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Li Y, Ruhm L, Wang Z, Zhao R, Anderson A, Arnold P, Huesmann G, Henning A, Lam F. Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction. Magn Reson Med 2025; 93:455-469. [PMID: 39233507 PMCID: PMC11604835 DOI: 10.1002/mrm.30276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 07/16/2024] [Accepted: 08/12/2024] [Indexed: 09/06/2024]
Abstract
PURPOSE To develop and evaluate a novel method for computationally efficient reconstruction from noisy MR spectroscopic imaging (MRSI) data. METHODS The proposed method features (a) a novel strategy that jointly learns a nonlinear low-dimensional representation of high-dimensional spectroscopic signals and a neural-network-based projector to recover the low-dimensional embeddings from noisy/limited data; (b) a formulation that integrates the forward encoding model, a regularizer exploiting the learned representation, and a complementary spatial constraint; and (c) a highly efficient algorithm enabled by the learned projector within an alternating direction method of multipliers (ADMM) framework, circumventing the computationally expensive network inversion subproblem. RESULTS The proposed method has been evaluated using simulations as well as in vivo 1 $$ {}^1 $$ H and 31 $$ {}^{31} $$ P MRSI data, demonstrating improved performance over state-of-the-art methods, with about 6× $$ \times $$ fewer averages needed than standard Fourier reconstruction for similar metabolite estimation variances and up to 100× $$ \times $$ reduction in processing time compared to a prior neural network constrained reconstruction method. Computational and theoretical analyses were performed to offer further insights into the effectiveness of the proposed method. CONCLUSION A novel method was developed for fast, high-SNR spatiospectral reconstruction from noisy MRSI data. We expect our method to be useful for enhancing the quality of MRSI or other high-dimensional spatiospectral imaging data.
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Affiliation(s)
- Yahang Li
- Department of BioengineeringUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Loreen Ruhm
- Advanced Imaging Research CenterUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- High‐Field Magnetic Resonance CenterMax Planck Institute for Biological CyberneticsTübingenGermany
| | - Zepeng Wang
- Department of BioengineeringUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Ruiyang Zhao
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Department of Electrical and Computer EngineeringUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
| | - Aaron Anderson
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Carle Neuroscience InstituteCarle Foundation HospitalUrbanaIllinoisUSA
| | - Paul Arnold
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Carle Neuroscience InstituteCarle Foundation HospitalUrbanaIllinoisUSA
| | - Graham Huesmann
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Carle Neuroscience InstituteCarle Foundation HospitalUrbanaIllinoisUSA
| | - Anke Henning
- Advanced Imaging Research CenterUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- High‐Field Magnetic Resonance CenterMax Planck Institute for Biological CyberneticsTübingenGermany
| | - Fan Lam
- Department of BioengineeringUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
- Department of Electrical and Computer EngineeringUniversity of Illinois Urbana‐ChampaignUrbanaIllinoisUSA
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2
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Bell TK, Goerzen D, Near J, Harris AD. Examination of methods to separate overlapping metabolites at 7T. Magn Reson Med 2025; 93:470-480. [PMID: 39344348 PMCID: PMC11604845 DOI: 10.1002/mrm.30293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 08/09/2024] [Accepted: 08/25/2024] [Indexed: 10/01/2024]
Abstract
PURPOSE Neurochemicals of interest quantified by MRS are often composites of overlapping signals. At higher field strengths (i.e., 7T), there is better separation of these signals. As the availability of higher field strengths is increasing, it is important to re-evaluate the separability of overlapping metabolite signals. METHODS This study compares the ability of stimulated echo acquisition mode (STEAM-8; TE = 8 ms), short-TE semi-LASER (sLASER-34; TE = 34 ms), and long-TE semi-LASER (sLASER-105; TE = 105 ms) acquisitions to separate the commonly acquired neurochemicals at 7T (Glx, consisting of glutamate and glutamine; total N-acetyl aspartate, consisting of N-acetyl aspartate and N-acetylaspartylglutamate; total creatine, consisting of creatine and phosphocreatine; and total choline, consisting of choline, phosphocholine, and glycerophosphocholine). RESULTS sLASER-34 produced the lowest fit errors for most neurochemicals; however, STEAM-8 had better within-subject reproducibility and required fewer subjects to detect a change between groups. However, this is dependent on the neurochemical of interest. CONCLUSION We recommend short-TE STEAM for separation of most standard neurochemicals at 7T over short-TE or long-TE sLASER.
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Affiliation(s)
- Tiffany K. Bell
- Department of RadiologyUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
| | - Dana Goerzen
- Weill Cornell MedicineCornell UniversityNew YorkNew YorkUSA
| | - Jamie Near
- Physical Studies Research PlatformSunnybrook Research InstituteTorontoOntarioCanada
- Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
| | - Ashley D. Harris
- Department of RadiologyUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain InstituteUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research InstituteUniversity of CalgaryCalgaryAlbertaCanada
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3
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Dell'Orco A, Riemann LT, Ellison SLR, Aydin S, Göschel L, Ittermann B, Tietze A, Scheel M, Fillmer A. Macromolecule Modelling for Improved Metabolite Quantification Using Short Echo Time Brain 1H-MRS at 3 T and 7 T: The PRaMM Model. NMR IN BIOMEDICINE 2025; 38:e5299. [PMID: 39701127 DOI: 10.1002/nbm.5299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 11/13/2024] [Accepted: 11/14/2024] [Indexed: 12/21/2024]
Abstract
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.
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Affiliation(s)
- Andrea Dell'Orco
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Institute of Neuroradiology, Berlin, Germany
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Berlin, Germany
- Department of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- NeuroCure Clinical Research, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Layla Tabea Riemann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Berlin, Germany
- Institute for Applied Medical Informatics, University Hospital Hamburg-Eppendorf (UKE), Hamburg, Germany
| | | | - Semiha Aydin
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Berlin, Germany
| | - Laura Göschel
- Department of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- NeuroCure Clinical Research, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Berlin, Germany
| | - Anna Tietze
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Institute of Neuroradiology, Berlin, Germany
| | - Michael Scheel
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Institute of Neuroradiology, Berlin, Germany
| | - Ariane Fillmer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Berlin, Germany
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4
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Emeliyanova P, Parkes LM, Williams SR, Lea-Carnall C. Evidence for biexponential glutamate T 2 relaxation in human visual cortex at 3T: A functional MRS study. NMR IN BIOMEDICINE 2024; 37:e5240. [PMID: 39188210 DOI: 10.1002/nbm.5240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 04/30/2024] [Accepted: 08/02/2024] [Indexed: 08/28/2024]
Abstract
Functional magnetic resonance spectroscopy (fMRS) measures dynamic changes in metabolite concentration in response to neural stimulation. The biophysical basis of these changes remains unclear. One hypothesis suggests that an increase or decrease in the glutamate signal detected by fMRS could be due to neurotransmitter movements between cellular compartments with different T2 relaxation times. Previous studies reporting glutamate (Glu) T2 values have generally sampled at echo times (TEs) within the range of 30-450 ms, which is not adequate to observe a component with short T2 (<20 ms). Here, we acquire MRS measurements for Glu, (t) total creatine (tCr) and total N-acetylaspartate (tNAA) from the visual cortex in 14 healthy participants at a range of TE values between 9.3-280 ms during short blocks (64 s) of flickering checkerboards and rest to examine both the short- and long-T2 components of the curve. We fit monoexponential and biexponential Glu, tCr and tNAA T2 relaxation curves for rest and stimulation and use Akaike information criterion to assess best model fit. We also include power calculations for detection of a 2% shift of Glu between compartments for each TE. Using pooled data over all participants at rest, we observed a short Glu T2-component with T2 = 10 ms and volume fraction of 0.35, a short tCr T2-component with T2 = 26 ms and volume fraction of 0.25 and a short tNAA T2-component around 15 ms with volume fraction of 0.34. No statistically significant change in Glu, tCr and tNAA signal during stimulation was detected at any TE. The volume fractions of short-T2 component between rest and active conditions were not statistically different. This study provides evidence for a short T2-component for Glu, tCr and tNAA but no evidence to support the hypothesis of task-related changes in glutamate distribution between short and long T2 compartments.
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Affiliation(s)
- Polina Emeliyanova
- School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom of Great Britain and Northern Ireland
- Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom of Great Britain and Northern Ireland
| | - Laura M Parkes
- School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom of Great Britain and Northern Ireland
- Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom of Great Britain and Northern Ireland
| | - Stephen R Williams
- School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom of Great Britain and Northern Ireland
| | - Caroline Lea-Carnall
- School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom of Great Britain and Northern Ireland
- Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom of Great Britain and Northern Ireland
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5
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Campos L, Swanberg KM, Gajdošík M, Landheer K, Juchem C. Improvements in precision and accuracy of complex- relative to real-domain linear combination model spectral fitting not necessarily recovered by zero filling. NMR IN BIOMEDICINE 2024; 37:e5236. [PMID: 39138125 DOI: 10.1002/nbm.5236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/18/2024] [Accepted: 07/23/2024] [Indexed: 08/15/2024]
Abstract
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.
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Affiliation(s)
- Leonardo Campos
- Biomedical Engineering, Columbia University, New York, New York, USA
| | - Kelley M Swanberg
- Biomedical Engineering, Columbia University, New York, New York, USA
| | - Martin Gajdošík
- Biomedical Engineering, Columbia University, New York, New York, USA
| | - Karl Landheer
- Biomedical Engineering, Columbia University, New York, New York, USA
| | - Christoph Juchem
- Biomedical Engineering, Columbia University, New York, New York, USA
- Radiology, Columbia University, New York, New York, USA
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6
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Xu J, Vaeggemose M, Schulte RF, Yang B, Lee CY, Laustsen C, Magnotta VA. PyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data. Diagnostics (Basel) 2024; 14:2668. [PMID: 39682576 DOI: 10.3390/diagnostics14232668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2024] [Revised: 11/17/2024] [Accepted: 11/21/2024] [Indexed: 12/18/2024] Open
Abstract
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.
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Affiliation(s)
- Jia Xu
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
| | - Michael Vaeggemose
- GE HealthCare, 2605 Brondby, Denmark
- MR Research Centre, Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark
| | - Rolf F Schulte
- GE HealthCare, Oskar-Schlemmer-Str. 11, 80807 Munich, Germany
| | | | - Chu-Yu Lee
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
| | - Christoffer Laustsen
- MR Research Centre, Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark
| | - Vincent A Magnotta
- Department of Radiology, University of Iowa, Iowa City, IA 52242, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
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7
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Abdolizadeh A, Torres-Carmona E, Kambari Y, Amaev A, Song J, Ueno F, Koizumi T, Nakajima S, Agarwal SM, De Luca V, Gerretsen P, Graff-Guerrero A. Evaluation of the Glymphatic System in Schizophrenia Spectrum Disorder Using Proton Magnetic Resonance Spectroscopy Measurement of Brain Macromolecule and Diffusion Tensor Image Analysis Along the Perivascular Space Index. Schizophr Bull 2024; 50:1396-1410. [PMID: 38748498 PMCID: PMC11548937 DOI: 10.1093/schbul/sbae060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2024]
Abstract
BACKGROUND AND HYPOTHESIS The glymphatic system (GS), a brain waste clearance pathway, is disrupted in various neurodegenerative and vascular diseases. As schizophrenia shares clinical characteristics with these conditions, we hypothesized GS disruptions in patients with schizophrenia spectrum disorder (SCZ-SD), reflected in increased brain macromolecule (MM) and decreased diffusion-tensor-image-analysis along the perivascular space (DTI-ALPS) index. STUDY DESIGN Forty-seven healthy controls (HCs) and 103 patients with SCZ-SD were studied. Data included 135 proton magnetic resonance spectroscopy (1H-MRS) sets, 96 DTI sets, with 79 participants contributing both. MM levels were quantified in the dorsal-anterior cingulate cortex (dACC), dorsolateral prefrontal cortex, and dorsal caudate (point resolved spectroscopy, echo-time = 35ms). Diffusivities in the projection and association fibers near the lateral ventricle were measured to calculate DTI-ALPS indices. General linear models were performed, adjusting for age, sex, and smoking. Correlation analyses examined relationships with age, illness duration, and symptoms severity. STUDY RESULTS MM levels were not different between patients and HCs. However, left, right, and bilateral DTI-ALPS indices were lower in patients compared with HCs (P < .001). In HCs, age was positively correlated with dACC MM and negatively correlated with left, right, and bilateral DTI-ALPS indices (P < .001). In patients, illness duration was positively correlated with dACC MM and negatively correlated with the right DTI-ALPS index (P < .05). In the entire population, dACC MM and DTI-ALPS indices showed an inverse correlation (P < .01). CONCLUSIONS Our results suggest potential disruptions in the GS of patients with SCZ-SD. Improving brain's waste clearance may offer a potential therapeutic approach for patients with SCZ-SD.
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Affiliation(s)
- Ali Abdolizadeh
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Edgardo Torres-Carmona
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Yasaman Kambari
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Aron Amaev
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jianmeng Song
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Fumihiko Ueno
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Teruki Koizumi
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, National Hospital Organization Shimofusa Psychiatric Medical Center, Chiba, Japan
| | - Shinichiro Nakajima
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Sri Mahavir Agarwal
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Vincenzo De Luca
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Philip Gerretsen
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, CAMH, Toronto, ON, Canada
| | - Ariel Graff-Guerrero
- Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, CAMH, Toronto, ON, Canada
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Jeong E, Jang J, Kim JH, Kim H. Recurrent neural network-aided processing of incomplete free induction decays in 1H-MRS of the brain. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 368:107762. [PMID: 39299053 DOI: 10.1016/j.jmr.2024.107762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 09/01/2024] [Accepted: 09/02/2024] [Indexed: 09/22/2024]
Abstract
In the case of limited sampling windows or truncation of free induction decays (FIDs) for artifact removal in proton magnetic resonance spectroscopy (1H-MRS) and spectroscopic imaging (1H-MRSI), metabolite quantification needs to be performed on incomplete FIDs. Given that FIDs are naturally time-domain sequential data, we investigated the potential of recurrent neural network (RNN)-types of neural networks (NNs) in the processing of incomplete human brain FIDs with or without FID restoration prior to quantitative analysis at 3.0T. First, we employed an RNN encoder-decoder and developed it to restore incomplete FIDs (rRNN) with different amounts of sampled data. The quantification of metabolites from the rRNN-restored FIDs was achieved by using LCModel. Second, we modified the RNN encoder-decoder and developed it to convert incomplete brain FIDs into incomplete metabolite-only FIDs without restoration, followed by linear regression using a metabolite basis set for quantitative analysis (cRNN). In consideration of the practical benefit of the FID restoration with respect to pure zero-filling, development and analysis of the NNs were focused particularly on the incomplete FIDs with only the first 64 data points retained. All NNs were trained on simulated data and tested mainly on in vivo data acquired from healthy volunteers (n = 27). Strong correlations were obtained between the NN-derived and ground truth metabolite content (LCModel-derived content on fully sampled FIDs) for myo-inositol, total choline, and total creatine (normalized to total N-acetylaspartate) on the in vivo data using both rRNN (R = 0.83-0.94; p ≤ 0.05) and cRNN (R = 0.86-0.91; p ≤ 0.05). RNN-types of NNs have potential in the quantification of the major brain metabolites from the FIDs with substantially reduced sampled data points. For the metabolites with low to medium SNR, the performance of the NNs needs to be further improved, for which development of more elaborate and advanced simulation techniques would be of help, but remains challenging.
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Affiliation(s)
- Eunho Jeong
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Joon Jang
- Department of Biomedical Sciences, Seoul National University, Seoul, South Korea
| | - Ji-Hoon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, Seoul National University, Seoul, South Korea.
| | - Hyeonjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Medical Sciences, Seoul National University, Seoul, South Korea.
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9
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Pasmiño D, Slotboom J, Schweisthal B, Guevara P, Valenzuela W, Pino EJ. Comparison of baseline correction algorithms for in vivo 1H-MRS. NMR IN BIOMEDICINE 2024; 37:e5203. [PMID: 38953695 DOI: 10.1002/nbm.5203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 05/08/2024] [Accepted: 05/29/2024] [Indexed: 07/04/2024]
Abstract
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.
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Affiliation(s)
- Diego Pasmiño
- Electrical Engineering Department, Universidad de Concepcion, Concepcion, Chile
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging (SCAN), Neuroradiology, University Hospital Inselspital, Bern, Switzerland
| | - Brigitte Schweisthal
- Support Center for Advanced Neuroimaging (SCAN), Neuroradiology, University Hospital Inselspital, Bern, Switzerland
- Politehnica University Timișoara, Timișoara, Romania
| | - Pamela Guevara
- Electrical Engineering Department, Universidad de Concepcion, Concepcion, Chile
| | - Waldo Valenzuela
- Support Center for Advanced Neuroimaging (SCAN), Neuroradiology, University Hospital Inselspital, Bern, Switzerland
| | - Esteban J Pino
- Electrical Engineering Department, Universidad de Concepcion, Concepcion, Chile
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Davies-Jenkins CW, Zöllner HJ, Simicic D, Hui SCN, Song Y, Hupfeld KE, Prisciandaro JJ, Edden RA, Oeltzschner G. GABA-edited MEGA-PRESS at 3 T: Does a measured macromolecule background improve linear combination modeling? Magn Reson Med 2024; 92:1348-1362. [PMID: 38818623 PMCID: PMC11262975 DOI: 10.1002/mrm.30158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 06/01/2024]
Abstract
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.
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Affiliation(s)
- Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Dunja Simicic
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Steve C. N. Hui
- Developing Brain Institute, Children’s National Hospital, Washington, DC, USA
- Department of Radiology, The George Washington School of Medicine and Health Sciences, Washington D.C., USA
- Department of Pediatrics, The George Washington School of Medicine and Health Sciences, Washington D.C., USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kathleen E. Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - James J. Prisciandaro
- Department of Psychiatry and Behavioral Sciences, Addiction Sciences Division, Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - Richard A.E. Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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11
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Davies-Jenkins CW, Zöllner HJ, Simicic D, Alcicek S, Edden RA, Oeltzschner G. Data-driven determination of 1H-MRS basis set composition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.11.612503. [PMID: 39314430 PMCID: PMC11419043 DOI: 10.1101/2024.09.11.612503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
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.
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Affiliation(s)
- Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Dunja Simicic
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Seyma Alcicek
- Institute of Neuroradiology, University Hospital Frankfurt, Goethe University, Frankfurt/Main, Germany
- University Cancer Center Frankfurt (UCT), Frankfurt/Main, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt/Main, Germany
| | - Richard A.E. Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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12
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Murali-Manohar S, Gudmundson AT, Hupfeld KE, Zöllner HJ, Hui SC, Song Y, Simicic D, Davies-Jenkins CW, Gong T, Wang G, Oeltzschner G, Edden RA. Metabolite T 1 relaxation times decrease across the adult lifespan. NMR IN BIOMEDICINE 2024; 37:e5152. [PMID: 38565525 PMCID: PMC11303093 DOI: 10.1002/nbm.5152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 01/08/2024] [Accepted: 03/02/2024] [Indexed: 04/04/2024]
Abstract
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.
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Affiliation(s)
- Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
| | - Aaron T. Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
| | - Kathleen E. Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
| | - Steve C.N. Hui
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
| | - Dunja Simicic
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
| | - Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
| | - Tao Gong
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
- Departments of Radiology, Shandong Provincial Hospital, Shandong University, Shandong, China
| | - Guangbin Wang
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
- Departments of Radiology, Shandong Provincial Hospital, Shandong University, Shandong, China
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
| | - Richard A.E. Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, USA
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13
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Hui SCN, Murali-Manohar S, Zöllner HJ, Hupfeld KE, Davies-Jenkins CW, Gudmundson AT, Song Y, Yedavalli V, Wisnowski JL, Gagoski B, Oeltzschner G, Edden RAE. Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for advanced MRS. J Neurosci Methods 2024; 409:110206. [PMID: 38942238 DOI: 10.1016/j.jneumeth.2024.110206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/20/2024] [Accepted: 06/21/2024] [Indexed: 06/30/2024]
Abstract
BACKGROUND To examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study. METHODS ISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5±6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T2 correction based on the dual-TE water integrals were compared with: 1) T2 correction based on the default white matter and gray matter T2 reference values in Osprey and 2) shorter WM and GM T2 values from recent literature. RESULTS No significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T2 correction based on shorter T2 values showed better agreement to the dual-TE water integral ratio. CONCLUSIONS ISTHMUS facilitated data acquisition and post-processing and reduced operator workload to eliminate potential human error.
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Affiliation(s)
- Steve C N Hui
- Developing Brain Institute, Children's National Hospital, Washington, D.C., USA; Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, D.C., USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, D.C., USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kathleen E Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Christopher W Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Aaron T Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Vivek Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jessica L Wisnowski
- Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA; Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Borjan Gagoski
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Richard A E Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
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14
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Pasanta D, White DJ, He JL, Ford TC, Puts NA. GABA and glutamate response to social processing: a functional MRS feasibility study. NMR IN BIOMEDICINE 2024; 37:e5092. [PMID: 38154459 DOI: 10.1002/nbm.5092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 10/04/2023] [Accepted: 11/22/2023] [Indexed: 12/30/2023]
Abstract
Several studies have suggested that atypical social processing in neurodevelopmental conditions (e.g. autism) is associated with differences in excitation and inhibition, through changes in the levels of glutamate and gamma-aminobutyric acid (GABA). While associations between baseline metabolite levels and behaviours can be insightful, assessing the neurometabolic response of GABA and glutamate during social processing may explain altered neurochemical function in more depth. Thus far, there have been no attempts to determine whether changes in metabolite levels are detectable using functional MRS (fMRS) during social processing in a control population. We performed Mescher-Garwood point resolved spectroscopy edited fMRS to measure the dynamic response of GABA and glutamate in the superior temporal sulcus (STS) and visual cortex (V1) while viewing social stimuli, using a design that allows for analysis in both block and event-related approaches. Sliding window analyses were used to investigate GABA and glutamate dynamics at higher temporal resolution. The changes of GABA and glutamate levels with social stimulus were largely non-significant. A small decrease in GABA levels was observed during social stimulus presentation in V1, but no change was observed in STS. Conversely, non-social stimulus elicited changes in both GABA and glutamate levels in both regions. Our findings suggest that the current experimental design primarily captures effects of visual stimulation, not social processing. Here, we discuss the feasibility of using fMRS analysis approaches to assess changes in metabolite response.
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Affiliation(s)
- Duanghathai Pasanta
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand
| | - David J White
- Centre for Human Psychopharmacology & Swinburne Neuroimaging, School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
| | - Jason L He
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Talitha C Ford
- Centre for Human Psychopharmacology & Swinburne Neuroimaging, School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
- Cognitive Neuroscience Unit, Faculty of Health, Deakin University, Geelong, Australia
| | - Nicolaas A Puts
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
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15
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Genovese G, Terpstra M, Filip P, Mangia S, McCarten JR, Hemmy LS, Marjańska M. Age-related differences in macromolecular resonances observed in ultra-short-TE STEAM MR spectra at 7T. Magn Reson Med 2024; 92:4-14. [PMID: 38441257 PMCID: PMC11055657 DOI: 10.1002/mrm.30061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 03/16/2024]
Abstract
PURPOSE To understand how macromolecular content varies in the human brain with age in a large cohort of healthy subjects. METHODS In-vivo 1H-MR spectra were acquired using ultra-short TE STEAM at 7T in the posterior cingulate cortex. Macromolecular content was studied in 147 datasets from a cohort ranging in age from 19 to 89 y. Three fitting approaches were used to evaluate the macromolecular content: (1) a macromolecular resonances model developed for this study; (2) LCModel-simulated macromolecules; and (3) a combination of measured and LCModel-simulated macromolecules. The effect of age on the macromolecular content was investigated by considering age both as a continuous variable (i.e., linear regressions) and as a categorical variable (i.e., multiple comparisons among sub-groups obtained by stratifying data according to age by decade). RESULTS While weak age-related effects were observed for macromolecular peaks at ˜0.9 (MM09), ˜1.2 (MM12), and ˜1.4 (MM14) ppm, moderate to strong effects were observed for peaks at ˜1.7 (MM17), and ˜2.0 (MM20) ppm. Significantly higher MM17 and MM20 content started from 30 to 40 y of age, while for MM09, MM12, and MM14, significantly higher content started from 60 to 70 y of age. CONCLUSIONS Our findings provide insights into age-related differences in macromolecular contents and strengthen the necessity of using age-matched measured macromolecules during quantification.
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Affiliation(s)
- Guglielmo Genovese
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Melissa Terpstra
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Pavel Filip
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
- Department of Neurology, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Silvia Mangia
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - J Riley McCarten
- Geriatric Research, Education and Clinical Center, Veterans Affairs Health Care System, Minneapolis, Minnesota, USA
- Department of Neurology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Laura S Hemmy
- Geriatric Research, Education and Clinical Center, Veterans Affairs Health Care System, Minneapolis, Minnesota, USA
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
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16
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Ehrhardt SE, Wards Y, Rideaux R, Marjańska M, Jin J, Cloos MA, Deelchand DK, Zöllner HJ, Saleh MG, Hui SCN, Ali T, Shaw TB, Barth M, Mattingley JB, Filmer HL, Dux PE. Neurochemical Predictors of Generalized Learning Induced by Brain Stimulation and Training. J Neurosci 2024; 44:e1676232024. [PMID: 38531634 PMCID: PMC11112648 DOI: 10.1523/jneurosci.1676-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/28/2024] Open
Abstract
Methods of cognitive enhancement for humans are most impactful when they generalize across tasks. However, the extent to which such "transfer" is possible via interventions is widely debated. In addition, the contribution of excitatory and inhibitory processes to such transfer is unknown. Here, in a large-scale neuroimaging individual differences study with humans (both sexes), we paired multitasking training and noninvasive brain stimulation (transcranial direct current stimulation, tDCS) over multiple days and assessed performance across a range of paradigms. In addition, we varied tDCS dosage (1.0 and 2.0 mA), electrode montage (left or right prefrontal regions), and training task (multitasking vs a control task) and assessed GABA and glutamate concentrations via ultrahigh field 7T magnetic resonance spectroscopy. Generalized benefits were observed in spatial attention, indexed by visual search performance, when multitasking training was combined with 1.0 mA stimulation targeting either the left or right prefrontal cortex (PFC). This transfer effect persisted for ∼30 d post intervention. Critically, the transferred benefits associated with right prefrontal tDCS were predicted by pretraining concentrations of glutamate in the PFC. Thus, the effects of this combined stimulation and training protocol appear to be linked predominantly to excitatory brain processes.
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Affiliation(s)
- Shane E Ehrhardt
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Yohan Wards
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Reuben Rideaux
- Queensland Brain Institute, The University of Queensland, St Lucia, Queensland 4072, Australia
- School of Psychology, The University of Sydney, Sydney, New South Wales 2050, Australia
| | - Małgorzata Marjańska
- Department of Radiology, Centre for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota 55455
| | - Jin Jin
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland 4072, Australia
- Siemens Healthcare Pty Ltd., Brisbane, Queensland 4006, Australia
| | - Martijn A Cloos
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Dinesh K Deelchand
- Department of Radiology, Centre for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota 55455
| | - Helge J Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21287
| | - Muhammad G Saleh
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201
| | - Steve C N Hui
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21287
| | - Tonima Ali
- School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales 2050, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2050, Australia
| | - Thomas B Shaw
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland 4072, Australia
- School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Markus Barth
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland 4072, Australia
- School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Jason B Mattingley
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
- Queensland Brain Institute, The University of Queensland, St Lucia, Queensland 4072, Australia
- Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario M5G 1M1, Canada
| | - Hannah L Filmer
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Paul E Dux
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
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17
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Hui SC, Murali-Manohar S, Zöllner HJ, Hupfeld KE, Davies-Jenkins CW, Gudmundson AT, Song Y, Yedavalli V, Wisnowski JL, Gagoski B, Oeltzschner G, Edden RA. Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for Advanced MRS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580516. [PMID: 38659947 PMCID: PMC11042202 DOI: 10.1101/2024.02.15.580516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Background To examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study. Methods ISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5±6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T2 correction based on the dual-TE water integrals were compared with: 1) T2 correction based the default white matter and gray matter T2 reference values in Osprey; 2) shorter WM and GM T2 values from recent literature; and 3) reduced CSF fractions. Results No significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T2 correction based on shorter T2 values showed better agreement to the dual-TE water integral ratio. Conclusions ISTHMUS facilitated and standardized acquisition and post-processing and reduced operator workload to eliminate potential human error.
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Affiliation(s)
- Steve C.N. Hui
- Developing Brain Institute, Children’s National Hospital, Washington, D.C. USA
- Departments of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, D.C. USA
- Departments of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, D.C. USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kathleen E. Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Aaron T. Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Vivek Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jessica L Wisnowski
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, USA
- Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - Borjan Gagoski
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Richard A.E. Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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18
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Nichols SJ, Yanes JA, Reid MA, Robinson JL. 7 T characterization of excitatory and inhibitory systems of acute pain in healthy female participants. NMR IN BIOMEDICINE 2024; 37:e5088. [PMID: 38140895 DOI: 10.1002/nbm.5088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023]
Abstract
Current understanding of the physiological underpinnings of normative pain processing is incomplete. Enhanced knowledge of these systems is necessary to advance our understanding of pain processes as well as to develop effective therapeutic interventions. Previous neuroimaging research suggests a network of interrelated brain regions that seem to be implicated in the processing and experience of pain. Among these, the dorsal anterior cingulate cortex (dACC) plays an important role in the affective aspects of pain signals. The current study leveraged functional MRS to investigate the underlying dynamic shifts in the neurometabolic signature of the human dACC at rest and during acute pain. Results provide support for increased glutamate levels following acute pain administration. Specifically, a 4.6% increase in glutamate was observed during moderate pressure pain compared with baseline. Exploratory analysis also revealed meaningful changes in dACC gamma aminobutyric acid in response to pain stimulation. These data contribute toward the characterization of neurometabolic shifts, which lend insight into the role of the dACC in the pain network. Further research in this area with larger sample sizes could contribute to the development of novel therapeutics or other advances in pain-related outcomes.
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Affiliation(s)
- Steven J Nichols
- Department of Psychological Sciences, Auburn University, Auburn, Alabama, USA
| | - Julio A Yanes
- Exponent Inc., Washington, District of Columbia, USA
| | - Meredith A Reid
- Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA
| | - Jennifer L Robinson
- Department of Psychological Sciences, Auburn University, Auburn, Alabama, USA
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19
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Chan KL, Panatpur A, Messahel S, Dahshi H, Johnson T, Henning A, Ren J, Minassian BA. 1H and 31P magnetic resonance spectroscopy reveals potential pathogenic and biomarker metabolite alterations in Lafora disease. Brain Commun 2024; 6:fcae104. [PMID: 38585668 PMCID: PMC10998360 DOI: 10.1093/braincomms/fcae104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/19/2024] [Accepted: 03/22/2024] [Indexed: 04/09/2024] Open
Abstract
Lafora disease is a fatal teenage-onset progressive myoclonus epilepsy and neurodegenerative disease associated with polyglucosan bodies. Polyglucosans are long-branched and as a result precipitation- and aggregation-prone glycogen. In mouse models, downregulation of glycogen synthase, the enzyme that elongates glycogen branches, prevents polyglucosan formation and rescues Lafora disease. Mouse work, however, has not yet revealed the mechanisms of polyglucosan generation, and few in vivo human studies have been performed. Here, non-invasive in vivo magnetic resonance spectroscopy (1H and 31P) was applied to test scan feasibility and assess neurotransmitter balance and energy metabolism in Lafora disease towards a better understanding of pathogenesis. Macromolecule-suppressed gamma-aminobutyric acid (GABA)-edited 1H magnetic resonance spectroscopy and 31P magnetic resonance spectroscopy at 3 and 7 tesla, respectively, were performed in 4 Lafora disease patients and a total of 21 healthy controls (12 for the 1H magnetic resonance spectroscopy and 9 for the 31PMRS). Spectra were processed using in-house software and fit to extract metabolite concentrations. From the 1H spectra, we found 33% lower GABA concentrations (P = 0.013), 34% higher glutamate + glutamine concentrations (P = 0.011) and 24% lower N-acetylaspartate concentrations (P = 0.0043) in Lafora disease patients compared with controls. From the 31P spectra, we found 34% higher phosphoethanolamine concentrations (P = 0.016), 23% lower nicotinamide adenine dinucleotide concentrations (P = 0.003), 50% higher uridine diphosphate glucose concentrations (P = 0.004) and 225% higher glucose 6-phosphate concentrations in Lafora disease patients versus controls (P = 0.004). Uridine diphosphate glucose is the substrate of glycogen synthase, and glucose 6-phosphate is its extremely potent allosteric activator. The observed elevated uridine diphosphate glucose and glucose 6-phosphate levels are expected to hyperactivate glycogen synthase and may underlie the generation of polyglucosans in Lafora disease. The increased glutamate + glutamine and reduced GABA indicate altered neurotransmission and energy metabolism, which may contribute to the disease's intractable epilepsy. These results suggest a possible basis of polyglucosan formation and potential contributions to the epilepsy of Lafora disease. If confirmed in larger human and animal model studies, measurements of the dysregulated metabolites by magnetic resonance spectroscopy could be developed into non-invasive biomarkers for clinical trials.
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Affiliation(s)
- Kimberly L Chan
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Aparna Panatpur
- Department of Pediatrics, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX 77030, USA
| | - Souad Messahel
- Division of Neurology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Hamza Dahshi
- Division of Neurology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Talon Johnson
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Anke Henning
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jimin Ren
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Berge A Minassian
- Division of Neurology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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20
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Mosso J, Briand G, Pierzchala K, Simicic D, Sierra A, Abdollahzadeh A, Jelescu IO, Cudalbu C. Diffusion of brain metabolites highlights altered brain microstructure in type C hepatic encephalopathy: a 9.4 T preliminary study. Front Neurosci 2024; 18:1344076. [PMID: 38572151 PMCID: PMC10987698 DOI: 10.3389/fnins.2024.1344076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 02/19/2024] [Indexed: 04/05/2024] Open
Abstract
Introduction Type C hepatic encephalopathy (HE) is a decompensating event of chronic liver disease leading to severe motor and cognitive impairment. The progression of type C HE is associated with changes in brain metabolite concentrations measured by 1H magnetic resonance spectroscopy (MRS), most noticeably a strong increase in glutamine to detoxify brain ammonia. In addition, alterations of brain cellular architecture have been measured ex vivo by histology in a rat model of type C HE. The aim of this study was to assess the potential of diffusion-weighted MRS (dMRS) for probing these cellular shape alterations in vivo by monitoring the diffusion properties of the major brain metabolites. Methods The bile duct-ligated (BDL) rat model of type C HE was used. Five animals were scanned before surgery and 6- to 7-week post-BDL surgery, with each animal being used as its own control. 1H-MRS was performed in the hippocampus (SPECIAL, TE = 2.8 ms) and dMRS in a voxel encompassing the entire brain (DW-STEAM, TE = 15 ms, diffusion time = 120 ms, maximum b-value = 25 ms/μm2) on a 9.4 T scanner. The in vivo MRS acquisitions were further validated with histological measures (immunohistochemistry, Golgi-Cox, electron microscopy). Results The characteristic 1H-MRS pattern of type C HE, i.e., a gradual increase of brain glutamine and a decrease of the main organic osmolytes, was observed in the hippocampus of BDL rats. Overall increased metabolite diffusivities (apparent diffusion coefficient and intra-stick diffusivity-Callaghan's model, significant for glutamine, myo-inositol, and taurine) and decreased kurtosis coefficients were observed in BDL rats compared to control, highlighting the presence of osmotic stress and possibly of astrocytic and neuronal alterations. These results were consistent with the microstructure depicted by histology and represented by a decline in dendritic spines density in neurons, a shortening and decreased number of astrocytic processes, and extracellular edema. Discussion dMRS enables non-invasive and longitudinal monitoring of the diffusion behavior of brain metabolites, reflecting in the present study the globally altered brain microstructure in BDL rats, as confirmed ex vivo by histology. These findings give new insights into metabolic and microstructural abnormalities associated with high brain glutamine and its consequences in type C HE.
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Affiliation(s)
- Jessie Mosso
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Guillaume Briand
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Katarzyna Pierzchala
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Dunja Simicic
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alejandra Sierra
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Ali Abdollahzadeh
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Ileana O. Jelescu
- Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
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21
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Ligneul C, Najac C, Döring A, Beaulieu C, Branzoli F, Clarke WT, Cudalbu C, Genovese G, Jbabdi S, Jelescu I, Karampinos D, Kreis R, Lundell H, Marjańska M, Möller HE, Mosso J, Mougel E, Posse S, Ruschke S, Simsek K, Szczepankiewicz F, Tal A, Tax C, Oeltzschner G, Palombo M, Ronen I, Valette J. Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling. Magn Reson Med 2024; 91:860-885. [PMID: 37946584 DOI: 10.1002/mrm.29877] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/18/2023] [Accepted: 09/08/2023] [Indexed: 11/12/2023]
Abstract
Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.
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Affiliation(s)
- Clémence Ligneul
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chloé Najac
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Christian Beaulieu
- Departments of Biomedical Engineering and Radiology, University of Alberta, Alberta, Edmonton, Canada
| | - Francesca Branzoli
- Paris Brain Institute-ICM, Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Guglielmo Genovese
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ileana Jelescu
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dimitrios Karampinos
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Roland Kreis
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager anf Hvidovre, Hvidovre, Denmark
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Harald E Möller
- NMR Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jessie Mosso
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- LIFMET, EPFL, Lausanne, Switzerland
| | - Eloïse Mougel
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| | - Stefan Posse
- Department of Neurology, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
- Department of Physics and Astronomy, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
| | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Kadir Simsek
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | | | - Assaf Tal
- Department of Chemical and Biological Physics, The Weizmann Institute of Science, Rehovot, Israel
| | - Chantal Tax
- University Medical Center Utrecht, Utrecht, The Netherlands
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Maryland, Baltimore, USA
- F. M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Maryland, Baltimore, USA
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Itamar Ronen
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, UK
| | - Julien Valette
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
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22
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Xiao Y, Lanz B, Lim S, Tkáč I, Xin L. Improved reproducibility of γ-aminobutyric acid measurement from short-echo-time proton MR spectroscopy by linewidth-matched basis sets in LCModel. NMR IN BIOMEDICINE 2024; 37:e5056. [PMID: 37839823 PMCID: PMC11580110 DOI: 10.1002/nbm.5056] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 10/17/2023]
Abstract
γ-Aminobutyric acid (GABA), as the primary inhibitory neurotransmitter, is extremely important for maintaining healthy brain function, and deviations from GABA homeostasis are related to various brain diseases. Short-echo-time (short-TE) proton MR spectroscopy (1 H-MRS) has been employed to measure GABA concentration from various human brain regions at high magnetic fields. The aim of this study was to investigate the effect of spectral linewidth on GABA quantification and explore the application of an optimized basis-set preparation approach using a spectral-linewidth-matched (LM) basis set in LCModel to improve the reproducibility of GABA quantification from short-TE 1 H-MRS. In contrast to the fixed-linewidth basis-set approach, the LM basis-set preparation approach, where all metabolite basis spectra were simulated with a linewidth 4 Hz narrower than that of water, showed a smaller standard deviation of estimated GABA concentration from synthetic spectra with varying linewidths and lineshapes. The test-retest reproducibility was assessed by the mean within-subject coefficient of variation, which improved from 19.2% to 12.0% in the thalamus, from 27.9% to 14.9% in the motor cortex, and from 9.7% to 2.8% in the medial prefrontal cortex using LM basis sets at 7 T. We conclude that spectral linewidth has a large effect on GABA quantification from short-TE 1 H-MRS data and that using LM basis sets in LCModel can improve the reproducibility of GABA quantification.
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Affiliation(s)
- Ying Xiao
- Center for Biomedical Imaging (CIBM)LausanneSwitzerland
- Animal Imaging and TechnologyEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Bernard Lanz
- Center for Biomedical Imaging (CIBM)LausanneSwitzerland
- Animal Imaging and TechnologyEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Song‐I Lim
- Center for Biomedical Imaging (CIBM)LausanneSwitzerland
- Animal Imaging and TechnologyEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Ivan Tkáč
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Lijing Xin
- Center for Biomedical Imaging (CIBM)LausanneSwitzerland
- Animal Imaging and TechnologyEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
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23
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Chabbey I, Cudalbu C, Barras E, Hanquinet S, Maréchal B, Rougemont A, Wacker J, Zangas‐Gheri F, McLin VA. Neurometabolism and brain morphometry in an adolescent female with an extra-hepatic congenital portosystemic shunt. JPGN REPORTS 2024; 5:35-42. [PMID: 38545268 PMCID: PMC10964341 DOI: 10.1002/jpr3.12035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 11/16/2023] [Accepted: 12/05/2023] [Indexed: 11/10/2024]
Abstract
Background Chronic hepatic encephalopathy (CHE) has been reported both in patients with congenital porto-systemic shunts (CPSS) and chronic liver disease. CHE is difficult to recognize in children as there is no clear definition and its manifestations are highly variable. CHE is associated with variations in brain volumes and metabolites that have already been demonstrated using 1.5-3T MRI systems. However, the in-depth study of brain metabolism requires the high spectral resolution of high magnetic fields. Objectives and Methods We analyzed the neurometabolic profile, brain volumes and T1 relaxation times of a child with a CPSS using high field proton magnetic resonance spectroscopy (1H MRS, 7T) combined with MRI and compared it to an age-matched control group. We also evaluated the impact of shunt closure on neurocognitive symptoms using adapted neuropsychological tests. Results 7T MRS revealed a significant increase in glutamine compared to controls, a decrease in brain osmolytes, and a slight elevation in NAA concentrations. 7T MRI scans showed morphological abnormalities but no changes in the signal intensity of the globus pallidus. Neurocognitive testing revealed attention deficit disorder, language difficulties, and mild intellectual disability. Most of these areas improved after shunt closure. Conclusions In this paediatric case of type B HE with normal fasting ammonia, neurometabolic profile was compatible with what has been previously shown in chronic liver disease, while also demonstrating an isolated glutamine peak. In addition, neurocognitive function partially improved after shunt closure, arguing strongly for shunt closure in both presymptomatic and symptomatic patients.
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Affiliation(s)
- Isaline Chabbey
- Department of Pediatrics, Gynecology and Obstetrics, Swiss Pediatric Liver Center, Division of Pediatric SurgeryUniversity of GenevaGenevaSwitzerland
| | - Cristina Cudalbu
- CIBM Center for Biomedical ImagingVaudSwitzerland
- Animal Imaging and TechnologyEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Eugénie Barras
- Diagnostic Department, Pediatric Radiology Unit, Radiology DivisionGeneva University HospitalsGenevaSwitzerland
| | - Sylviane Hanquinet
- Diagnostic Department, Pediatric Radiology Unit, Radiology DivisionGeneva University HospitalsGenevaSwitzerland
| | - Bénédicte Maréchal
- Advanced Clinical Imaging TechnologySiemens Healthineers International AGLausanneSwitzerland
- Department of Radiology, Lausanne University Hospital (CHUV)University of LausanneVaudSwitzerland
- LTSS, Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Anne‐Laure Rougemont
- Division of Clinical Pathology, Swiss Pediatric Liver CenterGeneva University HospitalsGenevaSwitzerland
| | - Julie Wacker
- Department of Pediatrics, Gynecology and Obstetrics, Pediatric Cardiology UnitUniversity of Geneva, Geneva, SwitzerlandGenevaSwitzerland
| | | | - Valérie A. McLin
- Department of Pediatrics, Gynecology and Obstetrics, Swiss Pediatric Liver Center, Pediatric Gastroenterology, Hepatology and Nutrition UnitUniversity of GenevaGenevaSwitzerland
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Sheikh-Bahaei N, Chen M, Pappas I. Magnetic Resonance Spectroscopy (MRS) in Alzheimer's Disease. Methods Mol Biol 2024; 2785:115-142. [PMID: 38427192 DOI: 10.1007/978-1-0716-3774-6_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
MRS is a noninvasive technique to measure different metabolites in the brain. Changes in the levels of certain metabolites can be used as surrogate markers for Alzheimer's disease. They can potentially be used for diagnosis, prediction of prognosis, or even assessing response to treatment.There are different techniques for MRS acquisitions including STimulated Echo Acquisition Mode (STEAM) and Point Resolved Spectroscopy (PRESS). In terms of localization, single or multi-voxel methods can be used. Based on current data: 1. NAA, marker of neuronal integrity and viability, reduces in AD with longitudinal changes over the time as the disease progresses. There are data claiming that reduction of NAA is associated with tau accumulation, early neurodegenerative processes, and cognitive decline. Therefore, it can be used as a stage biomarker for AD to assess the severity of the disease. With advancement of disease modifying therapies, there is a potential role for NAA in the future to be used as a marker of response to treatment. 2. mI, marker of glial cell proliferation and activation, is associated with AB pathology and has early changes in the course of the disease. The NAA/mI ratio can be predictive of AD development with high specificity and can be utilized in the clinical setting to stratify cases for further evaluation with PET for potential treatments. 3. The changes in the level of other metabolites such as Chol, Glu, Gln, and GABA are controversial because of the lack of standardization of MRS techniques, current technical limitations, and possible region specific changes. 4. Ultrahigh field MRS and more advanced techniques can overcome many of these limitations and enable us to measure more metabolites with higher accuracy. 5. Standardization of MRS techniques, validation of metabolites' changes against PET using PET-guided technique, and longitudinal follow-ups to investigate the temporal changes of the metabolites in relation to other biomarkers and cognition will be crucial to confirm the utility of MRS as a potential noninvasive biomarker for AD.
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Affiliation(s)
- Nasim Sheikh-Bahaei
- Department of Radiology, Keck School of Medicine of USC, Los Angeles, CA, USA.
| | - Michelle Chen
- Keck School of Medicine of USC, USC, Los Angeles, CA, USA
| | - Ioannis Pappas
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, USC, Los Angeles, CA, USA
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25
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Rizzo R, Kreis R. Multi-echo single-shot spectroscopy combined with simultaneous 2D model fitting for fast and accurate measurement of metabolite-specific concentrations and T 2 relaxation times. NMR IN BIOMEDICINE 2023; 36:e5016. [PMID: 37587062 DOI: 10.1002/nbm.5016] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 08/18/2023]
Abstract
The purpose of the current study was to develop a novel single-voxel MR spectroscopy acquisition scheme to simultaneously determine metabolite-specific concentrations and transverse relaxation times within realistic clinical scan times. Partly truncated multi-TE data are acquired as an echo train in a single acquisition (multi-echo single-shot [MESS]). A 2D multiparametric model fitting approach combines truncated, low-resolved short TE data with fully sampled, highly resolved, longer TE data to yield concentration and T2 estimates for major brain metabolites simultaneously. Cramer-Rao lower bounds (CRLB) are used as a measure of performance. The novel scheme was compared with traditional multi-echo multi-shot methods. In silico, in vitro, and in vivo experiments support the findings. MESS schemes, requiring only 2 min 12 s for the acquisition of three echo times, provide valid concentration and relaxation estimates for multiple metabolites and outperform traditional methods for simultaneous determinations of metabolite-specific T2 s and concentrations, with improvements ranging from 5% to 30% for T2 s and from 10% to 50% for concentrations. However, substantial unsuppressed residual water signals may hamper the method's reproducibility, as observed in an initial experiment setup that prioritizes short TEs with severely truncated acquisition for the benefit of signal-to-noise ratio (SNR). Nevertheless, CRLB have been confirmed to be well suited as design criteria, and within-session repeatability approaches CRLB when residual water is removed in postprocessing by exploiting longer and less truncated data recordings. MESS MRS combined with 2D model fitting promises comparable accuracy, increased precision, or inversely shorter experimental times compared with traditional approaches. However, the optimal design must be investigated as a trade-off between SNR, the truncation factor, and TE batch selections, all of which influence the robustness of estimations.
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Affiliation(s)
- Rudy Rizzo
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Department for Biomedical Research, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine (sitem-insel), Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Roland Kreis
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Department for Biomedical Research, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine (sitem-insel), Bern, Switzerland
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26
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Dell'Orco A, Riemann LT, Ellison SLR, Aydin S, Göschel L, Tietze A, Scheel M, Fillmer A. Macromolecule modelling for improved metabolite quantification using short echo time brain 1 H MRS at 3 T and 7 T: The PRaMM Model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.16.567383. [PMID: 38014000 PMCID: PMC10680753 DOI: 10.1101/2023.11.16.567383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Purpose To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1 H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Methods 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 to those other methods was investigated. Results 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. While 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 percent coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out. Conclusion 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.
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Starčuková J, Stefan D, Graveron-Demilly D. Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm. NMR IN BIOMEDICINE 2023; 36:e5008. [PMID: 37539457 DOI: 10.1002/nbm.5008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023]
Abstract
Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST-MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST-MM and three QUEST-based strategies for quantifying short echo time signals are compared in terms of bias-variance trade-off and Cramér-Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST-MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST-MM is shown for 1 H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST-MM is implemented in jMRUI and will be available for public use from version 7.1.
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Affiliation(s)
- Jana Starčuková
- Institute of Scientific Instruments of the CAS, Brno, Czech Republic
| | | | - Danielle Graveron-Demilly
- D1Si, Saint André de Corcy, France
- CREATIS, CNRS UMR 5220, INSERM U1294, Université Claude Bernard Lyon 1, Villeurbanne, France
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Hatay GH, Ozturk-Isik E. Optimized multi-voxel TE-averaged PRESS for glutamate detection in the human brain at 3T. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 356:107574. [PMID: 37922677 DOI: 10.1016/j.jmr.2023.107574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 10/20/2023] [Accepted: 10/20/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE To optimize possible combinations of echo times (TE) for multi-voxel TE-averaged Point RESolved Spectroscopy (PRESS) while reducing the total number of TEs required to separate glutamate (Glu) and glutamine (Gln) within a clinically feasible scan time. METHODS General Approach to Magnetic resonance Mathematical Analysis (GAMMA) was used to implement 2D J-resolved PRESS technique, and the spectra of 14 individual brain metabolites were simulated at 64 different TEs. Monte Carlo simulations were used for selecting the best TE combinations to separate Glu and Gln using TE-averaged PRESS with a total number of two, three, four and five TEs. Single-voxel 1H-MRS data were acquired using 64 different TEs from a healthy volunteer on a clinical 3T MR scanner to validate the echo time combinations selected with simulations. Additionally, 2D 1H-MRSI data of eight healthy volunteers were acquired on a clinical 3T MR scanner using four different TEs that were determined by Monte Carlo simulations. Optimized TE-averaged PRESS spectra were created by averaging the spectra acquired at selected TEs. LCModel was used for spectral quantification. A Wilcoxon signed-rank test was used to detect statistically significant differences in Glu/Gln ratios between 35 ms PRESS and optimized TE-averaged PRESS data. RESULTS Glu could be clearly separated from Gln at 2.35 ppm, using optimized TE-averaged PRESS with only four TEs (35, 37, 40, and 42 ms) that were selected through Monte Carlo simulations. Glu/Gln ratios were significantly higher in the optimized TE-averaged PRESS data of healthy volunteers than in the 35 ms PRESS data (P = 0.008). CONCLUSION Optimized multi-voxel TE-averaged PRESS enabled faster and unobstructed quantification of Glu at multiple voxels in the human brain in vivo at 3T.
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Affiliation(s)
- Gokce Hale Hatay
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey.
| | - Esin Ozturk-Isik
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey.
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Filmer HL, Loughnan K, Seeto JX, Ballard T, Ehrhardt SE, Shaw TB, Wards Y, Rideaux R, Leow LA, Sewell DK, Dux PE. Individual Differences in Decision Strategy Relate to Neurochemical Excitability and Cortical Thickness. J Neurosci 2023; 43:7006-7015. [PMID: 37657932 PMCID: PMC10586534 DOI: 10.1523/jneurosci.1086-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 08/22/2023] [Accepted: 08/26/2023] [Indexed: 09/03/2023] Open
Abstract
The speed-accuracy trade-off (SAT), whereby faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during the performance of almost all daily tasks. To date, computational modeling has implicated the latent decision variable of response caution (thresholds), the amount of evidence required for a decision to be made, in the SAT. Previous imaging has associated frontal regions, notably the left prefrontal cortex and the presupplementary motor area (pre-SMA), with the setting of such caution levels. In addition, causal brain stimulation studies, using transcranial direct current stimulation (tDCS), have indicated that while both of these regions are involved in the SAT, their role appears to be dissociable. tDCS efficacy to impact decision-making processes has previously been linked with neurochemical concentrations and cortical thickness of stimulated regions. However, to date, it is unknown whether these neurophysiological measures predict individual differences in the SAT, and brain stimulation effects on the SAT. Using ultra-high field (7T) imaging, here we report that instruction-based adjustments in caution are associated with both neurochemical excitability (the balance between GABA+ and glutamate) and cortical thickness across a range of frontal regions in both sexes. In addition, cortical thickness, but not neurochemical concentrations, was associated with the efficacy of left prefrontal and superior medial frontal cortex (SMFC) stimulation to modulate performance. Overall, our findings elucidate key neurophysiological predictors, frontal neural excitation, of individual differences in latent psychological processes and the efficacy of stimulation to modulate these.SIGNIFICANCE STATEMENT The speed-accuracy trade-off (SAT), faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during most daily tasks. The SAT is often investigated by explicitly instructing participants to prioritize speed or accuracy when responding to stimuli. Using ultra-high field (7T) magnetic resonance imaging (MRI), we found that individual differences in the extent to which participants adjust their decision strategies with instruction related to neurochemical excitability (ratio of GABA+ to glutamate) and cortical thickness in the frontal cortex. Moreover, brain stimulation to the left prefrontal cortex and the superior medial frontal cortex (SMFC) modulated performance, with the efficacy specifically related to cortical thickness. This work sheds new light on the neurophysiological basis of decision strategies and brain stimulation.
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Affiliation(s)
- Hannah L Filmer
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Kathleen Loughnan
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Jennifer X Seeto
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Timothy Ballard
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Shane E Ehrhardt
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Thomas B Shaw
- School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Queensland 4072, Australia
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Yohan Wards
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Reuben Rideaux
- Queensland Brain Institute, The University of Queensland, St Lucia, Queensland 4072, Australia
- School of Psychology, The University of Sydney, Camperdown, New South Wales 2050, Australia
| | - Li-Ann Leow
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - David K Sewell
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Paul E Dux
- School of Psychology, The University of Queensland, St Lucia, Queensland 4072, Australia
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30
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Qu B, Li X, Xiao M, Chen R, Tan H, Sun H, Li R, Xu J, Dong J, Zheng G, Ai S, Qu X. Comparative study of bilateral putamen for patients with severe Parkinson's disease detected by 1H magnetic resonance spectroscopy. Quant Imaging Med Surg 2023; 13:6646-6655. [PMID: 37869290 PMCID: PMC10585560 DOI: 10.21037/qims-23-231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 07/28/2023] [Indexed: 10/24/2023]
Abstract
Background The diagnosis of Parkinson's disease (PD) is challenging because the clinical symptoms overlap with other neurodegenerative diseases. The discovery of reliable biomarkers is highly expected to facilitate clinical diagnosis. Through the analysis of the 1H magnetic resonance spectroscopy (1H-MRS) in the putamen, the purpose of the study was to discuss the possibility of the difference in metabolite concentrations between the left and right putamen as biomarkers for patients with severe PD. Methods We collected 1H-MRS of unilateral or bilateral putamen from 41 patients and used the independent sample t-test and paired t-test to analyze 4 metabolite concentrations, including choline (Cho), total N-acetyl aspartate (tNAA), total creatine (tCr), and combined glutamate and glutamine; Bonferroni correction was used to correct P values for multiple comparisons. We designed 4 controlled experiments as follows: (I) PD patients versus healthy controls (HCs) in the left putamen; (II) PD patients versus HCs in the right putamen; (III) the left putamen versus the right putamen for PD patients; and (IV) the left putamen versus the right putamen for HCs. Results No statistically significant differences (P>0.05) were detected among 4 metabolites in the ipsilateral and bilateral putamen for the PD and HCs groups, except for tCr in the left putamen (PD 6.426±0.557, HCs 6.026±0.460, P=0.046) for ipsilateral comparisons. Conclusions In the bilateral putamen of severe PD patients, there was no statistically significant difference in the 4 metabolites. The difference (P<0.05) in tCr in the left putamen might be a potential biomarker to distinguish HCs from severe patients in clinic. This might provide a reference for the clinical diagnosis and acquisition strategy of 1H-MRS in severe PD.
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Affiliation(s)
- Biao Qu
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, China
| | - Xiaoyuan Li
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Min Xiao
- Institute of Artificial Intelligence, Xiamen University, Xiamen, China
| | - Runhan Chen
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Hejuan Tan
- Institute of Artificial Intelligence, Xiamen University, Xiamen, China
| | - Hongwei Sun
- United Imaging Research Institute of Intelligent Imaging, Beijing, China
| | - Rushuai Li
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jingjing Xu
- Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen, China
| | - Jiyang Dong
- Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen, China
| | - Gaofeng Zheng
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, China
| | - Shuyue Ai
- Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaobo Qu
- Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen, China
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Gudmundson AT, Davies-Jenkins CW, Özdemir İ, Murali-Manohar S, Zöllner HJ, Song Y, Hupfeld KE, Schnitzler A, Oeltzschner G, Stark CEL, Edden RAE. Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539813. [PMID: 37215030 PMCID: PMC10197548 DOI: 10.1101/2023.05.08.539813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.
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Affiliation(s)
- Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Christopher W Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - İpek Özdemir
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Kathleen E Hupfeld
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Craig E L Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
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Okada T, Kuribayashi H, Urushibata Y, Fujimoto K, Akasaka T, Seethamraju RT, Ahn S, Isa T. GABA, glutamate and excitatory-inhibitory ratios measured using short-TE STEAM MRS at 7-Tesla: Effects of macromolecule basis sets and baseline parameters. Heliyon 2023; 9:e18357. [PMID: 37539101 PMCID: PMC10393741 DOI: 10.1016/j.heliyon.2023.e18357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 08/05/2023] Open
Abstract
Rationale and objectives Macromolecules (MMs) affect the precision and accuracy of neurochemical quantification in magnetic resonance spectroscopy. A measured MM basis is increasingly used in LCModel analysis combined with a spline baseline, whose stiffness is controlled by a parameter named DKNTMN. The effects of measured MM basis and DKNTMN were investigated. Materials and methods Twenty-six healthy subjects were prospectively enrolled and scanned twice using a short echo-time Stimulated Echo Acquisition Mode (STEAM) at 7-T. Using LCModel, analyses were conducted using the simulated MM basis (MMsim) with DKNTMN 0.15 and an MM basis measured inhouse (MMmeas) with DKNTMN of 0.15, 0.30, 0.60 and 1.00. Cramér-Rao lower bound (CRLB) and the concentrations of gamma-aminobutyric acid (GABA), glutamate and excitatory-inhibitory ratio (EIR), in addition to MMs were statistically analyzed. Measurement stability was evaluated using coefficient of variation (CV). Results CRLBs of GABA were significantly lower when using MMsim than MMmeas; those of glutamate were 2-3. GABA concentrations were significantly higher in the analysis using MMsim than MMmeas where concentrations were significantly higher with DKNTMN of 0.15 or 0.30 than 0.60 or 1.00. Difference in glutamate concentration was not significant. EIRs showed the same difference as in GABA depending on the DKNTMN values. CVs between test-retest scans were relatively stable for glutamate but became larger as DKNTMN increased for GABA and EIR. Conclusion Neurochemical quantification depends on the parameters of the basis sets used for fitting. Analysis using MMmeas with DKNTMN of 0.30 conformed best to previous studies and is recommended.
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Affiliation(s)
| | | | | | - Koji Fujimoto
- Human Brain Research Center, Tokyo, Japan
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Japan
| | | | | | - Sinyeob Ahn
- Siemens Medical Solutions, Berkeley, California, USA
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Cudalbu C, Xin L, Marechal B, Lachat S, Zangas-Gheri F, Valenza N, Hanquinet S, McLin VA. High field brain proton magnetic resonance spectroscopy and volumetry in children with chronic, compensated liver disease - A pilot study. Anal Biochem 2023:115212. [PMID: 37356555 DOI: 10.1016/j.ab.2023.115212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND and rationale: There is increasing evidence that children or young adults having acquired liver disease in childhood display neurocognitive impairment which may become more apparent as they grow older. The molecular, cellular and morphological underpinnings of this clinical problem are incompletely understood. AIM Therefore, we used the advantages of highly-resolved proton magnetic resonance spectroscopy at ultra-high magnetic field to analyze the neurometabolic profile and brain morphometry of children with chronic, compensated liver disease, hypothesizing that with high field spectroscopy we would identify early evidence of rising brain glutamine and decreased myoinositol, such as has been described both in animals and humans with more significant liver disease. METHODS Patients (n = 5) and age-matched controls (n = 19) underwent 7T MR scans and short echo time 1H MR spectra were acquired using the semi-adiabatic SPECIAL sequence in two voxels located in gray and white matter dominated prefrontal cortex, respectively. A 3D MP2RAGE sequence was also acquired for brain volumetry and T1 mapping. Liver disease had to have developed at least 6 months before entering the study. Subjects underwent routine blood analysis and neurocognitive testing using validated methods within 3 months of MRI and MRS. RESULTS Five children currently aged 8-16 years with liver disease acquired in childhood were included. Baseline biological characteristics were similar among patients. There were no statistically significant differences between subjects and controls in brain metabolite levels or brain volumetry. Finally, there were minor neurocognitive fluctuations including attention deficit in one child, but none fell in the statistically significant range. CONCLUSION Children with chronic, compensated liver disease did not display an abnormal neurometabolic profile, neurocognitive abnormalities, or signal intensity changes in the globus pallidus. Despite the absence of neurometabolic changes, it is an opportunity to emphasize that it is only by developing the use of 1H MRS at high field in the clinical arena that we will understand the significance and generalizability of these findings in children with CLD. Attention deficit was observed in one child. Healthy children displayed neurometabolic regional differences as previously reported in adult subjects.
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Affiliation(s)
- Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Switzerland; Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Lijing Xin
- CIBM Center for Biomedical Imaging, Switzerland; Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Benedicte Marechal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sarah Lachat
- Swiss Pediatric Liver Center, Pediatric Gastroenterology, Hepatology and Nutrition Unit, University Hospitals Geneva, Department of Pediatrics, Gynecology and Obstetrics, University of Geneva Medical School, Geneva, Switzerland
| | - Florence Zangas-Gheri
- Pediatric Neurology Unit, University Hospitals Geneva, Department of Pediatrics, Gynecology and Obstetrics, University of Geneva Medical School, Geneva, Switzerland
| | - Nathalie Valenza
- Pediatric Neurology Unit, University Hospitals Geneva, Department of Pediatrics, Gynecology and Obstetrics, University of Geneva Medical School, Geneva, Switzerland
| | - Sylviane Hanquinet
- Pediatric Radiology Unit, Radiology Division, Diagnostic Department, Children's Hospital, University Hospitals of Geneva, Switzerland
| | - Valérie A McLin
- Swiss Pediatric Liver Center, Pediatric Gastroenterology, Hepatology and Nutrition Unit, University Hospitals Geneva, Department of Pediatrics, Gynecology and Obstetrics, University of Geneva Medical School, Geneva, Switzerland.
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Zimmermann J, Zölch N, Coray R, Bavato F, Friedli N, Baumgartner MR, Steuer AE, Opitz A, Werner A, Oeltzschner G, Seifritz E, Stock AK, Beste C, Cole DM, Quednow BB. Chronic 3,4-Methylenedioxymethamphetamine (MDMA) Use Is Related to Glutamate and GABA Concentrations in the Striatum But Not the Anterior Cingulate Cortex. Int J Neuropsychopharmacol 2023; 26:438-450. [PMID: 37235749 PMCID: PMC10289146 DOI: 10.1093/ijnp/pyad023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/24/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND 3,4-Methylenedioxymethamphetamine (MDMA) is a widely used recreational substance inducing acute release of serotonin. Previous studies in chronic MDMA users demonstrated selective adaptations in the serotonin system, which were assumed to be associated with cognitive deficits. However, serotonin functions are strongly entangled with glutamate as well as γ-aminobutyric acid (GABA) neurotransmission, and studies in MDMA-exposed rats show long-term adaptations in glutamatergic and GABAergic signaling. METHODS We used proton magnetic resonance spectroscopy (MRS) to measure the glutamate-glutamine complex (GLX) and GABA concentrations in the left striatum and medial anterior cingulate cortex (ACC) of 44 chronic but recently abstinent MDMA users and 42 MDMA-naïve healthy controls. While the Mescher-Garwood point-resolved-spectroscopy sequence (MEGA-PRESS) is best suited to quantify GABA, recent studies reported poor agreement between conventional short-echo-time PRESS and MEGA-PRESS for GLX measures. Here, we applied both sequences to assess their agreement and potential confounders underlying the diverging results. RESULTS Chronic MDMA users showed elevated GLX levels in the striatum but not the ACC. Regarding GABA, we found no group difference in either region, although a negative association with MDMA use frequency was observed in the striatum. Overall, GLX measures from MEGA-PRESS, with its longer echo time, appeared to be less confounded by macromolecule signal than the short-echo-time PRESS and thus provided more robust results. CONCLUSION Our findings suggest that MDMA use affects not only serotonin but also striatal GLX and GABA concentrations. These insights may offer new mechanistic explanations for cognitive deficits (e.g., impaired impulse control) observed in MDMA users.
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Affiliation(s)
- Josua Zimmermann
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Niklaus Zölch
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland
| | - Rebecca Coray
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Francesco Bavato
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Nicole Friedli
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Markus R Baumgartner
- Center for Forensic Hair Analytics, Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland
| | - Andrea E Steuer
- Department of Forensic Pharmacology and Toxicology, Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland
| | - Antje Opitz
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, TU Dresden, Dresden, Germany
| | - Annett Werner
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, TU Dresden, Dresden, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Erich Seifritz
- Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (Drs Zölch and Seifritz), University of Zurich, Zurich, Switzerland
| | - Ann-Kathrin Stock
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, TU Dresden, Dresden, Germany
- Biopsychology, Faculty of Psychology, School of Science, TU Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, TU Dresden, Dresden, Germany
| | - David M Cole
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Boris B Quednow
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland
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Prener M, Opheim G, Shams Z, Søndergaard CB, Lindberg U, Larsson HBW, Ziebell M, Larsen VA, Vestergaard MB, Paulson OB. Single-Voxel MR Spectroscopy of Gliomas with s-LASER at 7T. Diagnostics (Basel) 2023; 13:diagnostics13101805. [PMID: 37238288 DOI: 10.3390/diagnostics13101805] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/01/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND AND PURPOSE Magnetic resonance spectroscopy (MRS)-a method of analysing metabolites in vivo-has been utilized in several studies of brain glioma biomarkers at lower field strengths. At ultra-high field strengths, MRS provides an improved signal-to-noise-ratio and spectral resolution, but 7T studies on patients with gliomas are sparse. The purpose of this exploratory study was to evaluate the potential clinical implication of the use of single-voxel MRS at 7T to assess metabolic information on lesions in a pilot cohort of patients with grade II and III gliomas. METHODS We scanned seven patients and seven healthy controls using the semi-localization by adiabatic-selective refocusing sequence on a Philips Achieva 7T system with a standard dual-transmit head coil. The metabolic ratios were calculated relative to water and total creatine. Additionally, 2-hydroxyglutarate (2-HG) MRS was carried out in four of the patients, and the 2-HG concentration was calculated relative to water. RESULTS When comparing the tumour data to control regions in both patients and healthy controls, we found that the choline/creatine and myo-inositol/creatine ratios were significantly increased and that the N-acetylaspartate/creatine and the neurotransmitter glutamate/creatine ratios were significantly decreased. The N-acetylaspartate/water and glutamate/water ratios were also significantly decreased. The lactate/water and lactate/creatine ratios showed increases, although not significant. The GABA/water ratio was significantly decreased, but the GABA/creatine ratio was not. MRS spectra showed the presence of 2-HG in three of the four patients studied. Three of the patients, including the MRS 2-HG-negative patient, were operated on, and all of them had the IDH mutation. CONCLUSION Our findings were consistent with the existing literature on 3T and 7T MRS.
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Affiliation(s)
- Martin Prener
- Neurobiology Research Unit, Department of Neurology, Rigshospitalet Blegdamsvej, 2100 Copenhagen, Denmark
| | - Giske Opheim
- Neurobiology Research Unit, Department of Neurology, Rigshospitalet Blegdamsvej, 2100 Copenhagen, Denmark
- Department of Radiology, Rigshospitalet Blegdamsvej, 2100 Copenhagen, Denmark
| | - Zahra Shams
- Center for Image Sciences, University Medical Centre Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands
| | | | - Ulrich Lindberg
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Glostrup, 2600 Copenhagen, Denmark
| | - Henrik B W Larsson
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Glostrup, 2600 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Morten Ziebell
- Department of Neurosurgery, Rigshospitalet Blegdamsvej, 2100 Copenhagen, Denmark
| | | | - Mark Bitsch Vestergaard
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Glostrup, 2600 Copenhagen, Denmark
| | - Olaf B Paulson
- Neurobiology Research Unit, Department of Neurology, Rigshospitalet Blegdamsvej, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
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Schallmo MP, Weldon KB, Kamath RS, Moser HR, Montoya SA, Killebrew KW, Demro C, Grant AN, Marjańska M, Sponheim SR, Olman CA. The psychosis human connectome project: Design and rationale for studies of visual neurophysiology. Neuroimage 2023; 272:120060. [PMID: 36997137 PMCID: PMC10153004 DOI: 10.1016/j.neuroimage.2023.120060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/22/2023] [Accepted: 03/27/2023] [Indexed: 04/01/2023] Open
Abstract
Visual perception is abnormal in psychotic disorders such as schizophrenia. In addition to hallucinations, laboratory tests show differences in fundamental visual processes including contrast sensitivity, center-surround interactions, and perceptual organization. A number of hypotheses have been proposed to explain visual dysfunction in psychotic disorders, including an imbalance between excitation and inhibition. However, the precise neural basis of abnormal visual perception in people with psychotic psychopathology (PwPP) remains unknown. Here, we describe the behavioral and 7 tesla MRI methods we used to interrogate visual neurophysiology in PwPP as part of the Psychosis Human Connectome Project (HCP). In addition to PwPP (n = 66) and healthy controls (n = 43), we also recruited first-degree biological relatives (n = 44) in order to examine the role of genetic liability for psychosis in visual perception. Our visual tasks were designed to assess fundamental visual processes in PwPP, whereas MR spectroscopy enabled us to examine neurochemistry, including excitatory and inhibitory markers. We show that it is feasible to collect high-quality data across multiple psychophysical, functional MRI, and MR spectroscopy experiments with a sizable number of participants at a single research site. These data, in addition to those from our previously described 3 tesla experiments, will be made publicly available in order to facilitate further investigations by other research groups. By combining visual neuroscience techniques and HCP brain imaging methods, our experiments offer new opportunities to investigate the neural basis of abnormal visual perception in PwPP.
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Affiliation(s)
- Michael-Paul Schallmo
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
| | - Kimberly B Weldon
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA; Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Rohit S Kamath
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Hannah R Moser
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Samantha A Montoya
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Kyle W Killebrew
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Caroline Demro
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA; Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Andrea N Grant
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Małgorzata Marjańska
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Scott R Sponheim
- Veterans Affairs Medical Center, Minneapolis, MN, USA; Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Cheryl A Olman
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA; Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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Peek AL, Rebbeck TJ, Leaver AM, Foster SL, Refshauge KM, Puts NA, Oeltzschner G. A comprehensive guide to MEGA-PRESS for GABA measurement. Anal Biochem 2023; 669:115113. [PMID: 36958511 PMCID: PMC10805000 DOI: 10.1016/j.ab.2023.115113] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/25/2023]
Abstract
The aim of this guideline is to provide a series of evidence-based recommendations that allow those new to using MEGA-PRESS to produce high-quality data for the measurement of GABA levels using edited magnetic resonance spectroscopy with the MEGA-PRESS sequence at 3T. GABA is the main inhibitory neurotransmitter of the central nervous system and has been increasingly studied due to its relevance in many clinical disorders of the central nervous system. MEGA-PRESS is the most widely used method for quantification of GABA at 3T, but is technically challenging and operates at a low signal-to-noise ratio. Therefore, the acquisition of high-quality MRS data relies on avoiding numerous pitfalls and observing important caveats. The guideline was developed by a working party that consisted of experts in MRS and experts in guideline development and implementation, together with key stakeholders. Strictly following a translational framework, we first identified evidence using a systematically conducted scoping literature review, then synthesized and graded the quality of evidence that formed recommendations. These recommendations were then sent to a panel of 21 world leaders in MRS for feedback and approval using a modified-Delphi process across two rounds. The final guideline consists of 23 recommendations across six domains essential for GABA MRS acquisition (Parameters, Practicalities, Data acquisition, Confounders, Quality/reporting, Post-processing). Overall, 78% of recommendations were formed from high-quality evidence, and 91% received agreement from over 80% of the expert panel. These 23 expert-reviewed recommendations and accompanying extended documentation form a readily useable guideline to allow those new to using MEGA-PRESS to design appropriate MEGA-PRESS study protocols and generate high-quality data.
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Affiliation(s)
- A L Peek
- Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, 2141, Australia; NHMRC Centre of Research Excellence in Road Traffic Injury Recovery, Queensland, Australia.
| | - T J Rebbeck
- Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, 2141, Australia; NHMRC Centre of Research Excellence in Road Traffic Injury Recovery, Queensland, Australia.
| | - A M Leaver
- Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, 2141, Australia.
| | - S L Foster
- Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, 2141, Australia; Department of Radiology, Westmead Hospital, Hawkesbury Road, Westmead, New South Wales, 2145, Australia.
| | - K M Refshauge
- Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, 2141, Australia.
| | - N A Puts
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, Kings College London, UK.
| | - G Oeltzschner
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, United States; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, 21205, United States.
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Rizzo R, Dziadosz M, Kyathanahally SP, Shamaei A, Kreis R. Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias. Magn Reson Med 2023; 89:1707-1727. [PMID: 36533881 DOI: 10.1002/mrm.29561] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy and precision of DL predictions in view of inherent bias toward the training distribution. METHODS Simulated 1D spectra and 2D spectrograms that mimic an extensive range of pathological in vivo conditions are used to train and test 24 different DL architectures. Active learning through altered training and testing data distributions is probed to optimize quantification performance. Ensembles of networks are explored to improve DL robustness and reduce the variance of estimates. A set of scores compares performances of DL predictions and traditional model fitting (MF). RESULTS Ensembles of heterogeneous networks that combine 1D frequency-domain and 2D time-frequency domain spectrograms as input perform best. Dataset augmentation with active learning can improve performance, but gains are limited. MF is more accurate, although DL appears to be more precise at low SNR. However, this overall improved precision originates from a strong bias for cases with high uncertainty toward the dataset the network has been trained with, tending toward its average value. CONCLUSION MF mostly performs better compared to the faster DL approach. Potential intrinsic biases on training sets are dangerous in a clinical context that requires the algorithm to be unbiased to outliers (i.e., pathological data). Active learning and ensemble of networks are good strategies to improve prediction performances. However, data quality (sufficient SNR) has proven as a bottleneck for adequate unbiased performance-like in the case of MF.
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Affiliation(s)
- Rudy Rizzo
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland.,Department for Biomedical Research, University of Bern, Bern, Switzerland.,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Martyna Dziadosz
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland.,Department for Biomedical Research, University of Bern, Bern, Switzerland.,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Sreenath P Kyathanahally
- Department of System Analysis, Integrated Assessment and Modelling, Data Science for Environmental Research Group, EAWAG, Dübendorf, Switzerland
| | - Amirmohammad Shamaei
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic, Brno, Czech Republic.,Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic
| | - Roland Kreis
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland.,Department for Biomedical Research, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
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Shamaei AM, Starcukova J, Starcuk Z. Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data. Comput Biol Med 2023; 158:106837. [PMID: 37044049 DOI: 10.1016/j.compbiomed.2023.106837] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/06/2023] [Accepted: 03/26/2023] [Indexed: 04/08/2023]
Abstract
PURPOSE While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. METHOD We present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. RESULT The validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. CONCLUSION The proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.
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Zöllner HJ, Davies-Jenkins CW, Murali-Manohar S, Gong T, Hui SCN, Song Y, Chen W, Wang G, Edden RAE, Oeltzschner G. Feasibility and implications of using subject-specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data. NMR IN BIOMEDICINE 2023; 36:e4854. [PMID: 36271899 PMCID: PMC9930668 DOI: 10.1002/nbm.4854] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/14/2022] [Accepted: 10/15/2022] [Indexed: 05/27/2023]
Abstract
Expert consensus recommends linear-combination modeling (LCM) of 1 H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions are usually derived from metabolite-nulled spectra averaged across a small cohort. The use of subject-specific instead of cohort-averaged measured MM basis functions has not been studied widely. Furthermore, measured MM basis functions are not widely available to non-expert users, who commonly rely on parameterized MM signals internally simulated by LCM software. To investigate the impact of the choice of MM modeling, this study, therefore, compares metabolite level estimates between different MM modeling strategies (cohort-mean measured; subject-specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite-age associations. 100 conventional (TE = 30 ms) and metabolite-nulled (TI = 650 ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20-70 years of age), were analyzed in Osprey. Short-TE spectra were modeled in Osprey using six different strategies to consider the MM baseline. Fully tissue- and relaxation-corrected metabolite levels were compared between MM strategies. Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite-age associations. The choice of MM strategy had a significant impact on the mean metabolite level estimates and no major impact on variance. Correlation analysis revealed moderate-to-strong agreement between different MM strategies (r > 0.6). The lowest relative model residuals and AIC values were found for the cohort-mean measured MM. Metabolite-age associations were consistently found for two major singlet signals (total creatine (tCr])and total choline (tCho)) for all MM strategies; however, findings for metabolites that are less distinguishable from the background signals associations depended on the MM strategy. A variance partition analysis indicated that up to 44% of the total variance was related to the choice of MM strategy. Additionally, the variance partition analysis reproduced the metabolite-age association for tCr and tCho found in the simpler correlation analysis. In summary, the inclusion of a single high signal-to-noise ratio MM basis function (cohort-mean) in the short-TE LCM leads to more lower model residuals and AIC values compared with MM strategies with more degrees of freedom (Gaussian parametrization) or subject-specific MM information. Integration of multiple LCM analyses into a single statistical model potentially allows to identify the robustness in the detection of underlying effects (e.g., metabolite vs. age), reduces algorithm-based bias, and estimates algorithm-related variance.
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Affiliation(s)
- Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Christopher W. Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Tao Gong
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021,China
- Departments of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Jinan, Shandong, 250021,China
| | - Steve C. N. Hui
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | | | - Guangbin Wang
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021,China
- Departments of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Jinan, Shandong, 250021,China
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
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Harris AD, Amiri H, Bento M, Cohen R, Ching CRK, Cudalbu C, Dennis EL, Doose A, Ehrlich S, Kirov II, Mekle R, Oeltzschner G, Porges E, Souza R, Tam FI, Taylor B, Thompson PM, Quidé Y, Wilde EA, Williamson J, Lin AP, Bartnik-Olson B. Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations. Front Neurol 2023; 13:1045678. [PMID: 36686533 PMCID: PMC9845632 DOI: 10.3389/fneur.2022.1045678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023] Open
Abstract
Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.
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Affiliation(s)
- Ashley D. Harris
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Houshang Amiri
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Mariana Bento
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Ronald Cohen
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Los Angeles, CA, United States
| | - Christina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Emily L. Dennis
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
| | - Arne Doose
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Ivan I. Kirov
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY, United States
| | - Ralf Mekle
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Porges
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Roberto Souza
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Friederike I. Tam
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Brian Taylor
- Division of Diagnostic Imaging, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Los Angeles, CA, United States
| | - Yann Quidé
- School of Psychology, University of New South Wales (UNSW), Sydney, NSW, Australia
| | - Elisabeth A. Wilde
- TBI and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
| | - John Williamson
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, United States
| | - Alexander P. Lin
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Brenda Bartnik-Olson
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, United States
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42
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Graf C, Soellradl M, Aigner CS, Rund A, Stollberger R. Advanced design of MRI inversion pulses for inhomogeneous field conditions by optimal control. NMR IN BIOMEDICINE 2022; 35:e4790. [PMID: 35731240 PMCID: PMC9786750 DOI: 10.1002/nbm.4790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/01/2022] [Accepted: 06/19/2022] [Indexed: 06/15/2023]
Abstract
Non-selective inversion pulses find widespread use in MRI applications, where requirements on them are increasingly demanding. With the use of high and ultra-high field strength systems, robustness to Δ B 0 and B 1 + inhomogeneities, while tackling SAR and hardware limitations, has rapidly become important. In this work, we propose a time-optimal control framework for the optimization of Δ B 0 - and B 1 + -robust inversion pulses. Robustness is addressed by means of ensemble formulations, while allowing inclusion of hardware and energy limitations. The framework is flexible and performs excellently for various optimization goals. The optimization results are analyzed extensively in numerical experiments. Furthermore, they are validated, and compared with adiabatic RF pulses, in various phantom and in vivo measurements on a 3 T MRI system.
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Affiliation(s)
- Christina Graf
- Institute of Biomedical ImagingGraz University of TechnologyGrazAustria
| | - Martin Soellradl
- Institute of Biomedical ImagingGraz University of TechnologyGrazAustria
| | | | - Armin Rund
- Institute for Mathematics and Scientific ComputingUniversity of GrazGrazAustria
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43
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Mosso J, Simicic D, Şimşek K, Kreis R, Cudalbu C, Jelescu IO. MP-PCA denoising for diffusion MRS data: promises and pitfalls. Neuroimage 2022; 263:119634. [PMID: 36150605 DOI: 10.1016/j.neuroimage.2022.119634] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 09/07/2022] [Accepted: 09/14/2022] [Indexed: 10/31/2022] Open
Abstract
Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction strategies. In the present work, Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4 T in rat brain and at 3 T in human brain. We provide a descriptive study of the effects observed following different MP-PCA denoising strategies (denoising the entire matrix versus using a sliding window), in terms of apparent SNR, rank selection, noise correlation within and across b-values and quantification of metabolite concentrations and fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent SNR, a more accurate B0 drift correction between shots, and similar estimates of metabolite concentrations and diffusivities compared to the raw data. No spectral residuals on individual shots were observed but correlations in the noise level across shells were introduced, an effect which was mitigated using a sliding window, but which should be carefully considered.
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Affiliation(s)
- Jessie Mosso
- CIBM Center for Biomedical Imaging, Switzerland; Animal Imaging and Technology, EPFL, Lausanne, Switzerland; LIFMET, EPFL, Lausanne, Switzerland.
| | - Dunja Simicic
- CIBM Center for Biomedical Imaging, Switzerland; Animal Imaging and Technology, EPFL, Lausanne, Switzerland; LIFMET, EPFL, Lausanne, Switzerland
| | - Kadir Şimşek
- Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roland Kreis
- Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Switzerland; Animal Imaging and Technology, EPFL, Lausanne, Switzerland
| | - Ileana O Jelescu
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
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44
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Ziegs T, Dorst J, Ruhm L, Avdievitch N, Henning A. Measurement of glucose metabolism in the occipital lobe and frontal cortex after oral administration of [1-13C]glucose at 9.4 T. J Cereb Blood Flow Metab 2022; 42:1890-1904. [PMID: 35632989 PMCID: PMC9536126 DOI: 10.1177/0271678x221104540] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 04/29/2022] [Accepted: 05/05/2022] [Indexed: 12/02/2022]
Abstract
For the first time, labeling effects after oral intake of [1-13C]glucose are observed in the human brain with pure 1H detection at 9.4 T. Spectral time series were acquired using a short-TE 1H MRS MC-semiLASER (Metabolite Cycling semi Localization by Adiabatic SElective Refocusing) sequence in two voxels of 5.4 mL in the frontal cortex and the occipital lobe. High-quality time-courses of [4-13C]glutamate, [4-13C]glutamine, [3-13C]glutamate + glutamine, [2-13C] glutamate+glutamine and [3-13C]aspartate for individual volunteers and additionally, group-averaged time-courses of labeled and non-labeled brain glucose could be obtained. Using a one-compartment model, mean metabolic rates were calculated for each voxel position: The mean rate of the TCA-cycle (Vtca) value was determined to be 1.36 and 0.93 μmol min-1 g-1, the mean rate of glutamine synthesis (Vgln) was calculated to be 0.23 and 0.45 μmol min-1 g-1, the mean exchange rate between cytosolic amino acids and mitochondrial Krebs cycle intermediates (Vx) rate was found to be 0.57 and 1.21 μmol min-1 g-1 for the occipital lobe and the frontal cortex, respectively. These values were in agreement with previously reported data. Altogether, it can be shown that this most simple technique combining oral administration of [1-13C]Glc with pure 1H MRS acquisition is suitable to measure metabolic rates.
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Affiliation(s)
- Theresia Ziegs
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Johanna Dorst
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Loreen Ruhm
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Nikolai Avdievitch
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High‐Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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45
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Pavlova I, Drazanova E, Kratka L, Amchova P, Macicek O, Starcukova J, Starcuk Z, Ruda-Kucerova J. Laterality in functional and metabolic state of the bulbectomised rat brain detected by ASL and 1H MRS: A pilot study. World J Biol Psychiatry 2022; 24:414-428. [PMID: 36102141 DOI: 10.1080/15622975.2022.2124450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
OBJECTIVES Pilot study validating the animal model of depression - the bilateral olfactory bulbectomy in rats - by two nuclear magnetic resonance methods, indirectly detecting the metabolic state of the brain. Furthermore, the study focussed on potential differences in brain laterality. METHODS Arterial spin labelling assessed cerebral brain flow in prefrontal, sensorimotor, and piriform cortices, nucleus accumbens, hippocampus, thalamus, circle of Willis, and whole brain. Proton magnetic resonance spectroscopy provided information about relative metabolite concentrations in the cortex and hippocampus. RESULTS Arterial spin labelling found no differences in cerebral perfusion in the group comparison but revealed lateralisation in the thalamus of the control group and the sensorimotor cortex of the bulbectomized rats. Lower Cho/tCr and Cho/NAA levels were found in the right hippocampus in bulbectomized rats. The differences in lateralisation were shown in the hippocampus: mI/tCr in the control group, Cho/NAA, NAA/tCr, Tau/tCr in the model group, and in the cortex: NAA/tCr, mI/tCr in the control group. CONCLUSION Olfactory bulbectomy affects the neuronal and biochemical profile of the rat brain laterally and, as a model of depression, was validated by two nuclear magnetic resonance methods.
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Affiliation(s)
- Iveta Pavlova
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.,Department of Condensed Matter Physics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Eva Drazanova
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.,Department of Pharmacology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Lucie Kratka
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Petra Amchova
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Ondrej Macicek
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Jana Starcukova
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Zenon Starcuk
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Jana Ruda-Kucerova
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
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46
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Ziegs T, Wright AM, Henning A. Test-retest reproducibility of human brain multi-slice 1 H FID-MRSI data at 9.4T after optimization of lipid regularization, macromolecular model, and spline baseline stiffness. Magn Reson Med 2022; 89:11-28. [PMID: 36128885 DOI: 10.1002/mrm.29423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE This study analyzes the effects of retrospective lipid suppression, a simulated macromolecular prior knowledge and different spline baseline stiffness values on 9.4T multi-slice proton FID-MRSI data spanning the whole cerebrum of human brain and the reproducibility of respective metabolite ratio to total creatine (/tCr) maps for 10 brain metabolites. METHODS Measurements were performed twice on 5 volunteers using a short TR and TE FID MRSI 2D sequence at 9.4T. The effects of retrospective lipid L2-regularization, macromolecular spectrum and different LCModel baseline flexibilities on SNR, FWHM, fitting residual, Cramér-Rao lower bound, and metabolite ratio maps were investigated. Intra-subject, inter-session coefficient of variation and the test-retest reproducibility of the mean metabolite ratios (/tCr) of each slice was calculated. RESULTS Transversal, sagittal, and coronal slices of many metabolite ratio maps correspond to the anatomically expected concentration relations in gray and white matter for the majority of the cerebrum when using a flexible baseline in LCModel fit. Results from the second measurements of the same subjects show that slice positioning and data quality correlate significantly to the first measurement. L2-regularization provided effective suppression of lipid-artifacts, but should be avoided if no artifacts are detected. CONCLUSION Reproducible concentration ratio maps (/tCr) for 4 metabolites (total choline, N-acetylaspartate, glutamate, and myoinositol) spanning the majority of the cerebrum and 6 metabolites (N-acetylaspartylglutamate, γ-aminobutyric acid, glutathione, taurine, glutamine, and aspartate) covering 32 mm in the upper part of the brain were acquired at 9.4T using multi-slice FID MRSI with retrospective lipid suppression, a macromolecular spectrum and a flexible LCModel baseline.
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Affiliation(s)
- Theresia Ziegs
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Andrew Martin Wright
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, Tübingen, Germany
| | - Anke Henning
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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47
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Rideaux R, Ehrhardt SE, Wards Y, Filmer HL, Jin J, Deelchand DK, Marjańska M, Mattingley JB, Dux PE. On the relationship between GABA+ and glutamate across the brain. Neuroimage 2022; 257:119273. [PMID: 35526748 PMCID: PMC9924060 DOI: 10.1016/j.neuroimage.2022.119273] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 04/13/2022] [Accepted: 04/29/2022] [Indexed: 01/27/2023] Open
Abstract
Equilibrium between excitation and inhibition (E/I balance) is key to healthy brain function. Conversely, disruption of normal E/I balance has been implicated in a range of central neurological pathologies. Magnetic resonance spectroscopy (MRS) provides a non-invasive means of quantifying in vivo concentrations of excitatory and inhibitory neurotransmitters, which could be used as diagnostic biomarkers. Using the ratio of excitatory and inhibitory neurotransmitters as an index of E/I balance is common practice in MRS work, but recent studies have shown inconsistent evidence for the validity of this proxy. This is underscored by the fact that different measures are often used in calculating E/I balance such as glutamate and Glx (glutamate and glutamine). Here we used a large MRS dataset obtained at ultra-high field (7 T) measured from 193 healthy young adults and focused on two brain regions - prefrontal and occipital cortex - to resolve this inconsistency. We find evidence that there is an inter-individual common ratio between GABA+ (γ-aminobutyric acid and macromolecules) and Glx in the occipital, but not prefrontal cortex. We further replicate the prefrontal result in a legacy dataset (n = 78) measured at high-field (3 T) strength. By contrast, with ultra-high field MRS data, we find extreme evidence that there is a common ratio between GABA+ and glutamate in both prefrontal and occipital cortices, which cannot be explained by participant demographics, signal quality, fractional tissue volume, or other metabolite concentrations. These results are consistent with previous electrophysiological and theoretical work supporting E/I balance. Our findings indicate that MRS-detected GABA+ and glutamate (but not Glx), are a reliable measure of E/I balance .
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Affiliation(s)
- Reuben Rideaux
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia.
| | - Shane E Ehrhardt
- School of Psychology, The University of Queensland, St Lucia, Australia
| | - Yohan Wards
- School of Psychology, The University of Queensland, St Lucia, Australia
| | - Hannah L Filmer
- School of Psychology, The University of Queensland, St Lucia, Australia
| | - Jin Jin
- Siemens Healthcare Pty Ltd, Brisbane, Australia; Center for Advanced Imaging, The University of Queensland, St Lucia, Australia
| | - Dinesh K Deelchand
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Jason B Mattingley
- Queensland Brain Institute, The University of Queensland, St Lucia, Australia; School of Psychology, The University of Queensland, St Lucia, Australia
| | - Paul E Dux
- School of Psychology, The University of Queensland, St Lucia, Australia
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48
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Kumaragamage C, Coppoli A, Brown PB, McIntyre S, Nixon TW, De Feyter HM, Mason GF, de Graaf RA. Short symmetric and highly selective asymmetric first and second order gradient modulated offset independent adiabaticity (GOIA) pulses for applications in clinical MRS and MRSI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107247. [PMID: 35691241 PMCID: PMC9933141 DOI: 10.1016/j.jmr.2022.107247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 05/04/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Gradient modulated RF pulses, especially gradient offset independent adiabaticity (GOIA) pulses, are increasingly gaining attention for high field clinical magnetic resonance spectroscopy and spectroscopic imaging (MRS/MRSI) due to the lower peak B1 amplitude and associated power demands achievable relative to its non-modulated adiabatic full passage counterparts. In this work we describe the development of two GOIA RF pulses: 1) A power efficient, 3.0 ms wideband uniform rate with smooth truncation (WURST) modulated RF pulse with 15 kHz bandwidth compatible with a clinically feasible peak B1 amplitude of 0.87 kHz (or 20 µT), and 2) A highly selective asymmetric 6.66 ms RF pulse with 20 kHz bandwidth designed to achieve a single-sided, fractional transition width of only 1.7%. Effects of potential asynchrony between RF and gradient-modulated (GM) waveforms for 3 ms GOIA-WURST RF pulses was evaluated by simulation and experimentally. Results demonstrate that a 20+ µs asynchrony between RF and GM functions substantially degrades inversion performance when using large RF offsets to achieve translation. A projection-based method is presented that allows a quick calibration of RF and GM asynchrony on pre-clinical/clinical MR systems. The asymmetric GOIA pulse was implemented within a multi-pulse OVS sequence to achieve power efficient, highly-selective, and B1 and T1-independent signal suppression for extracranial lipid suppression. The developed GOIA pulses were utilized with linear gradient modulation (X, Y, Z gradient fields), and with second-order-field modulations (Z2, X2Y2 gradient fields) to provide elliptically-shaped regions-of-interest for MRS and MRSI acquisitions. Both described GOIA-RF pulses have substantial clinical value; specifically, the 3.0 ms GOIA-WURST pulse is beneficial to realize short TE sLASER localized proton MRS/MRSI sequences, and the asymmetric GOIA RF pulse has applications in highly selective outer volume signal suppression to allow interrogation of tissue proximal to extracranial lipids with full-intensity.
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Affiliation(s)
- Chathura Kumaragamage
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA.
| | - Anastasia Coppoli
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Peter B Brown
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Scott McIntyre
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Terence W Nixon
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Henk M De Feyter
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Graeme F Mason
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale University School of Medicine, New Haven, CT, USA
| | - Robin A de Graaf
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale University School of Medicine, New Haven, CT, USA
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49
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Soher BJ, Clarke WT, Wilson M, Near J, Oeltzschner G. Community-Organized Resources for Reproducible MRS Data Analysis. Magn Reson Med 2022; 88:1959-1961. [PMID: 35849735 DOI: 10.1002/mrm.29387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/01/2022] [Accepted: 06/23/2022] [Indexed: 11/10/2022]
Affiliation(s)
- Brian J Soher
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
| | - William T Clarke
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Jamie Near
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland.,F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
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50
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Spurny-Dworak B, Godbersen GM, Reed MB, Unterholzner J, Vanicek T, Baldinger-Melich P, Hahn A, Kranz GS, Bogner W, Lanzenberger R, Kasper S. The Impact of Theta-Burst Stimulation on Cortical GABA and Glutamate in Treatment-Resistant Depression: A Surface-Based MRSI Analysis Approach. Front Mol Neurosci 2022; 15:913274. [PMID: 35909445 PMCID: PMC9328022 DOI: 10.3389/fnmol.2022.913274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Theta burst stimulation (TBS) belongs to one of the biological antidepressant treatment options. When applied bilaterally, excitatory intermittent TBS (iTBS) is commonly targeted to the left and inhibitory continuous TBS (cTBS) to the right dorsolateral prefrontal cortex. TBS was shown to influence neurotransmitter systems, while iTBS is thought to interfere with glutamatergic circuits and cTBS to mediate GABAergic neurotransmission. Objectives: We aimed to expand insights into the therapeutic effects of TBS on the GABAergic and glutamatergic system utilizing 3D-multivoxel magnetic resonance spectroscopy imaging (MRSI) in combination with a novel surface-based MRSI analysis approach to investigate changes of cortical neurotransmitter levels in patients with treatment-resistant depression (TRD). Methods: Twelve TRD patients (five females, mean age ± SD = 35 ± 11 years) completed paired MRSI measurements, using a GABA-edited 3D-multivoxel MEGA-LASER sequence, before and after 3 weeks of bilateral TBS treatment. Changes in cortical distributions of GABA+/tNAA (GABA+macromolecules relative to total N-acetylaspartate) and Glx/tNAA (Glx = mixed signal of glutamate and glutamine), were investigated in a surface-based region-of-interest (ROI) analysis approach. Results: ANCOVAs revealed a significant increase in Glx/tNAA ratios in the left caudal middle frontal area (p corr. = 0.046, F = 13.292), an area targeted by iTBS treatment. Whereas, contralateral treatment with cTBS evoked no alterations in glutamate or GABA concentrations. Conclusion: This study demonstrates surface-based adaptions in the stimulation area to the glutamate metabolism after excitatory iTBS but not after cTBS, using a novel surface-based analysis of 3D-MRSI data. The reported impact of facilitatory iTBS on glutamatergic neurotransmission provides further insight into the neurobiological effects of TBS in TRD.
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Affiliation(s)
- Benjamin Spurny-Dworak
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Jakob Unterholzner
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Vanicek
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Pia Baldinger-Melich
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Georg S. Kranz
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Wolfgang Bogner
- Department of Biomedical Imaging and Image-guided Therapy, High Field MR Centre, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Department of Molecular Neuroscience, Center for Brain Research, Medical University of Vienna, Vienna, Austria
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