female brain temperature were significantly lower than in males, we found an inverse relationship between the absolute temperature change (ATC) and cognitive performance, alongside a correlation with blood oxygen level dependent (BOLD) signal change induced by neural activity. This suggests that in females, ATC is a crucial determinant for the link between cognitive performance and BOLD responses, a linkage not evident in males. However, we also observed additional female specific BOLD responses aligned with comparable task performance to that of males.

Discussion

Our results suggest that females compensate for their brain's heightened temperature sensitivity by activating additional neuronal networks to support working memory. This study not only underscores the complexity of gender differences in cognitive processing but also opens new avenues for understanding how temperature fluctuations influence brain functionality.

Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy

BACKGROUND

Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful.

MATERIALS AND METHODS

This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system.

RESULTS

The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and  absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content.

CONCLUSION

In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.

Deep learning pipeline for quality filtering of MRSI spectra

With the rise of novel 3D magnetic resonance spectroscopy imaging (MRSI) acquisition protocols in clinical practice, which are capable of capturing a large number of spectra from a subject's brain, there is a need for an automated preprocessing pipeline that filters out bad-quality spectra and identifies contaminated but salvageable spectra prior to the metabolite quantification step. This work introduces such a pipeline based on an ensemble of deep-learning classifiers. The dataset consists of 36,338 spectra from one healthy subject and five brain tumor patients, acquired with an EPSI variant, which implemented a novel type of spectral editing named SLOtboom-Weng (SLOW) editing on a 7T MR scanner. The spectra were labeled manually by an expert into four classes of spectral quality as follows: (i) noise, (ii) spectra greatly influenced by lipid-related artifacts (deemed not to contain clinical information), (iii) spectra containing metabolic information slightly contaminated by lipid signals, and (iv) good-quality spectra. The AI model consists of three pairs of networks, each comprising a convolutional autoencoder and a multilayer perceptron network. In the classification step, the encoding half of the autoencoder is kept as a dimensionality reduction tool, while the fully connected layers are added to its output. Each of the three pairs of networks is trained on different representations of spectra (real, imaginary, or both), aiming at robust decision-making. The final class is assigned via a majority voting scheme. The F1 scores obtained on the test dataset for the four previously defined classes are 0.96, 0.93, 0.82, and 0.90, respectively. The arguably lower value of 0.82 was reached for the least represented class of spectra mildly influenced by lipids. Not only does the proposed model minimise the required user interaction, but it also greatly reduces the computation time at the metabolite quantification step (by selecting a subset of spectra worth quantifying) and enforces the display of only clinically relevant information.

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time

PURPOSE

Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan.

METHODS

There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data.

RESULTS

Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics.

CONCLUSION

DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.