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Cognitive flexibility represents the capacity to switch among different mental schemes, providing an adaptive advantage to a changing environment. The neural underpinnings of this executive function have been deeply studied in humans through fMRI, showing that the left inferior frontal cortex (IFC) and the left inferior parietal lobule (IPL) are crucial. Here, we investigated the inhibitory– excitatory balance in these regions by means of γ-aminobutyric acid (GABA+) and glutamate + glutamine (Glx), measured with magnetic resonance spectroscopy, during a cognitive flexibility task and its relationship with the performance level and the local task-induced blood oxygenation level-dependent (BOLD) response in 40 young (18–35 years; 26 female) and 40 older (18–35 years; 21 female) human adults. As the IFC and the IPL are richly connected regions, we also examined whole-brain effects associated with their local metabolic activity. Results did not show absolute metabolic modulations associated with flexibility performance, but the performance level was related to the direction of metabolic modulation in the IPL with opposite patterns in young and older individuals. The individual inhibitory–excitatory balance modulation showed an inverse relationship with the local BOLD response in the IPL. Finally, the modulation of inhibitory–excitatory balance in IPL was related to whole-brain effects only in older individuals. These findings show disparities in the metabolic mechanisms underlying cognitive flexibility in young and older adults and their association with the performance level and BOLD response. Such metabolic differences are likely to play a role in executive functioning during aging and specifically in cognitive flexibility. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The neurobiological underpinnings of Autism Spectrum Disorder (ASD) are diverse and likely multifactorial. One possible mechanism is increased oxidative stress leading to altered neurodevelopment and brain function. However, this hypothesis has mostly been tested in post-mortem studies. So far, available in vivo studies in autistic individuals have reported no differences in glutathione (GSH) levels in frontal, occipital, and subcortical regions. However, these studies were limited by the technically challenging quantification of GSH, the main brain antioxidant molecule. This study aimed to overcome previous studies' limitations by using a GSH-tailored spectroscopy sequence and optimised quantification methodology to provide clarity on GSH levels in autistic adults. Methods: We used spectral editing proton-magnetic resonance spectroscopy (1H-MRS) combined with linear combination model fitting to quantify GSH in the dorsomedial prefrontal cortex (DMPFC) and medial occipital cortex (mOCC) of autistic and non-autistic adults (male and female). We compared GSH levels between groups. We also examined correlations between GSH and current autism symptoms, measured using the Autism Quotient (AQ). Results: Data were available from 31 adult autistic participants (24 males, 7 females) and 40 non-autistic participants (21 males, 16 females); the largest sample to date. The GSH levels did not differ between groups in either region. No correlations with AQ were observed. Conclusion: GSH levels as measured using 1H-MRS are unaltered in the DMPFC and mOCC regions of autistic adults, suggesting that oxidative stress in these cortical regions is not a marked neurobiological signature of ASD. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Literature values vary widely for within‐subject test–retest reproducibility of gamma‐aminobutyric acid (GABA) measured with edited magnetic resonance spectroscopy (MRS). Reasons for this variation remain unclear. Here, we tested whether three acquisition parameters—(1) sequence complexity (two‐experiment MEscher–GArwood Point RESolved Spectroscopy [MEGA‐PRESS] vs. four‐experiment Hadamard Encoding and Reconstruction of MEGA‐Edited Spectroscopy [HERMES]); (2) editing pulse duration (14 vs. 20 ms); and (3) scanner frequency drift (interleaved water referencing [IWR] turned ON vs. OFF)—and two linear combination modeling variations—(1) three different coedited macromolecule models (called "1to1GABA", "1to1GABAsoft", and "3to2MM" in the Osprey software package); and (2) 0.55‐ versus 0.4‐ppm spline baseline knot spacing—affected the within‐subject coefficient of variation of GABA + macromolecules (GABA+). We collected edited MRS data from the dorsal anterior cingulate cortex from 20 participants (mean age: 30.8 ± 9.5 years; 10 males). Test and retest scans were separated by removing the participant from the scanner for 5–10 min. Each acquisition consisted of two MEGA‐PRESS and two HERMES sequences with editing pulse durations of 14 and 20 ms (referred to here as MEGA‐14, MEGA‐20, HERMES‐14, and HERMES‐20; all TE = 80 ms, 224 averages). We identified the best test–retest reproducibility following postprocessing with a composite model of the 0.9‐ and 3‐ppm macromolecules ("3to2MM"); this model performed particularly well for the HERMES data. Furthermore, sparser (0.55‐ compared with 0.4‐ppm) spline baseline knot spacing yielded generally better test–retest reproducibility for GABA+. Replicating our prior results, linear combination modeling in Osprey compared with simple peak fitting in Gannet resulted in substantially better test–retest reproducibility. However, reproducibility did not consistently differ for MEGA‐PRESS compared with HERMES, for 14‐ compared with 20‐ms editing pulses, or for IWR‐ON versus IWR‐OFF. These results highlight the importance of model selection for edited MRS studies of GABA+, particularly for clinical studies that focus on individual patient differences in GABA+ or changes following an intervention. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate J‐difference coediting of glutamate using Hadamard encoding and reconstruction of Mescher‐Garwood‐edited spectroscopy (HERMES). Methods: Density‐matrix simulations of HERMES (TE 80 ms) and 1D J‐resolved (TE 31–229 ms) of glutamate (Glu), glutamine (Gln), γ‐aminobutyric acid (GABA), and glutathione (GSH) were performed. HERMES comprised four sub‐experiments with editing pulses applied as follows: (A) 1.9/4.56 ppm simultaneously (ONGABA/ONGSH); (B) 1.9 ppm only (ONGABA/OFFGSH); (C) 4.56 ppm only (OFFGABA/ONGSH); and (D) 7.5 ppm (OFFGABA/OFFGSH). Phantom HERMES and 1D J‐resolved experiments of Glu were performed. Finally, in vivo HERMES (20‐ms editing pulses) and 1D J‐resolved (TE 31–229 ms) experiments were performed on 137 participants using 3 T MRI scanners. LCModel was used for quantification. Results: HERMES simulation and phantom experiments show a Glu‐edited signal at 2.34 ppm in the Hadamard sum combination A+B+C+D with no overlapping Gln signal. The J‐resolved simulations and phantom experiments show substantial TE modulation of the Glu and Gln signals across the TEs, whose average yields a well‐resolved Glu signal closely matching the Glu‐edited signal from the HERMES sum spectrum. In vivo quantification of Glu show that the two methods are highly correlated (p < 0.001) with a bias of ∼10%, along with similar between‐subject coefficients of variation (HERMES/TE‐averaged: ∼7.3%/∼6.9%). Other Hadamard combinations produce the expected GABA‐edited (A+B–C–D) or GSH‐edited (A–B+C–D) signal. Conclusion: HERMES simulation and phantom experiments show the separation of Glu from Gln. In vivo HERMES experiments yield Glu (without Gln), GABA, and GSH in a single MRS scan. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication challenges and repetitive behaviors. Altered neurometabolite levels, including glutathione (GSH) and gamma‐aminobutyric acid (GABA), have been proposed as potential contributors to the biology underlying ASD. This study investigated whether cerebral GSH or GABA levels differ between a cohort of children aged 8–12 years with ASD (n = 52) and typically developing children (TDC, n = 49). A comprehensive analysis of GSH and GABA levels in multiple brain regions, including the primary motor cortex (SM1), thalamus (Thal), medial prefrontal cortex (mPFC), and supplementary motor area (SMA), was conducted using single‐voxel HERMES MR spectroscopy at 3T. The results revealed no significant differences in cerebral GSH or GABA levels between the ASD and TDC groups across all examined regions. These findings suggest that the concentrations of GSH (an important antioxidant and neuromodulator) and GABA (a major inhibitory neurotransmitter) do not exhibit marked alterations in children with ASD compared to TDC. A statistically significant positive correlation was observed between GABA levels in the SM1 and Thal regions with ADHD inattention scores. No significant correlation was found between metabolite levels and hyper/impulsive scores of ADHD, measures of core ASD symptoms (ADOS‐2, SRS‐P) or adaptive behavior (ABAS‐2). While both GSH and GABA have been implicated in various neurological disorders, the current study provides valuable insights into the specific context of ASD and highlights the need for further research to explore other neurochemical alterations that may contribute to the pathophysiology of this complex disorder. Lay Summary: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication challenges and repetitive behaviors. Altered glutathione (GSH, an important antioxidant and neuromodulator) and gamma‐aminobutyric acid (GABA, a major inhibitory neurotransmitter) levels have been proposed as potential contributors to the biology underlying ASD. Here, we used advanced edited magnetic resonance spectroscopy to measure levels of these low‐concentration metabolites in four brain regions of a pediatric cohort. Contrary to our hypothesis, no significant difference was found between ASD and control subjects in either GSH or GABA levels in any brain region. [ABSTRACT FROM AUTHOR]

Purpose: To compare the respective ability of PRESS and sLASER to reveal biological relationships, using age as a validation covariate at 3 T. Methods: MRS data were acquired from 102 healthy volunteers using PRESS and sLASER in centrum semiovale and posterior cingulate cortex (PCC). Acquisition parameters included TR/TE = 2000/30 ms, 96 transients, and 2048 datapoints sampled at 2 kHz. Spectra were analyzed using Osprey. SNR, FWHM linewidth of total creatine, and metabolite concentrations were extracted. A linear model was used to compare SNR and linewidth. Paired t‐tests were used to assess differences in metabolite measurements between PRESS and sLASER. Correlations were used to evaluate the relationship between PRESS and sLASER metabolite estimates, as well as the strength of each metabolite‐age relationship. Coefficients of variation were calculated to assess inter‐subject variability in each metabolite measurement. Results: SNR and linewidth were significantly higher (p < 0.01) for sLASER than PRESS in PCC. Paired t‐tests showed significant differences between PRESS and sLASER in most metabolite measurements. PRESS‐sLASER measurements were significantly correlated (p < 0.05) for most metabolites. Metabolite‐age relationships were consistently identified using both methods. Similar coefficients of variation were observed for most metabolites. Conclusion: The study results suggest strong agreement between PRESS and sLASER in identifying relationships between brain metabolites and age in centrum semiovale and PCC data acquired at 3 T. sLASER is technically desirable due to the reduced chemical shift displacement artifact; however, PRESS performed similarly in homogeneous brain regions at clinical field strength. [ABSTRACT FROM AUTHOR]

Synaptic plasticity relies on the balance between excitation and inhibition in the brain. As the primary inhibitory and excitatory neurotransmitters, gamma‐aminobutyric acid (GABA) and glutamate (Glu), play critical roles in synaptic plasticity and learning. However, the role of these neurometabolites in motor learning is still unclear. Furthermore, it remains to be investigated which neurometabolite levels from the regions composing the sensorimotor network predict future learning outcome. Here, we studied the role of baseline neurometabolite levels in four task‐related brain areas during different stages of motor skill learning under two different feedback (FB) conditions. Fifty‐one healthy participants were trained on a bimanual motor task over 5 days while receiving either concurrent augmented visual FB (CA‐VFB group, N = 25) or terminal intrinsic visual FB (TA‐VFB group, N = 26) of their performance. Additionally, MRS‐measured baseline GABA+ (GABA + macromolecules) and Glx (Glu + glutamine) levels were measured in the primary motor cortex (M1), primary somatosensory cortex (S1), dorsolateral prefrontal cortex (DLPFC), and medial temporal cortex (MT/V5). Behaviorally, our results revealed that the CA‐VFB group outperformed the TA‐VFB group during task performance in the presence of augmented VFB, while the TA‐VFB group outperformed the CA‐VFB group in the absence of augmented FB. Moreover, baseline M1 GABA+ levels positively predicted and DLPFC GABA+ levels negatively predicted both initial and long‐term motor learning progress in the TA‐VFB group. In contrast, baseline S1 GABA+ levels positively predicted initial and long‐term motor learning progress in the CA‐VFB group. Glx levels did not predict learning progress. Together, these findings suggest that baseline GABA+ levels predict motor learning capability, yet depending on the FB training conditions afforded to the participants. [ABSTRACT FROM AUTHOR]

Sensory processing abnormalities in autism have largely been described by parent report. This study used a multi-method (parent-report and measurement), multi-trait (tactile sensitivity and attention) design to evaluate somatosensory processing in ASD. Results showed multiple significant within-method (e.g., parent report of different traits)/cross-trait (e.g., attention and tactile sensitivity) correlations, suggesting that parent-reported tactile sensory dysfunction and performance-based tactile sensitivity describe different behavioral phenomena. Additionally, both parent-reported tactile functioning and performance-based tactile sensitivity measures were significantly associated with measures of attention. Findings suggest that sensory (tactile) processing abnormalities in ASD are multifaceted, and may partially reflect a more global deficit in behavioral regulation (including attention). Challenges of relying solely on parent-report to describe sensory difficulties faced by children/families with ASD are also highlighted., Author(s): Ericka L. Wodka[sup.1] [sup.2] , Nicolaas A. J. Puts[sup.3] [sup.4] , E. Mark Mahone[sup.2] [sup.5] , Richard A. E. Edden[sup.3] [sup.4] , Mark Tommerdahl[sup.6] , Stewart H. Mostofsky[sup.1] [sup.2] [...]