Objective: Subarachnoid hemorrhage (SAH) patients are monitored in the neurointensive care unit (NICU) to avoid additional brain injuries, yet methods for monitoring cerebral blood flow (CBF) are limited. The aim was to investigate the feasibility of simultaneous MRI, using arterial spin labeling (ASL) and 2D-flow MRI, and probe-based laser Doppler flowmetry (LDF) in NICU patients. Method: Four SAH patients, three of whom received optical probes during routine surgery, were included. Compatibility of simultaneous LDF and MRI was evaluated before performing MRI 3–4 times over several days per patient. The methods were compared using mean CBF in grey matter obtained from the absolute ASL CBF-maps, the total inflow calculated from 2D-flow MRI, and local average LDF perfusion. Additionally, regional mean CBF from ASL and each arterial flowrate was compared for each hemisphere. Result: Twelve MRI measurements were successfully performed, eight of which included simultaneous LDF. With careful routines and the MR scanner uniquely located in the NICU, neither patient safety nor data quality was compromised, demonstrating the feasibility of concurrent measurements. All methods showed longitudinal dynamic changes, following the same increasing or decreasing trends. In three patients, ASL and 2D-flow data were closely related, with dynamic changes within 10 %. Conclusion: This novel approach offers longitudinal, simultaneous estimates of macro- and microcirculatory components locally, regionally, and globally in the human brain. This concept has potential to provide insights into the interplay of different aspects of CBF in NICU patients and thereby aid in prevention of secondary brain injuries.

IntroductionThe mechanisms underlying the clinical effect of Deep Brain Stimulation (DBS) for Parkinson’s disease (PD) remain debated. Proton magnetic resonance spectroscopy (1H-MRS) provides a biochemical non-invasive in vivo insight. This article aims to increase the understanding of advanced PD pathophysiology and DBS using MRS before and after surgery.MethodsEleven PD patients and seven healthy controls were included. Preoperatively and approximately 7 months postoperatively, single-voxel MRS using a PRESS sequence was performed on a 1.5 T (tesla) system. Voxels were placed bilaterally in the thalamus (14 × 13 × 13 mm3) and the lentiform nucleus (15 × 13 × 12 mm3). Metabolite concentrations of total N-acetylaspartate + N-acetyl-aspartyl-glutamate (tNA), total creatine + phosphocreatine (tCr), total choline + phosphocholine + glycerophosphocholine (tCho), and total glutamate and glutamine, which together constitute Glx were quantified. To assess treatment outcomes following surgery, medications were converted to levodopa equivalent doses (LED) using a standardized conversion formula, both pre- and post-DBS.ResultsA total of 11 patients, with a mean PD duration of 9.4 years, were implanted with bilateral implantation (22 leads). All patients self-reported relief of symptoms and significantly reduced their medication (p < 0.001), with a calculated preoperative LED of 925 ± 272 and a postoperative LED of 611 ± 210 (mean ± SD), representing a 32% reduction after surgery. The patients, prior to surgery, compared to a healthy control group, showed no differences in the resulting metabolite concentrations (tCr, tNA, tCho, Glx) in voxels placed in the thalamus and lentiform nucleus. However, thalamic tNA concentrations differed significantly following DBS targeting the subthalamic nucleus (STN), both in comparison to healthy controls (p = 0.02) and relative to preoperative concentrations within the patient group (p = 0.03). No furher resulting concentrations differed.ConclusionWe present novel metabolite observations obtained through MRS in this exploratory study. Thalamic tNA concentrations in PD patients were comparable to those of healthy controls prior to surgery, but were significantly reduced following DBS implantation targeting the STN. These findings suggests the presence of a metabolite thalamic effect associated with DBS treatment.

IntroductionThe pathophysiology behind essential tremor (ET) and the mechanisms behind the clinical effect after deep brain stimulation (DBS) is not fully understood. This article aims to increase the understanding of ET pathophysiology and the mechanisms behind DBS using magnetic resonance spectroscopy (1H-MRS). Patients with ET underwent MRS scans of the cerebellum and thalamus before and after DBS, and the results were compared to a healthy control group.MethodsTen ET patients and seven healthy controls were included. Preoperatively and ~5 months postoperatively, single-voxel MRS was performed on a 1.5 T (tesla) system. Voxels were placed in the thalamus (14 × 13 × 13 mm3), dentate nucleus (13 × 13 × 13 mm3), and cerebellar cortex (13 × 13 × 13 mm3). Metabolite concentrations of total N-acetylaspartate + N-acetyl-aspartyl-glutamate (tNA), total creatine + phosphocreatine (tCr), total choline + phosphocholine + glycerophosphocholine (tCho), and total glutamate and glutamine, which together constitute Glx, were quantified. The patients were evaluated with the Essential Tremor Rating Scale (ETRS) preoperatively and postoperatively.ResultsA total of 14 leads were implanted, and ETRS scores improved significantly following surgery. Thalamic tNA concentrations reduced significantly within the patient group after surgery, as well as in comparison to healthy control values. A significant difference in tNA concentration following surgery was observed only in the thalamus that had been targeted with a lead, not elsewhere. No additional differences in metabolite concentrations (tCr, tCho, Glx) were observed in the thalamic voxel, and none of the studied metabolites (tNA, tCr, tCho, Glx) showed detectable differences in the cerebellar voxels (dentate nucleus and cerebellar cortex).ConclusionIn a highly selected patient group affected by ET, we present novel metabolite information using MRS. Specifically, a reduced thalamic tNA concentration was observed on the lead-implanted side following DBS, suggesting a possible treatment effect.

Cerebral blood flow (CBF) is carefully monitored in the Neurointensive Care Unit (NICU) to prevent secondary brain insults in patients who have suffered subarachnoid hemorrhage. Including absolute MRI measurements of CBF in the NICU monitoring protocol could add valuable information and potentially improve patient outcomes. This is particularly feasible at Linköping University Hospital, which uniquely has an MRI scanner located in the NICU, enabling longitudinal CBF measurements while eliminating medical transportation risks. Arterial spin labeling is a subtraction-based MRI technique that can measure CBF globally in the brain without the use of contrast agents, and thus is suitable for repeated measurements over short time periods. Therefore, this work aims to develop and implement a methodological workflow for the acquisition, analysis, absolute quantification, and visualization of longitudinal arterial spin labeling MRI measurements acquired in the clinical NICU setting. At this initial stage, the workflow was implemented and tested using acquired test-retest data and longitudinal data from two healthy participants. Subsequently, the workflow was tested in clinical practice on an intubated and ventilated patient monitored in the NICU after suffering a subarachnoid hemorrhage. To ensure accurate day-to-day comparisons between the repeated measurements, the selection of processing and analysis methods aimed to obtain CBF maps in absolute units of ml/min/100g. These CBF maps were quantified using both the FMRIB Software Library and an openly available flow territory atlas. The test-retest data showed small variations (4.4 ml/min/100g between sessions), and the longitudinal measurement resulted in low CBF variability over 12 days. Despite the greater complexity of clinical data, the quantification and chosen visualization tools proved helpful in interpreting the results. In conclusion, this workflow including repeated MRI measurements could help detect changes in CBF between different measurement days and complement other conventional monitoring techniques in the NICU.

Purpose To investigate the editing-pulse flip angle (FA) dependence of editing efficiency and ultimately to maximize the edited signal of commonly edited MR spectroscopy (MRS) signals, such as gamma-aminobutyric acid (GABA) and lactate. Methods Density-matrix simulations were performed for a range of spin systems to find the editing-pulse FA for maximal editing efficiency. Simulations were confirmed by phantom experiments and in vivo measurements in 10 healthy participants using a 3T Philips scanner. Four MEGA-PRESS in vivo measurements targeting GABA+ and lactate were performed, comparing the conventional editing-pulse FA (FA = 180 degrees) to the optimal one suggested by simulations (FA = 210 degrees). Results Simulations and phantom experiments show that edited GABA and lactate signals are maximal at FA = 210 degrees. Compared to conventional editing (FA = 180 degrees), in vivo signals from GABA+ and lactate signals increase on average by 8.5% and 9.3%, respectively. Conclusion Increasing the FA of editing-pulses in the MEGA-PRESS experiment from 180 degrees to 210 degrees increases the edited signals from GABA+ and lactate by about 9% in vivo.

Objective: Irritable bowel syndrome (IBS) is a chronic pain disorder characterized by disturbed interactions between the gut and the brain with depression as a common comorbidity. In both IBS and depression, structural brain alterations of the insular cortices, key structures for pain processing and interoception, have been demonstrated but the specificity of these findings remains unclear. We compared the gray matter volume (GMV) of insular cortex (IC) subregions in IBS women and healthy controls (HC) and examined relations to gastrointestinal (GI) symptoms and glutamate + glutamine (Glx) concentrations. We further analyzed GMV of IC subregions in women with major depression (MDD) compared to HC and addressed possible differences between depression, IBS, IBS with depression and HC. Design: Women with IBS (n = 75), MDD (n = 41) and their respective HC (n = 39 and n = 43) underwent structural brain MRI. IC subregion volumes were estimated using statistical parametric mapping software. General linear model approaches were applied to IC volumetric data and FDR-corrected partial correlation analyses assessed relations between GMV, GI symptoms and Glx concentrations. Results: IBS patients had significantly smaller IC subregions than HC in both hemispheres but there was no significant difference between MDD compared with IBS and HC for any insular subregion. In IBS, the dorsal anterior insular volumes were negatively correlated with symptoms of nausea and pain, and the left ventral subregion showed a positive correlation with straining to defecate, while the posterior subregion volumes showed no relation to symptoms. In the anterior insula, concentration of Glx showed positive correlations with GMV bilaterally in HC and with GMV of the right anterior insula in IBS. Conclusion: As the interoceptive cortex, the insula shows substantial and disease-specific structural differences in patients with chronic interoceptive visceral pain. Particularly changes in the anterior proportions might be related to chronic exposure to or enhanced salience towards adverse interoceptive visceral signals and could be linked to biochemical changes, calling for further multimodal and longitudinal work.

PurposeTo investigate whether a deep learning‐based (DL) approach can be used for frequency‐and‐phase correction (FPC) of MEGA‐edited MRS data.MethodsTwo neural networks (1 for frequency, 1 for phase) consisting of fully connected layers were trained and validated using simulated MEGA‐edited MRS data. This DL‐FPC was subsequently tested and compared to a conventional approach (spectral registration [SR]) and to a model‐based SR implementation (mSR) using in vivo MEGA‐edited MRS datasets. Additional artificial offsets were added to these datasets to further investigate performance.ResultsThe validation showed that DL‐based FPC was capable of correcting within 0.03 Hz of frequency and 0.4°of phase offset for unseen simulated data. DL‐based FPC performed similarly to SR for the unmanipulated in vivo test datasets. When additional offsets were added to these datasets, the networks still performed well. However, although SR accurately corrected for smaller offsets, it often failed for larger offsets. The mSR algorithm performed well for larger offsets, which was because the model was generated from the in vivo datasets. In addition, the computation times were much shorter using DL‐based FPC or mSR compared to SR for heavily distorted spectra.ConclusionThese results represent a proof of principle for the use of DL for preprocessing MRS data.

Purpose To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex. Methods 102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 x 26 x 26 mm(3), pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a "clean" MM spectrum using the Osprey software. Pearson's correlation coefficient was calculated between integrals and age for the 14 MM signals. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes. Results MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM-predominant CSO (R = -0.29) and GM-predominant PCC regions (R = -0.57) respectively while CSF increased significantly with age in both regions. Conclusion Our findings suggest that a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.

Purpose J-difference-edited spectroscopy is a valuable approach for the in vivo detection of γ-aminobutyric-acid (GABA) with MRS. A recent expert consensus article recommends linear combination modeling (LCM) of edited MRS but does not give specific details of implementation. This study explores different modeling strategies to adapt LCM for GABA-edited MRS. Methods 61 medial parietal lobe GABA-edited MEGA-PRESS spectra from a recent 3T multi-site study were modeled using 102 different strategies combining six different approaches to account for co-edited macromolecules, three modeling ranges, three baseline knot spacings, and the use of basis sets with or without homocarnosine. The resulting GABA and GABA+ estimates (quantified relative to total creatine), the residuals at different ranges, SDs and CVs, and Akaike information criteria, were used to evaluate the models’ performance. Results Significantly different GABA+ and GABA estimates were found when a well-parameterized MM3co basis function was included in the model. The mean GABA estimates were significantly lower when modeling MM, while the CVs were similar. A sparser spline knot spacing led to lower variation in the GABA and GABA+ estimates, and a narrower modeling range – only including the signals of interest – did not substantially improve or degrade modeling performance. Additionally, results suggest that LCM can separate GABA and the underlying co-edited MM3co. Incorporating homocarnosine into the modeling did not significantly improve variance in GABA+ estimates. Conclusion GABA-edited MRS is most appropriately quantified by LCM with a well-parameterized co-edited MM3co basis function with a constraint to the non-overlapped MM0.93, in combination with a sparse spline knot spacing (0.55 ppm) and a modeling range between 0.5 and 4 ppm. Graphical Abstract 102 strategies to model GABA-edited MRS with linear combination modeling were evaluated to quantify GABA and GABA+ in Osprey. Significantly different GABA and GABA+ estimates were found when a well-parameterized macro-molecule at 3 ppm was included. The findings suggest that linear combination modeling needs to be adapted for quantification of GABA-edited MRS.

J‐difference‐edited spectroscopy is a valuable approach for the in vivo detection of γ‐aminobutyric‐acid (GABA) with magnetic resonance spectroscopy (MRS). A recent expert consensus article recommends linear combination modeling (LCM) of edited MRS but does not give specific details regarding implementation. This study explores different modeling strategies to adapt LCM for GABA‐edited MRS. Sixty‐one medial parietal lobe GABA‐edited MEGA‐PRESS spectra from a recent 3‐T multisite study were modeled using 102 different strategies combining six different approaches to account for co‐edited macromolecules (MMs), three modeling ranges, three baseline knot spacings, and the use of basis sets with or without homocarnosine. The resulting GABA and GABA+ estimates (quantified relative to total creatine), the residuals at different ranges, standard deviations and coefficients of variation (CVs), and Akaike information criteria, were used to evaluate the models' performance. Significantly different GABA+ and GABA estimates were found when a well‐parameterized MM3cobasis function was included in the model. The mean GABA estimates were significantly lower when modeling MM3co, while the CVs were similar. A sparser spline knot spacing led to lower variation in the GABA and GABA+ estimates, and a narrower modeling range—only including the signals of interest—did not substantially improve or degrade modeling performance. Additionally, the results suggest that LCM can separate GABA and the underlying co‐edited MM3co. Incorporating homocarnosine into the modeling did not significantly improve variance in GABA+ estimates. In conclusion, GABA‐edited MRS is most appropriately quantified by LCM with a well‐parameterized co‐edited MM3cobasis function with a constraint to the nonoverlapped MM0.93, in combination with a sparse spline knot spacing (0.55 ppm) and a modeling range of 0.5–4 ppm.

Purpose Mobile macromolecules (MMs) from amino acids, cytosolic proteins and mobile lipids contribute a significant spectral background underlying the metabolite signals in the MR spectrum. A recent consensus recommends that MM contributions should be removed or included in modeling basis sets for determination of metabolite concentrations and/or metabolite ratios. The purpose of this study was to acquire the MM spectrum from healthy participants at a range of ages, and to investigate changes in the signals with age and sex groups. Methods Inversion time (TI) series were acquired to determine an optimal inversion time to null the metabolite signals. Experiments were carried out using a single adiabatic hyperbolic-secant inversion pulse. After the preliminary experiment, 102 volunteers (49M/53F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). The protocol consisted of a T1-weighted MPRAGE for structural images, followed by PRESS localization using a voxel size of 30 × 26 × 26 mm3 with pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled using a reduced basis set (NAA, Cr, Cho, Glu) and a flexible spline baseline (0.1 ppm knot spacing) followed by subtraction of the modeled metabolite signals to yield a ‘clean’ MM spectrum, using the Osprey software. Pearson’s correlation coefficient was calculated between integrals and age for the 14 MM signals between 0.9–4.2 ppm. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes. Relationships between brain tissues with age and sex groups were also measured. Results MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM integrals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > 0.004). No differences were observed between sex groups. The voxels were segmented as 80 ± 4% white matter, 18 ± 4% gray matter, and 2 ± 1% CSF for CSO and 28 ± 4% white matter, 61 ± 4% gray matter and 11 ± 1% CSF for PCC. WM and GM showed a significant (p < 0.05) negative linear association with age in the WM-predominant CSO (R = −0.29) and GM-predominant PCC regions (R = −0.57) respectively while CSF increased significantly with age in both regions. Conclusion Our findings indicate that the MM spectrum is stable across a large age range and between sexes, suggesting a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups. Highlights A large publicly available MM-aging dataset is presented. Macromolecule signals do not change with age between 20 and 70. There is no sex difference for macromolecule integrals.

Short-TE proton MRS is used to study metabolism in the human brain. Common analysis methods model the data as a linear combination of metabolite basis spectra. This large-scale multi-site study compares the levels of the four major metabolite complexes in short-TE spectra estimated by three linear-combination modeling (LCM) algorithms. 277 medial parietal lobe short-TE PRESS spectra (TE = 35 ms) from a recent 3 T multi-site study were preprocessed with the Osprey software. The resulting spectra were modeled with Osprey, Tarquin and LCModel, using the same three vendor-specific basis sets (GE, Philips and Siemens) for each algorithm. Levels of total N-acetylaspartate (tNAA), total choline (tCho), myo-inositol (mI) and glutamate + glutamine (Glx) were quantified with respect to total creatine (tCr). Group means and coefficient of variations of metabolite estimates agreed well for tNAA and tCho across vendors and algorithms, but substantially less so for Glx and mI, with mI systematically estimated as lower by Tarquin. The cohort mean coefficient of determination for all pairs of LCM algorithms across all datasets and metabolites was R2¯ = 0.39, indicating generally only moderate agreement of individual metabolite estimates between algorithms. There was a significant correlation between local baseline amplitude and metabolite estimates (cohort mean R2¯ = 0.10). While mean estimates of major metabolite complexes broadly agree between linear-combination modeling algorithms at group level, correlations between algorithms are only weak-to-moderate, despite standardized preprocessing, a large sample of young, healthy and cooperative subjects, and high spectral quality. These findings raise concerns about the comparability of MRS studies, which typically use one LCM software and much smaller sample sizes.

Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). Results: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.

Objective Essential tremor is a common movement disorder with an unclear origin. Emerging evidence suggests the role of the cerebellum and the thalamus in tremor pathophysiology. We examined the two main neurotransmitters acting inhibitory (GABA+) and excitatory (Glx) respectively, in the thalamus and cerebellum, in patients diagnosed with severe essential tremor. Furthermore, we also investigated the relationship between determined neurotransmitter concentrations and tremor severity in the essential tremor patients. Methods Ten essential tremor patients (prior to deep brain stimulation surgery) and six healthy controls, were scanned using a 3 T MR system. GABA+ and Glx concentrations were measured using magnetic resonance spectroscopy (MRS) performed using single voxel MEGA-PRESS. For the purpose of assessing the tremor severity, the essential tremor rating scale (ETRS) was used in accordance with Fahn, Tolosa, and Marin. Results We demonstrated that the cerebellar GABA+/Glx ratio was positively correlated to the ETRS (r = 0.70, p = 0.03) in essential tremor. Cerebellar and thalamic GABA+ and Glx concentrations did not show any significant difference when comparing essential tremor patients with healthy controls, at the group level. Conclusion We demonstrated a positive correlation between increasing tremor disability and the ratio of GABA+/ Glx in the cerebellum of essential tremor patients. This highlights the impact of an altered balance of the excitatory and inhibitory neurotransmitters in tremor severity. Rather than a change in GABA+, which was constant, we attribute this finding to an overall decrease of Glx.

Background Processing and quantitative analysis of magnetic resonance spectroscopy (MRS) data are far from standardized and require interfacing with third-party software. Here, we present Osprey, a fully integrated open-source data analysis pipeline for MRS data, with seamless integration of pre-processing, linear-combination modelling, quantification, and data visualization. New Method Osprey loads multiple common MRS data formats, performs phased-array coil combination, frequency-and phase-correction of individual transients, signal averaging and Fourier transformation. Linear combination modelling of the processed spectrum is carried out using simulated basis sets and a spline baseline. The MRS voxel is coregistered to an anatomical image, which is segmented for tissue correction and quantification is performed based upon modelling parameters and tissue segmentation. The results of each analysis step are visualized in the Osprey GUI. The analysis pipeline is demonstrated in 12 PRESS, 11 MEGA-PRESS, and 8 HERMES datasets acquired in healthy subjects. Results Osprey successfully loads, processes, models, and quantifies MRS data acquired with a variety of conventional and spectral editing techniques. Comparison with Existing Method(s) Osprey is the first MRS software to combine uniform pre-processing, linear-combination modelling, tissue correction and quantification into a coherent ecosystem. Compared to existing compiled, often closed-source modelling software, Osprey’s open-source code philosophy allows researchers to integrate state-of-the-art data processing and modelling routines, and potentially converge towards standardization of analysis. Conclusions Osprey combines robust, peer-reviewed data processing methods into a modular workflow that is easily augmented by community developers, allowing the rapid implementation of new methods.

An algorithm for retrospective correction of frequency and phase offsets in MRS data is presented. The algorithm, termed robust spectral registration (rSR), contains a set of subroutines designed to robustly align individual transients in a given dataset even in cases of significant frequency and phase offsets or unstable lipid contamination and residual water signals. Data acquired by complex multiplexed editing approaches with distinct subspectral profiles are also accurately aligned. Automated removal of unstable lipid contamination and residual water signals is applied first, when needed. Frequency and phase offsets are corrected in the time domain by aligning each transient to a weighted average reference in a statistically optimal order using nonlinear least-squares optimization. The alignment of subspectra in edited datasets is performed using an approach that specifically targets subtraction artifacts in the frequency domain. Weighted averaging is then used for signal averaging to down-weight poorer-quality transients. Algorithm performance was assessed on one simulated and 67 in vivo pediatric GABA-/GSH-edited HERMES datasets and compared with the performance of a multistep correction method previously developed for aligning HERMES data. The performance of the novel approach was quantitatively assessed by comparing the estimated frequency/phase offsets against the known values for the simulated dataset or by examining the presence of subtraction artifacts in the in vivo data. Spectral quality was improved following robust alignment, especially in cases of significant spectral distortion. rSR reduced more subtraction artifacts than the multistep method in 64% of the GABA difference spectra and 75% of the GSH difference spectra. rSR overcomes the major challenges of frequency and phase correction.

There is an increasing interest in studying the concentration of the inhibitory neurotransmitter γ-Amino Butyric Acid (GABA), both in the healthy and diseased brain by using Magnetic Resonance Spectroscopy (MRS). Recent studies have shown correlations between an abnormal GABA concentration in certain regions of the brain and disorders as e.g. Parkinson’s disease and depressive disorders. There are presently many technical difficulties with the absolute quantification of GABA and the method MEGA-PRESS is currently the standard technique used in data acquisitions and processing of spectra. In this thesis, different techniques of GABA quantification have been evaluated and the most important aspect was to explore the precision of the method for further usage as a clinical tool. This project involved the exploration of data acquisitions by using a MEGA-PRESS sequence on a 3 T MR-system, processing of the resulting datasets using different methodologies, GABA quantification by using linear combination of model spectra (LCModel), and interpretation of the results by performing statistical analyses. The thesis resulted in a low resolution GABA-atlas of the brain which did not indicate any significant differences in the GABA concentration within the healthy subject group. However, a significant regional difference was observed in the brain. The main uncertainties arose mainly due to the relatively small subject groups and the large measurement error. Future measurements will require improvements both in the data acquisition and in analyzing these with an improved method of processing. The final conclusion was that the GABA quantification sequence MEGA-PRESS is useful both in diagnosis and as a research tool, although further improvements are required.

Altered neural mechanisms are well-acknowledged in irritable bowel syndrome (IBS), a disorder of brain-gut-communication highly comorbid with anxiety and depression. As a key hub in corticolimbic inhibition, medial prefrontal cortex (mPFC) may be involved in disturbed emotion regulation in IBS. However, aberrant mPFC excitatory and inhibitory neurotransmission potentially contributing to psychological symptoms in IBS remains unknown. Using quantitative magnetic resonance spectroscopy (qMRS), we compared mPFC glutamate + glutamine (Glx) and γ-aminobutyric acid (GABA+) concentrations in 64 women with IBS and 32 age-matched healthy women (HCs) and investigated their association with anxiety and depression in correlational and subgroup analyses. Applying functional magnetic resonance imaging (fMRI), we explored whether altered neurotransmission was paralleled by aberrant mPFC resting-state functional connectivity (FC). IBS patients did not differ from HCs with respect to mPFC GABA+ or Glx levels. Anxiety was positively associated with mPFC GABA+ concentrations in IBS, whereas Glx was unrelated to psychological or gastrointestinal symptoms. Subgroup comparisons of patients with high or low anxiety symptom severity and HCs revealed increased GABA+ in patients with high symptom severity, and lower mPFC FC with adjacent anterior cingulate cortex (ACC), a crucial region of emotion modulation. Our findings provide novel evidence that altered prefrontal inhibitory neurotransmission may be linked to anxiety in IBS.

Supplemental Digital Content is Available in the Text. Quantitative magnetic resonance spectroscopy of anterior insula reveals decreased glutamatergic neurotransmitter concentrations in patients with irritable bowel syndrome and hemisphere-specific associations with pain severity and pain-related coping.

The study investigated brain activity changes during performance of a verbal working memory task in a population of adolescents with narcolepsy. Seventeen narcolepsy patients and twenty healthy controls performed a verbal working memory task during simultaneous fMRI and EEG acquisition. All subjects also underwent MRS to measure GABA and Glutamate concentrations in the medial prefrontal cortex. Activation levels in the default mode network and left middle frontal gyrus were examined to investigate whether narcolepsy is characterized by an imbalance in cognitive resources. Significantly increased deactivation within the default mode network during task performance was observed for the narcolepsy patients for both the encoding and recognition phases of the task. No evidence for task performance deficits or reduced activation within the left middle frontal gyrus was noted for the narcolepsy patients. Correlation analyses between the spectroscopy and fMRI data indicated that deactivation of the anterior aspect of the default mode in narcolepsy patients correlated more with increased concentrations of Glutamate and decreased concentrations of GABA. In contrast, deactivation in the default mode was correlated with increased concentrations of GABA and decreased concentrations of Glutamate in controls. The results suggested that narcolepsy is not characterized by a deficit in working memory but rather an imbalance of cognitive resources in favor of monitoring and maintaining attention over actual task performance. This points towards dysregulation within the sustained attention system being the origin behind self-reported cognitive difficulties in narcolepsy.