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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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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:e5236. [PMID: 39138125 DOI: 10.1002/nbm.5236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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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3
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Nikoumanesh N, Chase CJ, Nagarajan R, Potter K, Martini DN. Frontal cortex neurometabolites and mobility in older adults: a preliminary study. Exp Brain Res 2024; 242:2013-2022. [PMID: 38949687 DOI: 10.1007/s00221-024-06881-0] [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: 02/22/2024] [Accepted: 06/19/2024] [Indexed: 07/02/2024]
Abstract
BACKGROUND The frontal cortex, relevant to global cognition and motor function, is recruited to compensate for mobility dysfunction in older adults. However, the in vivo neurophysiological (e.g., neurometabolites) underpinnings of the frontal cortex compensation for mobility dysfunction remain poorly understood. The purpose of this study was to investigate the relationships among frontal cortex neurophysiology, mobility, and cognition in healthy older adults. METHODS Magnetic Resonance Spectroscopy (MRS) quantified N-acetylasparate (tNAA) and total choline (tCho) concentrations and ratios in the frontal cortex in 21 older adults. Four inertial sensors recorded the Timed Up & Go (TUG) test. Cognition was assessed using the Flanker Inhibitory Control and Attention Test which requires conflict resolution because of response interference from flanking distractors during incongruent trials. Congruent trials require no conflict resolution. RESULTS tNAA concentration significantly related to the standing (p = 0.04) and sitting (p = 0.03) lean angles. tCho concentration (p = 0.04) and tCho ratio (p = 0.02) significantly related to TUG duration. tCho concentration significantly related to incongruent response time (p = 0.01). tCho ratio significantly related to both congruent (p = 0.009) and incongruent (p < 0.001) response times. Congruent (p = 0.02) and incongruent (p = 0.02) Flanker response times significantly related to TUG duration. CONCLUSIONS Altered levels of frontal cortex neurometabolites are associated with both mobility and cognitive abilities in healthy older adults. Identifying neurometabolites associated with frontal cortex compensation of mobility dysfunction could improve targeted therapies aimed at improving mobility in older adults.
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Affiliation(s)
- Nikou Nikoumanesh
- Department of Kinesiology, University of Massachusetts Amherst, Totman Building 30 Eastman Lane Amherst, Amherst, MA, 01003, USA
| | - Colleen J Chase
- Department of Kinesiology, University of Massachusetts Amherst, Totman Building 30 Eastman Lane Amherst, Amherst, MA, 01003, USA
| | - Rajakumar Nagarajan
- Human Magnetic Resonance Center, Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Katie Potter
- Department of Kinesiology, University of Massachusetts Amherst, Totman Building 30 Eastman Lane Amherst, Amherst, MA, 01003, USA
- Center for Personalized Health Monitoring, Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Douglas N Martini
- Department of Kinesiology, University of Massachusetts Amherst, Totman Building 30 Eastman Lane Amherst, Amherst, MA, 01003, USA.
- Center for Personalized Health Monitoring, Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA.
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Turco F, Capiglioni M, Weng G, Slotboom J. TensorFit: A torch-based tool for ultrafast metabolite fitting of large MRSI data sets. Magn Reson Med 2024; 92:447-458. [PMID: 38469890 DOI: 10.1002/mrm.30084] [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: 09/29/2023] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE To introduce a tool (TensorFit) for ultrafast and robust metabolite fitting of MRSI data based on Torch's auto-differentiation and optimization framework. METHODS TensorFit was implemented in Python based on Torch's auto-differentiation to fit individual metabolites in MRS spectra. The underlying time domain and/or frequency domain fitting model is based on a linear combination of metabolite spectroscopic response. The computational time efficiency and accuracy of TensorFit were tested on simulated and in vivo MRS data and compared against TDFDFit and QUEST. RESULTS TensorFit demonstrates a significant improvement in computation speed, achieving a 165-times acceleration compared with TDFDFit and 115 times against QUEST. TensorFit showed smaller percentual errors on simulated data compared with TDFDFit and QUEST. When tested on in vivo data, it performed similarly to TDFDFit with a 2% better fit in terms of mean squared error while obtaining a 169-fold speedup. CONCLUSION TensorFit enables fast and robust metabolite fitting in large MRSI data sets compared with conventional metabolite fitting methods. This tool could boost the clinical applicability of large 3D MRSI by enabling the fitting of large MRSI data sets within computation times acceptable in a clinical environment.
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Affiliation(s)
- Federico Turco
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Milena Capiglioni
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Guodong Weng
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
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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:e5203. [PMID: 38953695 DOI: 10.1002/nbm.5203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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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Huang YL, Lin YR, Tsai SY. Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy. MAGMA (NEW YORK, N.Y.) 2024; 37:477-489. [PMID: 37713007 DOI: 10.1007/s10334-023-01120-z] [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: 04/24/2023] [Revised: 08/09/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful. MATERIALS AND METHODS This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system. RESULTS The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content. CONCLUSION In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.
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Affiliation(s)
- Yu-Long Huang
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Yi-Ru Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Shang-Yueh Tsai
- Graduate Institute of Applied Physics, National Chengchi University, No.64, Sec.2, ZhiNan Rd., Wenshan District, Taipei, 11605, Taiwan.
- Research Center for Mind, Brain and Learning, National Chengchi University, Taipei, Taiwan.
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7
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Zhao T, Grist JT, Auer DP, Avula S, Bailey S, Davies NP, Grundy RG, Khan O, MacPherson L, Morgan PS, Pizer B, Rose HEL, Sun Y, Wilson M, Worthington L, Arvanitis TN, Peet AC. Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification. NMR IN BIOMEDICINE 2024; 37:e5129. [PMID: 38494431 DOI: 10.1002/nbm.5129] [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: 04/12/2023] [Revised: 01/07/2024] [Accepted: 02/03/2024] [Indexed: 03/19/2024]
Abstract
Proton magnetic resonance spectroscopy (1H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1H-MRS may have better diagnostic performance for paediatric brain tumours.
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Affiliation(s)
- Teddy Zhao
- Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - James T Grist
- Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Dorothee P Auer
- Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Shivaram Avula
- Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Simon Bailey
- Paediatric Oncology, Great North Children's Hospital, Newcastle upon Tyne, UK
| | - Nigel P Davies
- Imaging and Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Omar Khan
- Digital Healthcare, WMG, University of Warwick, Coventry, UK
| | | | - Paul S Morgan
- Clinical Neuroscience, University of Nottingham, Nottingham, UK
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Medical Physics, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | | | - Heather E L Rose
- Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - Yu Sun
- Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - Martin Wilson
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Lara Worthington
- Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- RRPPS, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Theodoros N Arvanitis
- Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- Digital Healthcare, WMG, University of Warwick, Coventry, UK
- Engineering, University of Birmingham, Birmingham, UK
| | - Andrew C Peet
- Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
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Nemati S, Haghani Dogahe M, Saberi A, Ramezani N, Kiani P, Yaghubi Kalurazi T, Kazemnejad Leili E, Seddighi S, Monsef A. Magnetic resonance spectroscopy findings of brain olfactory areas in patients with COVID-19-related anosmia: A preliminary comparative study. World J Otorhinolaryngol Head Neck Surg 2024; 10:105-112. [PMID: 38855283 PMCID: PMC11156679 DOI: 10.1002/wjo2.132] [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: 04/10/2023] [Revised: 06/01/2023] [Accepted: 06/19/2023] [Indexed: 06/11/2024] Open
Abstract
Objectives 2019 novel coronavirus disease (COVID-19) infection is commonly associated with olfactory dysfunctions, but the basic pathogenesis of these complications remains controversial. This study seeks to evaluate the value of magnetic resonance spectroscopy (MRS) in determining the molecular neurometabolite alterations within the main brain olfactory areas in patients with COVID-19-related anosmia. Methods In a cross-sectional study, seven patients with persistent COVID-19-related anosmia (mean age: 29.57 years) and seven healthy volunteers (mean age: 27.28 years) underwent MRS in which N-acetyl-aspartate (NAA), choline (Cho), creatine (Cr), and their ratios were measured in the anterior cingulate cortex, dorsolateral prefrontal cortex, orbitofrontal cortex (OFC), insular cortex, and ventromedial prefrontal cortex. Data were analyzed using TARQUIN software (version 4.3.10), and the results were compared with an independent sample t-test and nonparametric Mann-Whitney test based on the normality of the MRS data distribution. Results The mean duration of anosmia before imaging was 8.5 months in COVID-19-related anosmia group. MRS analysis elucidated a significant association between MRS findings within OFC and COVID-19-related anosmia (P disease < 0.01), and NAA was among the most important neurometabolites (P interaction = 0.006). Reduced levels of NAA (P < 0.001), Cr (P < 0.001) and NAA/Cho ratio (P = 0.007) within OFC characterize COVID-19-related anosmia. Conclusions This study emphasizes that MRS can be illuminating in COVID-19-related anosmia and indicates a possible association between central nervous system impairment and persistent COVID-19-related anosmia.
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Affiliation(s)
- Shadman Nemati
- Department of Otolaryngology and Head and Neck SurgerySchool of Medicine, Otorhinolaryngology Research Center, Guilan University of Medical SciencesRashtIran
| | - Mohammad Haghani Dogahe
- Department of Otolaryngology and Head and Neck SurgerySchool of Medicine, Otorhinolaryngology Research Center, Guilan University of Medical SciencesRashtIran
- Guilan Road Trauma Research CenterGuilan University of Medical SciencesRashtGuilanIran
| | - Alia Saberi
- Department of NeurologyNeuroscience Research Center, Poursina Hospital, Guilan University of Medical SciencesRashtIran
| | - Naghi Ramezani
- Department of RadiologyPars International HospitalGuilan ProvinceIran
| | - Pejman Kiani
- Department of RadiologyPars International HospitalGuilan ProvinceIran
- Department of Neuroscience and Addiction StudiesSchool of Advanced Technologies in MedicineTehranIran
| | - Tofigh Yaghubi Kalurazi
- Department of Infectious DiseasesRazi Clinical Research Development Unit, Razi Hospital, Guilan University of Medical SciencesRashtIran
| | | | - Sara Seddighi
- Guilan Road Trauma Research CenterGuilan University of Medical SciencesRashtGuilanIran
| | - Abbas Monsef
- Center of Magnetic Resonance Research, Department of Radiation OncologyUniversity of Minnesota Medical SchoolMinneapolisMinnesotaUSA
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9
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Argyropoulos GD, Christidi F, Karavasilis E, Bede P, Velonakis G, Antoniou A, Seimenis I, Kelekis N, Smyrnis N, Papakonstantinou O, Efstathopoulos E, Ferentinos P. A Magnetic Resonance Spectroscopy Study on Polarity Subphenotypes in Bipolar Disorder. Diagnostics (Basel) 2024; 14:1170. [PMID: 38893696 PMCID: PMC11172378 DOI: 10.3390/diagnostics14111170] [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: 04/26/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Although magnetic resonance spectroscopy (MRS) has provided in vivo measurements of brain chemical profiles in bipolar disorder (BD), there are no data on clinically and therapeutically important onset polarity (OP) and predominant polarity (PP). We conducted a proton MRS study in BD polarity subphenotypes, focusing on emotion regulation brain regions. Forty-one euthymic BD patients stratified according to OP and PP and sixteen healthy controls (HC) were compared. 1H-MRS spectra of the anterior and posterior cingulate cortex (ACC, PCC), left and right hippocampus (LHIPPO, RHIPPO) were acquired at 3.0T to determine metabolite concentrations. We found significant main effects of OP in ACC mI, mI/tNAA, mI/tCr, mI/tCho, PCC tCho, and RHIPPO tNAA/tCho and tCho/tCr. Although PP had no significant main effects, several medium and large effect sizes emerged. Compared to HC, manic subphenotypes (i.e., manic-OP, manic-PP) showed greater differences in RHIPPO and PCC, whereas depressive suphenotypes (i.e., depressive-OP, depressive-PP) in ACC. Effect sizes were consistent between OP and PP as high intraclass correlation coefficients (ICC) were confirmed. Our findings support the utility of MRS in the study of the neurobiological underpinnings of OP and PP, highlighting that the regional specificity of metabolite changes within the emotion regulation network consistently marks both polarity subphenotypes.
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Affiliation(s)
- Georgios D. Argyropoulos
- Research Unit of Radiology and Medical Imaging, 2nd Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece (E.K.); (G.V.); (N.K.); (O.P.); (E.E.)
| | - Foteini Christidi
- 2nd Department of Psychiatry, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (A.A.); (N.S.); (P.F.)
- School of Medicine, Democritus University of Alexandroupolis, 681 00 Alexandroupolis, Greece
- Computational Neuroimaging Group, Trinity College Dublin, D08 NHY1 Dublin, Ireland;
| | - Efstratios Karavasilis
- Research Unit of Radiology and Medical Imaging, 2nd Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece (E.K.); (G.V.); (N.K.); (O.P.); (E.E.)
- School of Medicine, Democritus University of Alexandroupolis, 681 00 Alexandroupolis, Greece
| | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, D08 NHY1 Dublin, Ireland;
- Department of Neurology, St James’s Hospital, D08 W9RT Dublin, Ireland
| | - Georgios Velonakis
- Research Unit of Radiology and Medical Imaging, 2nd Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece (E.K.); (G.V.); (N.K.); (O.P.); (E.E.)
| | - Anastasia Antoniou
- 2nd Department of Psychiatry, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (A.A.); (N.S.); (P.F.)
| | - Ioannis Seimenis
- Medical Physics Laboratory, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece;
| | - Nikolaos Kelekis
- Research Unit of Radiology and Medical Imaging, 2nd Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece (E.K.); (G.V.); (N.K.); (O.P.); (E.E.)
| | - Nikolaos Smyrnis
- 2nd Department of Psychiatry, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (A.A.); (N.S.); (P.F.)
| | - Olympia Papakonstantinou
- Research Unit of Radiology and Medical Imaging, 2nd Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece (E.K.); (G.V.); (N.K.); (O.P.); (E.E.)
| | - Efstathios Efstathopoulos
- Research Unit of Radiology and Medical Imaging, 2nd Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece (E.K.); (G.V.); (N.K.); (O.P.); (E.E.)
| | - Panagiotis Ferentinos
- 2nd Department of Psychiatry, School of Medicine, National and Kapodistrian University of Athens, 115 27 Athens, Greece; (A.A.); (N.S.); (P.F.)
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10
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Morelli M, Dudzikowska K, Deelchand DK, Quinn AJ, Mullins PG, Apps MAJ, Wilson M. Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594270. [PMID: 38798416 PMCID: PMC11118477 DOI: 10.1101/2024.05.15.594270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Background Functional MRS (fMRS) is a technique used to measure metabolic changes in response to increased neuronal activity, providing unique insights into neurotransmitter dynamics and neuroenergetics. In this study we investigate the response of lactate and glutamate levels in the motor cortex during a sustained motor task using conventional spectral fitting and explore the use of a novel analysis approach based on the application of linear modelling directly to the spectro-temporal fMRS data. Methods fMRS data were acquired at a field strength of 3 Tesla from 23 healthy participants using a short echo-time (28ms) semi-LASER sequence. The functional task involved rhythmic hand clenching over a duration of 8 minutes and standard MRS preprocessing steps, including frequency and phase alignment, were employed. Both conventional spectral fitting and direct linear modelling were applied, and results from participant-averaged spectra and metabolite-averaged individual analyses were compared. Results We observed a 20% increase in lactate in response to the motor task, consistent with findings at higher magnetic field strengths. However, statistical testing showed some variability between the two averaging schemes and fitting algorithms. While lactate changes were supported by the direct spectral modelling approach, smaller increases in glutamate (2%) were inconsistent. Exploratory spectral modelling identified a 4% decrease in aspartate, aligning with conventional fitting and observations from prolonged visual stimulation. Conclusion We demonstrate that lactate dynamics in response to a prolonged motor task are observed using short-echo time semi-LASER at 3 Tesla, and that direct linear modelling of fMRS data is a useful complement to conventional analysis. Future work includes mitigating spectral confounds, such as scalp lipid contamination and lineshape drift, and further validation of our novel direct linear modelling approach through experimental and simulated datasets.
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Affiliation(s)
- Maria Morelli
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Katarzyna Dudzikowska
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Dinesh K. Deelchand
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Andrew J. Quinn
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | | | - Matthew A. J. Apps
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
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11
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Gill SK, Rose HEL, Wilson M, Rodriguez Gutierrez D, Worthington L, Davies NP, MacPherson L, Hargrave DR, Saunders DE, Clark CA, Payne GS, Leach MO, Howe FA, Auer DP, Jaspan T, Morgan PS, Grundy RG, Avula S, Pizer B, Arvanitis TN, Peet AC. Characterisation of paediatric brain tumours by their MRS metabolite profiles. NMR IN BIOMEDICINE 2024; 37:e5101. [PMID: 38303627 DOI: 10.1002/nbm.5101] [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: 12/21/2022] [Revised: 11/20/2023] [Accepted: 12/04/2023] [Indexed: 02/03/2024]
Abstract
1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.
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Affiliation(s)
- Simrandip K Gill
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Heather E L Rose
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - Martin Wilson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
| | | | - Lara Worthington
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- Department of Imaging and Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Nigel P Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- Department of Imaging and Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Darren R Hargrave
- Paediatric Oncology Unit, Great Ormond Street Hospital For Sick Children, London, UK
| | - Dawn E Saunders
- Paediatric Oncology Unit, Great Ormond Street Hospital For Sick Children, London, UK
| | - Christopher A Clark
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Geoffrey S Payne
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Martin O Leach
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Franklyn A Howe
- Neurosciences Research Section, Molecular and Clinical Sciences Research Institute, St George's, University of London, London, UK
| | - Dorothee P Auer
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Radiological Sciences, Department of Clinical Neuroscience, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospital, Queen's Medical Centre, Nottingham, UK
| | - Tim Jaspan
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospital, Queen's Medical Centre, Nottingham, UK
| | - Paul S Morgan
- Medical Physics, Nottingham University Hospital, Queen's Medical Centre, Nottingham, UK
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Richard G Grundy
- The Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
| | - Shivaram Avula
- Department of Radiology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Barry Pizer
- Department of Paediatric Oncology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Theodoros N Arvanitis
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham, UK
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Birmingham Children's Hospital NHS Foundation Trust, Birmingham, UK
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12
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Stamatelatou A, Bertinetto CG, Jansen JJ, Postma G, Selnaes KM, Bathen TF, Heerschap A, Scheenen TWJ. A multivariate curve resolution analysis of multicenter proton spectroscopic imaging of the prostate for cancer localization and assessment of aggressiveness. NMR IN BIOMEDICINE 2024; 37:e5062. [PMID: 37920145 DOI: 10.1002/nbm.5062] [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: 08/01/2023] [Revised: 09/21/2023] [Accepted: 09/25/2023] [Indexed: 11/04/2023]
Abstract
In this study, we investigated the potential of the multivariate curve resolution alternating least squares (MCR-ALS) algorithm for analyzing three-dimensional (3D) 1 H-MRSI data of the prostate in prostate cancer (PCa) patients. MCR-ALS generates relative intensities of components representing spectral profiles derived from a large training set of patients, providing an interpretable model. Our objectives were to classify magnetic resonance (MR) spectra, differentiating tumor lesions from benign tissue, and to assess PCa aggressiveness. We included multicenter 3D 1 H-MRSI data from 106 PCa patients across eight centers. The patient cohort was divided into a training set (N = 63) and an independent test set (N = 43). Singular value decomposition determined that MR spectra were optimally represented by five components. The profiles of these components were extracted from the training set by MCR-ALS and assigned to specific tissue types. Using these components, MCR-ALS was applied to the test set for a quantitative analysis to discriminate tumor lesions from benign tissue and to assess tumor aggressiveness. Relative intensity maps of the components were reconstructed and compared with histopathology reports. The quantitative analysis demonstrated a significant separation between tumor and benign voxels (t-test, p < 0.001). This result was achieved including voxels with low-quality MR spectra. A receiver operating characteristic analysis of the relative intensity of the tumor component revealed that low- and high-risk tumor lesions could be distinguished with an area under the curve of 0.88. Maps of this component properly identified the extent of tumor lesions. Our study demonstrated that MCR-ALS analysis of 1 H-MRSI of the prostate can reliably identify tumor lesions and assess their aggressiveness. It handled multicenter data with minimal preprocessing and without using prior knowledge or quality control. These findings indicate that MCR-ALS can serve as an automated tool to assess the presence, extent, and aggressiveness of tumor lesions in the prostate, enhancing diagnostic capabilities and treatment planning of PCa patients.
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Affiliation(s)
- Angeliki Stamatelatou
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Jeroen J Jansen
- Department of Analytical Chemistry & Chemometrics, Radboud University, Nijmegen, The Netherlands
| | - Geert Postma
- Department of Analytical Chemistry & Chemometrics, Radboud University, Nijmegen, The Netherlands
| | - Kirsten Margrete Selnaes
- Department of Circulation and Medical Imaging, Norwegian University of Technology and Science, Trondheim, Norway
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Technology and Science, Trondheim, Norway
- Department of radiology and nuclear medicine, St. Olavs Hospital - Trondheim University Hospital, Trondheim, Norway
| | - Arend Heerschap
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tom W J Scheenen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
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13
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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: 3] [Impact Index Per Article: 3.0] [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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14
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Wintermark P, Lapointe A, Steinhorn R, Rampakakis E, Burhenne J, Meid AD, Bajraktari-Sylejmani G, Khairy M, Altit G, Adamo MT, Poccia A, Gilbert G, Saint-Martin C, Toffoli D, Vachon J, Hailu E, Colin P, Haefeli WE. Feasibility and Safety of Sildenafil to Repair Brain Injury Secondary to Birth Asphyxia (SANE-01): A Randomized, Double-blind, Placebo-controlled Phase Ib Clinical Trial. J Pediatr 2024; 266:113879. [PMID: 38142044 DOI: 10.1016/j.jpeds.2023.113879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/21/2023] [Accepted: 12/17/2023] [Indexed: 12/25/2023]
Abstract
OBJECTIVE To test feasibility and safety of administering sildenafil in neonates with neonatal encephalopathy (NE), developing brain injury despite therapeutic hypothermia (TH). STUDY DESIGN We performed a randomized, double-blind, placebo-controlled phase Ib clinical trial between 2016 and 2019 in neonates with moderate or severe NE, displaying brain injury on day-2 magnetic resonance imaging (MRI) despite TH. Neonates were randomized (2:1) to 7-day sildenafil or placebo (2 mg/kg/dose enterally every 12 hours, 14 doses). Outcomes included feasibility and safety (primary outcomes), pharmacokinetics (secondary), and day-30 neuroimaging and 18-month neurodevelopment assessments (exploratory). RESULTS Of the 24 enrolled neonates, 8 were randomized to sildenafil and 3 to placebo. A mild decrease in blood pressure was reported in 2 of the 8 neonates after initial dose, but not with subsequent doses. Sildenafil plasma steady-state concentration was rapidly reached, but decreased after TH discontinuation. Twelve percent of neonates (1/8) neonates died in the sildenafil group and 0% (0/3) in the placebo group. Among surviving neonates, partial recovery of injury, fewer cystic lesions, and less brain volume loss on day-30 magnetic resonance imaging were noted in 71% (5/7) of the sildenafil group and in 0% (0/3) of the placebo group. The rate of death or survival to 18 months with severe neurodevelopmental impairment was 57% (4/7) in the sildenafil group and 100% (3/3) in the placebo group. CONCLUSIONS Sildenafil was safe and well-absorbed in neonates with NE treated with TH. Optimal dosing needs to be established. Evaluation of a larger number of neonates through subsequent phases II and III trials is required to establish efficacy. CLINICAL TRIAL REGISTRATION ClinicalTrials.govNCT02812433.
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Affiliation(s)
- Pia Wintermark
- Division of Newborn Medicine, Department of Pediatrics, McGill University, Montreal, Quebec, Canada; Research Institute of the McGill University Health Center, McGill University, Montreal, Quebec, Canada.
| | - Anie Lapointe
- Department of Neonatology, Sainte-Justine Hospital, University of Montreal, Montreal, Quebec, Canada
| | - Robin Steinhorn
- Department of Pediatrics, University of California San Diego, and Rady Children's Hospital, San Diego, CA
| | | | - Jürgen Burhenne
- Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Andreas D Meid
- Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gzona Bajraktari-Sylejmani
- Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany
| | - May Khairy
- Department of Pediatrics, McGill University, Montreal, Québec, Canada
| | - Gabriel Altit
- Division of Newborn Medicine, Department of Pediatrics, McGill University, Montreal, Quebec, Canada; Research Institute of the McGill University Health Center, McGill University, Montreal, Quebec, Canada
| | - Marie-Therese Adamo
- Pharmacy Department, McGill University Health Center, Montreal, Québec, Canada
| | - Alishia Poccia
- Research Institute of the McGill University Health Center, McGill University, Montreal, Quebec, Canada
| | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare, Mississauga, Ontario, Canada
| | | | - Daniela Toffoli
- Department of Ophthalmology, McGill University, Montreal, Québec, Canada
| | - Julie Vachon
- Member of the Ordre des Psychologues du Quebec, Montreal, Québec, Canada
| | - Elizabeth Hailu
- Division of Newborn Medicine, Department of Pediatrics, McGill University, Montreal, Quebec, Canada
| | - Patrick Colin
- Patrick Colin Consultant Inc, Montreal, Québec, Canada
| | - Walter E Haefeli
- Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg, Germany
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15
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Craven AR, Bell TK, Ersland L, Harris AD, Hugdahl K, Oeltzschner G. Linewidth-related bias in modelled concentration estimates from GABA-edited 1H-MRS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582249. [PMID: 38464094 PMCID: PMC10925149 DOI: 10.1101/2024.02.27.582249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
J-difference-edited MRS is widely used to study GABA in the human brain. Editing for low-concentration target molecules (such as GABA) typically exhibits lower signal-to-noise ratio (SNR) than conventional non-edited MRS, varying with acquisition region, volume and duration. Moreover, spectral lineshape may be influenced by age-, pathology-, or brain-region-specific effects of metabolite T2, or by task-related blood-oxygen level dependent (BOLD) changes in functional MRS contexts. Differences in both SNR and lineshape may have systematic effects on concentration estimates derived from spectral modelling. The present study characterises the impact of lineshape and SNR on GABA+ estimates from different modelling algorithms: FSL-MRS, Gannet, LCModel, Osprey, spant and Tarquin. Publicly available multi-site GABA-edited data (222 healthy subjects from 20 sites; conventional MEGA-PRESS editing; TE = 68 ms) were pre-processed with a standardised pipeline, then filtered to apply controlled levels of Lorentzian and Gaussian linebroadening and SNR reduction. Increased Lorentzian linewidth was associated with a 2-5% decrease in GABA+ estimates per Hz, observed consistently (albeit to varying degrees) across datasets and most algorithms. Weaker, often opposing effects were observed for Gaussian linebroadening. Variations are likely caused by differing baseline parametrization and lineshape constraints between models. Effects of linewidth on other metabolites (e.g., Glx and tCr) varied, suggesting that a linewidth confound may persist after scaling to an internal reference. These findings indicate a potentially significant confound for studies where linewidth may differ systematically between groups or experimental conditions, e.g. due to T2 differences between brain regions, age, or pathology, or varying T2* due to BOLD-related changes. We conclude that linewidth effects need to be rigorously considered during experimental design and data processing, for example by incorporating linewidth into statistical analysis of modelling outcomes or development of appropriate lineshape matching algorithms.
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Affiliation(s)
- Alexander R. Craven
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Tiffany K. Bell
- Department of Radiology, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
| | - Lars Ersland
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Ashley D. Harris
- Department of Radiology, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada
| | - Kenneth Hugdahl
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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16
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Gordji-Nejad A, Matusch A, Kleedörfer S, Jayeshkumar Patel H, Drzezga A, Elmenhorst D, Binkofski F, Bauer A. Single dose creatine improves cognitive performance and induces changes in cerebral high energy phosphates during sleep deprivation. Sci Rep 2024; 14:4937. [PMID: 38418482 PMCID: PMC10902318 DOI: 10.1038/s41598-024-54249-9] [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: 11/22/2023] [Accepted: 02/10/2024] [Indexed: 03/01/2024] Open
Abstract
The inverse effects of creatine supplementation and sleep deprivation on high energy phosphates, neural creatine, and cognitive performances suggest that creatine is a suitable candidate for reducing the negative effects of sleep deprivation. With this, the main obstacle is the limited exogenous uptake by the central nervous system (CNS), making creatine only effective over a long-term diet of weeks. Thus far, only repeated dosing of creatine over weeks has been studied, yielding detectable changes in CNS levels. Based on the hypothesis that a high extracellular creatine availability and increased intracellular energy consumption will temporarily increase the central creatine uptake, subjects were orally administered a high single dose of creatinemonohydrate (0.35 g/kg) while performing cognitive tests during sleep deprivation. Two consecutive 31P-MRS scans, 1H-MRS, and cognitive tests were performed each at evening baseline, 3, 5.5, and 7.5 h after single dose creatine (0.35 g/kg) or placebo during sub-total 21 h sleep deprivation (SD). Our results show that creatine induces changes in PCr/Pi, ATP, tCr/tNAA, prevents a drop in pH level, and improves cognitive performance and processing speed. These outcomes suggest that a high single dose of creatine can partially reverse metabolic alterations and fatigue-related cognitive deterioration.
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Affiliation(s)
- Ali Gordji-Nejad
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, 52425, Jülich, Germany.
| | - Andreas Matusch
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Sophie Kleedörfer
- Division of Clinical Cognitive Sciences, Department of Neurology, RWTH Aachen University Hospital, 52074, Aachen, Germany
| | - Harshal Jayeshkumar Patel
- Division of Clinical Cognitive Sciences, Department of Neurology, RWTH Aachen University Hospital, 52074, Aachen, Germany
| | - Alexander Drzezga
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, 52425, Jülich, Germany
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
| | - David Elmenhorst
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, 52425, Jülich, Germany
- Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937, Cologne, Germany
| | - Ferdinand Binkofski
- Division of Clinical Cognitive Sciences, Department of Neurology, RWTH Aachen University Hospital, 52074, Aachen, Germany
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich, Jülich, Germany
| | - Andreas Bauer
- Institute of Neuroscience and Medicine (INM-2), Molecular Organization of the Brain, Forschungszentrum Jülich, 52425, Jülich, Germany
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17
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Xiao Y, Lanz B, Lim SI, 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 DOI: 10.1002/nbm.5056] [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: 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), Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bernard Lanz
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Song-I Lim
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ivan Tkáč
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Lijing Xin
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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18
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Dong QY, Cao YB, Huang HW, Li D, Lin Y, Chen HJ. Metabolic disorder and functional disturbance in the central executive network in minimal hepatic encephalopathy. Cereb Cortex 2024; 34:bhae036. [PMID: 38365269 DOI: 10.1093/cercor/bhae036] [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: 11/29/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 02/18/2024] Open
Abstract
The aim of this paper is to investigate dynamical functional disturbance in central executive network in minimal hepatic encephalopathy and determine its association with metabolic disorder and cognitive impairment. Data of magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging were obtained from 27 cirrhotic patients without minimal hepatic encephalopathy, 20 minimal hepatic encephalopathy patients, and 24 healthy controls. Central executive network was identified utilizing seed-based correlation approach. Dynamic functional connectivity across central executive network was calculated using sliding-window approach. Functional states were estimated by K-means clustering. Right dorsolateral prefrontal cortex metabolite ratios (i.e. glutamate and glutamine complex/total creatine, myo-inositol / total creatine, and choline / total creatine) were determined. Neurocognitive performance was determined by psychometric hepatic encephalopathy scores. Minimal hepatic encephalopathy patients had decreased myo-inositol / total creatine and choline / total creatine and increased glutamate and glutamine complex / total creatine in right dorsolateral prefrontal cortex (all P ≤ 0.020); decreased static functional connectivity between bilateral dorsolateral prefrontal cortex and between right dorsolateral prefrontal cortex and lateral-inferior temporal cortex (P ≤ 0.001); increased frequency and mean dwell time in state-1 (P ≤ 0.001), which exhibited weakest functional connectivity. Central executive network dynamic functional indices were significantly correlated with right dorsolateral prefrontal cortex metabolic indices and psychometric hepatic encephalopathy scores. Right dorsolateral prefrontal cortex myo-inositol / total creatine and mean dwell time in state-1 yielded best potential for diagnosing minimal hepatic encephalopathy. Dynamic functional disturbance in central executive network may contribute to neurocognitive impairment and could be correlated with metabolic disorder.
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Affiliation(s)
- Qiu-Yi Dong
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Yun-Bin Cao
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Hui-Wei Huang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Dan Li
- Department of Gastroenterology, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Yanqin Lin
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China
| | - Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
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19
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Bridgen P, Tomi-Tricot R, Uus A, Cromb D, Quirke M, Almalbis J, Bonse B, De la Fuente Botella M, Maggioni A, Cio PD, Cawley P, Casella C, Dokumaci AS, Thomson AR, Willers Moore J, Bridglal D, Saravia J, Finck T, Price AN, Pickles E, Cordero-Grande L, Egloff A, O’Muircheartaigh J, Counsell SJ, Giles SL, Deprez M, De Vita E, Rutherford MA, Edwards AD, Hajnal JV, Malik SJ, Arichi T. High resolution and contrast 7 tesla MR brain imaging of the neonate. FRONTIERS IN RADIOLOGY 2024; 3:1327075. [PMID: 38304343 PMCID: PMC10830693 DOI: 10.3389/fradi.2023.1327075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/29/2023] [Indexed: 02/03/2024]
Abstract
Introduction Ultra-high field MR imaging offers marked gains in signal-to-noise ratio, spatial resolution, and contrast which translate to improved pathological and anatomical sensitivity. These benefits are particularly relevant for the neonatal brain which is rapidly developing and sensitive to injury. However, experience of imaging neonates at 7T has been limited due to regulatory, safety, and practical considerations. We aimed to establish a program for safely acquiring high resolution and contrast brain images from neonates on a 7T system. Methods Images were acquired from 35 neonates on 44 occasions (median age 39 + 6 postmenstrual weeks, range 33 + 4 to 52 + 6; median body weight 2.93 kg, range 1.57 to 5.3 kg) over a median time of 49 mins 30 s. Peripheral body temperature and physiological measures were recorded throughout scanning. Acquired sequences included T2 weighted (TSE), Actual Flip angle Imaging (AFI), functional MRI (BOLD EPI), susceptibility weighted imaging (SWI), and MR spectroscopy (STEAM). Results There was no significant difference between temperature before and after scanning (p = 0.76) and image quality assessment compared favorably to state-of-the-art 3T acquisitions. Anatomical imaging demonstrated excellent sensitivity to structures which are typically hard to visualize at lower field strengths including the hippocampus, cerebellum, and vasculature. Images were also acquired with contrast mechanisms which are enhanced at ultra-high field including susceptibility weighted imaging, functional MRI, and MR spectroscopy. Discussion We demonstrate safety and feasibility of imaging vulnerable neonates at ultra-high field and highlight the untapped potential for providing important new insights into brain development and pathological processes during this critical phase of early life.
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Affiliation(s)
- Philippa Bridgen
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Raphael Tomi-Tricot
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare Limited, London, United Kingdom
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Megan Quirke
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jennifer Almalbis
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Beya Bonse
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Miguel De la Fuente Botella
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alessandra Maggioni
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Pierluigi Di Cio
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Paul Cawley
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Chiara Casella
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Ayse Sila Dokumaci
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alice R. Thomson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Jucha Willers Moore
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Devi Bridglal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joao Saravia
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Thomas Finck
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Anthony N. Price
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Elisabeth Pickles
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, ISCIII, Madrid, Spain
| | - Alexia Egloff
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sharon L. Giles
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Enrico De Vita
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Physics, Radiology Department, Great Ormond Street Hospital for Children, London, United Kingdom
| | - Mary A. Rutherford
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - A. David Edwards
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Shaihan J. Malik
- LondonCollaborative Ultra High Field System (LoCUS), King’s College London, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Tomoki Arichi
- Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
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20
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Chen X, Li J, Chen D, Zhou Y, Tu Z, Lin M, Kang T, Lin J, Gong T, Zhu L, Zhou J, Lin OY, Guo J, Dong J, Guo D, Qu X. CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 358:107601. [PMID: 38039654 DOI: 10.1016/j.jmr.2023.107601] [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: 09/06/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/03/2023]
Abstract
Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: (1) Automatically statistical analysis to find biomarkers for diseases; (2) Consistency verification between the classic and artificial intelligence quantification algorithms; (3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, data of both healthy subjects and patients with mild cognitive impairment are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing at least two years of free access and service. If you are interested, please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.
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Affiliation(s)
- Xiaodie Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Jiayu Li
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Dicheng Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Yirong Zhou
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Zhangren Tu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Meijin Lin
- Department of Applied Marine Physics & Engineering, Xiamen University, Xiamen, China
| | - Taishan Kang
- Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Jianzhong Lin
- Department of Radiology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
| | - Tao Gong
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Liuhong Zhu
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Ou-Yang Lin
- Department of Medical Imaging of Southeast Hospital, Medical College of Xiamen University, Xiamen, China
| | - Jiefeng Guo
- Department of Microelectronics and Integrated Circuit, Xiamen University, Xiamen, China
| | - Jiyang Dong
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Di Guo
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Xiaobo Qu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
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21
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Yu M, Lin L, Xu K, Xu M, Ren J, Niu X, Gao X, Zhang M, Yang Z, Dang J, Tao Q, Han S, Wang W, Cheng J, Zhang Y. Changes in aspartate metabolism in the medial-prefrontal cortex of nicotine addicts based on J-edited magnetic resonance spectroscopy. Hum Brain Mapp 2023; 44:6429-6438. [PMID: 37909379 PMCID: PMC10681642 DOI: 10.1002/hbm.26519] [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: 04/23/2023] [Revised: 09/05/2023] [Accepted: 10/04/2023] [Indexed: 11/03/2023] Open
Abstract
This study aims to explore the changes of the aspartate (Asp) level in the medial-prefrontal cortex (mPFC) of subjects with nicotine addiction (nicotine addicts [NAs]) using the J-edited 1 H MR spectroscopy (MRS), which may provide a positive imaging evidence for intervention of NA. From March to August 2022, 45 males aged 40-60 years old were recruited from Henan Province, including 21 in NA and 24 in nonsmoker groups. All subjects underwent routine magnetic resonance imaging (MRI) and J-edited MRS scans on a 3.0 T MRI scanner. The Asp level in mPFC was quantified with reference to the total creatine (Asp/Cr) and water (Aspwater-corr , with correction of the brain tissue composition) signals, respectively. Two-tailed independent samples t-test was used to analyze the differences in levels of Asp and other coquantified metabolites (including total N-acetylaspartate [tNAA], total cholinine [tCho], total creatine [tCr], and myo-Inositol [mI]) between the two groups. Finally, the correlations of the Asp level with clinical characteristic assessment scales were performed using the Spearman criteria. Compared with the control group (n = 22), NAs (n = 18) had higher levels of Asp (Asp/Cr: p = .005; Aspwater-corr : p = .004) in the mPFC, and the level of Asp was positively correlated with the daily smoking amount (Asp/Cr: p < .001; Aspwater-corr : p = .004). No significant correlation was found between the level of Asp and the years of nicotine use, Fagerstrom Nicotine Dependence (FTND), Russell Reason for Smoking Questionnaire (RRSQ), or Barratt Impulsivity Scale (BIS-11) score. The elevated Asp level was observed in mPFC of NAs in contrast to nonsmokers, and the Asp level was positively correlated with the amount of daily smoking, which suggests that nicotine addiction may result in elevated Asp metabolism in the human brain.
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Affiliation(s)
- Miaomiao Yu
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Liangjie Lin
- Clinical and Technical SupportPhilips HealthcareBeijingChina
| | - Ke Xu
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Man Xu
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Jianxin Ren
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Xiaoyu Niu
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Xinyu Gao
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Mengzhe Zhang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Zhengui Yang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Jinghan Dang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Qiuying Tao
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Shaoqiang Han
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Weijian Wang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Jingliang Cheng
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Yong Zhang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
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22
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Davies-Jenkins CW, Döring A, Fasano F, Kleban E, Mueller L, Evans CJ, Afzali M, Jones DK, Ronen I, Branzoli F, Tax CMW. Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients. Front Neurosci 2023; 17:1258408. [PMID: 38144210 PMCID: PMC10740196 DOI: 10.3389/fnins.2023.1258408] [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: 07/14/2023] [Accepted: 11/03/2023] [Indexed: 12/26/2023] Open
Abstract
Introduction Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure-compared to water-based methods alone-but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations. Methods In this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values. Results Simulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature. Discussion In conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR.
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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, United States
- Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - André Döring
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- CIBM Center for Biomedical Imaging, EPFL CIBM-AIT, EPFL Lausanne, Lausanne, Switzerland
| | - Fabrizio Fasano
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Siemens Healthcare Ltd., Camberly, United Kingdom
| | - Elena Kleban
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Department of Radiology, Universität Bern, Bern, Switzerland
| | - Lars Mueller
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - C. John Evans
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Itamar Ronen
- Clinical Sciences Institue, Brighton and Sussex Medical School, Brighton, United Kingdom
| | - Francesca Branzoli
- Center for NeuroImaging Research (CENIR), Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, Paris, France
- Inserm U1127, CNRS U7225, Sorbonne Universités, Paris, France
| | - Chantal M. W. Tax
- Brain Research Imaging Centre, School Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
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Zhang Y, Shen J. Quantification of spatially localized MRS by a novel deep learning approach without spectral fitting. Magn Reson Med 2023; 90:1282-1296. [PMID: 37183798 PMCID: PMC10524908 DOI: 10.1002/mrm.29711] [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: 10/14/2022] [Revised: 04/05/2023] [Accepted: 04/29/2023] [Indexed: 05/16/2023]
Abstract
PURPOSE To propose a novel end-to-end deep learning model to quantify absolute metabolite concentrations from in vivo J-point resolved spectroscopy (JPRESS) without using spectral fitting. METHODS A novel encoder-decoder-style neural network was created, which was trained to predict metabolite concentrations and individual component signals concurrently from 3T JPRESS data in the time domain. The training data set contained 100 000 samples created by spin-density simulations using experimentally used RF pulses. Concentrations, phase, frequencies, linewidths, and T2 relaxation times in the training data set were varied over a large range with uniform distributions. Random synthesized noise and extraneous signals were added to the data set. Two thousand validation samples were created similarly to the training data set but with mean concentrations close to in vivo values. An in vivo test was conducted with 20 samples acquired from the human brain. RESULTS Both validation and in vivo test results showed that the proposed model successfully predicted metabolite concentrations as well as individual metabolite signals without involving spectral fitting, while extraneous peaks or unregistered signals were filtered out. Compared with the short-TE spectral fitting by LCModel, the proposed method had the advantage that the undesired correlations between the estimated concentrations and noise levels and between metabolites were eliminated or substantially reduced. CONCLUSION The proposed method provides a working deep learning model that directly maps in vivo JPRESS data to metabolite concentrations. Because spectral fitting is not used, the trained model does not depend on the assumptions associated with parameter tuning when applied to in vivo data.
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Affiliation(s)
- Yan Zhang
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA
| | - Jun Shen
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA
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24
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Dogahe MH, Ramezani S, Reihanian Z, Raminfard S, Feizkhah A, Alijani B, Herfeh SS. Role of brain metabolites during acute phase of mild traumatic brain injury in prognosis of post-concussion syndrome: A 1H-MRS study. Psychiatry Res Neuroimaging 2023; 335:111709. [PMID: 37688998 DOI: 10.1016/j.pscychresns.2023.111709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 06/20/2023] [Accepted: 08/24/2023] [Indexed: 09/11/2023]
Abstract
This study has investigated the potency and accuracy of early magnetic resonance spectroscopy (MRS) to predict post-concussion syndrome (PCS) in adult patients with a single mild traumatic brain injury (mTBI) without abnormality on a routine brain scan. A total of 48 eligible mTBI patients and 24 volunteers in the control group participated in this project. Brain MRS over regions of interest (ROI) and signal stop task (SST) were done within the first 72 hours of TBI onset. After six months, PCS appearance and severity were determined. In non-PCS patients, N-acetyl aspartate (NAA) levels significantly increased in the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) relative to the control group, however, this increase of NAA levels were recorded in all ROI versus PCS subjects. There were dramatic declines in creatinine (Cr) levels of all ROI and a decrease in choline levels of corpus callosum (CC) in the PCS group versus control and non-PCS ones. NAA and NAA/Cho values in ACC were the main predictors of PCS appearance. The Cho/Cr level in ACC was the first predictor of PCS severity. Predicting accuracy was higher in ACC than in other regions. This study suggested the significance of neuro-markers in ACC for optimal prediction of PCS and rendered a new insight into the biological mechanism of mTBI that underpins PCS.
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Affiliation(s)
| | - Sara Ramezani
- Guilan Road Trauma Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Food Science and Nutrition, California State University, Fresno, CA, USA; Neuroscience Research Center, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
| | - Zoheir Reihanian
- Department of Neurosurgery, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | - Samira Raminfard
- Neuroimaging and Analysis Group, Research Center of Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Feizkhah
- Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Medical Physics, Guilan University of Medical Sciences, Rasht, Iran
| | - Babak Alijani
- Guilan Road Trauma Research Center, Guilan University of Medical Sciences, Rasht, Iran; Department of Neurosurgery, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | - Sina Sedaghat Herfeh
- Neuroscience Research Center, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
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25
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Koolschijn RS, Clarke WT, Ip IB, Emir UE, Barron HC. Event-related functional magnetic resonance spectroscopy. Neuroimage 2023; 276:120194. [PMID: 37244321 PMCID: PMC7614684 DOI: 10.1016/j.neuroimage.2023.120194] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/24/2023] [Indexed: 05/29/2023] Open
Abstract
Proton-Magnetic Resonance Spectroscopy (MRS) is a non-invasive brain imaging technique used to measure the concentration of different neurochemicals. "Single-voxel" MRS data is typically acquired across several minutes, before individual transients are averaged through time to give a measurement of neurochemical concentrations. However, this approach is not sensitive to more rapid temporal dynamics of neurochemicals, including those that reflect functional changes in neural computation relevant to perception, cognition, motor control and ultimately behaviour. In this review we discuss recent advances in functional MRS (fMRS) that now allow us to obtain event-related measures of neurochemicals. Event-related fMRS involves presenting different experimental conditions as a series of trials that are intermixed. Critically, this approach allows spectra to be acquired at a time resolution in the order of seconds. Here we provide a comprehensive user guide for event-related task designs, choice of MRS sequence, analysis pipelines, and appropriate interpretation of event-related fMRS data. We raise various technical considerations by examining protocols used to quantify dynamic changes in GABA, the primary inhibitory neurotransmitter in the brain. Overall, we propose that although more data is needed, event-related fMRS can be used to measure dynamic changes in neurochemicals at a temporal resolution relevant to computations that support human cognition and behaviour.
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Affiliation(s)
- Renée S Koolschijn
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands.
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom; Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - I Betina Ip
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom
| | - Uzay E Emir
- School of Health Sciences, Purdue University, West Lafayette, United States
| | - Helen C Barron
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom; Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom.
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van de Sande DMJ, Merkofer JP, Amirrajab S, Veta M, van Sloun RJG, Versluis MJ, Jansen JFA, van den Brink JS, Breeuwer M. A review of machine learning applications for the proton MR spectroscopy workflow. Magn Reson Med 2023. [PMID: 37402235 DOI: 10.1002/mrm.29793] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 07/06/2023]
Abstract
This literature review presents a comprehensive overview of machine learning (ML) applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the MR field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.
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Affiliation(s)
- Dennis M J van de Sande
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Julian P Merkofer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Sina Amirrajab
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Mitko Veta
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ruud J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Philips Research, Eindhoven, The Netherlands
| | | | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- MR R&D - Clinical Science, Philips Healthcare, Best, The Netherlands
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27
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Tzanetakos D, Kyrozis A, Karavasilis E, Velonakis G, Tzartos JS, Toulas P, Sotirli SA, Evdokimidis I, Tsivgoulis G, Potagas C, Kilidireas C, Andreadou E. Early metabolic alterations in the normal‑appearing grey and white matter of patients with clinically isolated syndrome suggestive of multiple sclerosis: A proton MR spectroscopic study. Exp Ther Med 2023; 26:349. [PMID: 37324507 PMCID: PMC10265702 DOI: 10.3892/etm.2023.12048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 04/18/2023] [Indexed: 06/17/2023] Open
Abstract
Proton magnetic resonance spectroscopy (1H-MRS) is an advanced method of examining metabolic profiles. The present study aimed to assess in vivo metabolite levels in areas of normal-appearing grey (thalamus) and white matter (centrum semiovale) using 1H-MRS in patients with clinically isolated syndrome (CIS) suggestive of multiple sclerosis and compare them to healthy controls (HCs). Data from 35 patients with CIS (CIS group), of which 23 were untreated (CIS-untreated group) and 12 were treated (CIS-treated group) with disease-modifying-therapies (DMTs) at the time of 1H-MRS, and from 28 age- and sex-matched HCs were collected using a 3.0 T MRI and single-voxel 1H-MRS (point resolved spectroscopy sequence; repetition time, 2,000 msec; time to echo, 35 msec). Concentrations and ratios of total N-acetyl aspartate (tNAA), total creatine (tCr), total choline (tCho), myoinositol, glutamate (Glu), glutamine (Gln), Glu + Gln (Glx) and glutathione (Glth) were estimated in the thalamic-voxel (th) and centrum semiovale-voxel (cs). For the CIS group, the median duration from the first clinical attack to 1H-MRS was 102 days (interquartile range, 89.5.-131.5). Compared with HCs, significantly lower Glx(cs) (P=0.014) and ratios of tCho/tCr(th) (P=0.026), Glu/tCr(cs) (P=0.040), Glx/tCr(cs) (P=0.004), Glx/tNAA(th) (P=0.043) and Glx/tNAA(cs) (P=0.015) were observed in the CIS group. No differences in tNAA levels were observed between the CIS and the HC groups; however, tNAA(cs) was higher in the CIS-treated than in the CIS-untreated group (P=0.028). Compared with those in HC group, decreased Glu(cs) (P=0.019) and Glx(cs) levels (P=0.014) and lower ratios for tCho/tCr(th) (P=0.015), Gln/tCr(th) (P=0.004), Glu/tCr(cs) (P=0.021), Glx/tCr(th) (P=0.041), Glx/tCr(cs) (P=0.003), Glx/tNAA(th) (P=0.030) and Glx/tNAA(cs) (P=0.015) were found in the CIS-untreated group. The present findings showed alterations in the normal-appearing grey and white matter of patients with CIS; moreover, the present results suggested an early indirect treatment effect of DMTs on the brain metabolic profile of these patients.
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Affiliation(s)
- Dimitrios Tzanetakos
- Second Department of Neurology, ‘Attikon’ University Hospital, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece
| | - Andreas Kyrozis
- First Department of Neurology, Eginition Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece
| | - Efstratios Karavasilis
- Research Unit of Radiology, Second Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece
- Medical Physics Laboratory, School of Medicine, Democritus University of Thrace, 68100 Alexandroupoli, Greece
| | - Georgios Velonakis
- Research Unit of Radiology, Second Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece
| | - John S. Tzartos
- Second Department of Neurology, ‘Attikon’ University Hospital, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece
| | - Panagiotis Toulas
- Research Unit of Radiology, Second Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece
| | - Stefania Alexia Sotirli
- MS Center and Other Neurodegenerative diseases, Metropolitan General Hospital, 15562 Holargos, Athens, Greece
| | - Ioannis Evdokimidis
- First Department of Neurology, Eginition Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece
| | - Georgios Tsivgoulis
- Second Department of Neurology, ‘Attikon’ University Hospital, School of Medicine, National and Kapodistrian University of Athens, 12462 Athens, Greece
| | - Constantin Potagas
- First Department of Neurology, Eginition Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece
| | - Costantinos Kilidireas
- First Department of Neurology, Eginition Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece
| | - Elisabeth Andreadou
- First Department of Neurology, Eginition Hospital, School of Medicine, National and Kapodistrian University of Athens, 11528 Athens, Greece
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Soher BJ, Semanchuk P, Todd D, Ji X, Deelchand D, Joers J, Oz G, Young K. Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis. Magn Reson Med 2023. [PMID: 37183778 DOI: 10.1002/mrm.29686] [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: 09/16/2022] [Revised: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 05/16/2023]
Abstract
PURPOSE The Vespa package (Versatile Simulation, Pulses, and Analysis) is described and demonstrated. It provides workflows for developing and optimizing linear combination modeling (LCM) fitting for 1 H MRS data using intuitive graphical user interface interfaces for RF pulse design, spectral simulation, and MRS data analysis. Command line interfaces for embedding workflows in MR manufacturer platforms and utilities for synthetic dataset creation are included. Complete provenance is maintained for all steps in workflows. THEORY AND METHODS Vespa is written in Python for compatibility across operating systems. It embeds the PyGAMMA spectral simulation library for spectral simulation. Multiprocessing methods accelerate processing and visualization. Applications use the Vespa database for results storage and cross-application access. Three projects demonstrate pulse, sequence, simulation, and data analysis workflows: (1) short TE semi-LASER single-voxel spectroscopy (SVS) LCM fitting, (2) optimizing MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) flip angle and LCM fitting, and (3) creating a synthetic short TE dataset. RESULTS The LCM workflows for in vivo basis set creation and spectral analysis showed reasonable results for both the short TE semi-LASER and MEGA-PRESS. Examples of pulses, simulations, and data fitting are shown in Vespa application interfaces for various steps to demonstrate the interactive workflow. CONCLUSION Vespa provides an efficient and extensible platform for characterizing RF pulses, pulse design, spectral simulation optimization, and automated LCM fitting via an interactive platform. Modular design and command line interface make it easy to embed in other platforms. As open source, it is free to the MRS community for use and extension. Vespa source code and documentation are available through GitHub.
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Affiliation(s)
- Brian J Soher
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Philip Semanchuk
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - David Todd
- Center for Imaging of Neurodegenerative Disorders, University of California, San Francisco, CA, USA
| | - Xiao Ji
- Center for Advanced MR Development, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Dinesh Deelchand
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Gulin Oz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Karl Young
- Department of Radiology, University of California, San Francisco, CA, USA
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29
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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: 7] [Impact Index Per Article: 7.0] [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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30
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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: 0] [Impact Index Per Article: 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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31
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Baek HM. Experimental Basis Sets of Quantification of Brain 1H-Magnetic Resonance Spectroscopy at 3.0 T. Metabolites 2023; 13:metabo13030368. [PMID: 36984808 PMCID: PMC10056301 DOI: 10.3390/metabo13030368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/09/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
In vivo short echo time (TE) proton magnetic resonance spectroscopy (1H-MRS) is a useful method for the quantification of human brain metabolites. The purpose of this study was to evaluate the performance of an in-house, experimentally measured basis set and compare it with the performance of a vendor-provided basis set. A 3T clinical scanner with 32-channel receive-only phased array head coil was used to generate 16 brain metabolites for the metabolite basis set. For voxel localization, point-resolved spin-echo sequence (PRESS) was used with volume of interest (VOI) positioned at the center of the phantoms. Two different basis sets were subjected to linear combination of model spectra of metabolite solutions in vitro (LCModel) analysis to evaluate the in-house acquired in vivo 1H-MR spectra from the left prefrontal cortex of 22 healthy subjects. To evaluate the performance of the two basis sets, the Cramer-Rao lower bounds (CRLBs) of each basis set were compared. The LCModel quantified the following metabolites and macromolecules: alanine (Ala), aspartate (Asp), γ-amino butyric acid (GABA), glucose (Glc), glutamine (Gln), glutamate (Glu), glutathione (GHS), Ins (myo-Inositol), lactate (Lac), N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), taurine (Tau), phosphoryl-choline + glycerol-phosphoryl-choline (tCho), N-acetylaspartate + N-acetylaspartylglutamate (tNA), creatine + phosphocreatine (tCr), Glu + Gln (Glx) and Lip13a, Lip13b, Lip09, MM09, Lip20, MM20, MM12, MM14, MM17, Lip13a + Lip13b, MM14 + Lip13a + Lip13b + MM12, MM09 + Lip09, MM20 + Lip20. Statistical analysis showed significantly different CRLBs: Asp, GABA, Gln, GSH, Ins, Lac, NAA, NAAG, Tau, tCho, tNA, Glx, MM20, MM20 + Lip20 (p < 0.001), tCr, MM12, MM17 (p < 0.01), and Lip20 (p < 0.05). The estimated ratio of cerebrospinal fluid (CSF) in the region of interest was calculated to be about 5%. Fitting performances are better, for the most part, with the in-house basis set, which is more precise than the vendor-provided basis set. In particular, Asp is expected to have reliable CRLB (<30%) at high field (e.g., 3T) in the left prefrontal cortex of human brain. The quantification of Asp was difficult, due to the inaccuracy of Asp fitting with the vendor-provided basis set.
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Affiliation(s)
- Hyeon-Man Baek
- Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon 21999, Republic of Korea; ; Tel.: +82-32-899-6678
- Department of Molecular Medicine, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Republic of Korea
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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: 8] [Impact Index Per Article: 8.0] [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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Hippocampal Metabolic Alterations in Amyotrophic Lateral Sclerosis: A Magnetic Resonance Spectroscopy Study. Life (Basel) 2023; 13:life13020571. [PMID: 36836928 PMCID: PMC9965919 DOI: 10.3390/life13020571] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Magnetic resonance spectroscopy (MRS) in amyotrophic lateral sclerosis (ALS) has been overwhelmingly applied to motor regions to date and our understanding of frontotemporal metabolic signatures is relatively limited. The association between metabolic alterations and cognitive performance in also poorly characterised. MATERIAL AND METHODS In a multimodal, prospective pilot study, the structural, metabolic, and diffusivity profile of the hippocampus was systematically evaluated in patients with ALS. Patients underwent careful clinical and neurocognitive assessments. All patients were non-demented and exhibited normal memory performance. 1H-MRS spectra of the right and left hippocampi were acquired at 3.0T to determine the concentration of a panel of metabolites. The imaging protocol also included high-resolution T1-weighted structural imaging for subsequent hippocampal grey matter (GM) analyses and diffusion tensor imaging (DTI) for the tractographic evaluation of the integrity of the hippocampal perforant pathway zone (PPZ). RESULTS ALS patients exhibited higher hippocampal tNAA, tNAA/tCr and tCho bilaterally, despite the absence of volumetric and PPZ diffusivity differences between the two groups. Furthermore, superior memory performance was associated with higher hippocampal tNAA/tCr bilaterally. Both longer symptom duration and greater functional disability correlated with higher tCho levels. CONCLUSION Hippocampal 1H-MRS may not only contribute to a better academic understanding of extra-motor disease burden in ALS, but given its sensitive correlations with validated clinical metrics, it may serve as practical biomarker for future clinical and clinical trial applications. Neuroimaging protocols in ALS should incorporate MRS in addition to standard structural, functional, and diffusion sequences.
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Tal A. The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches. Magn Reson Med 2023; 89:499-507. [PMID: 36121336 PMCID: PMC10087547 DOI: 10.1002/mrm.29456] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/01/2022] [Accepted: 08/23/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Many MRS paradigms produce 2D spectral-temporal datasets, including diffusion-weighted, functional, and hyperpolarized and enriched (carbon-13, deuterium) experiments. Conventionally, temporal parameters-such as T2 , T1 , or diffusion constants-are assessed by first fitting each spectrum independently and subsequently fitting a temporal model (1D fitting). We investigated whether simultaneously fitting the entire dataset using a single spectral-temporal model (2D fitting) would improve the precision of the relevant temporal parameter. METHODS We derived a Cramer Rao lower bound for the temporal parameters for both 1D and 2D approaches for 2 experiments: a multi-echo experiment designed to estimate metabolite T2 s, and a functional MRS experiment designed to estimate fractional change ( δ $$ \delta $$ ) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T2 in multi-echo and δ $$ \delta $$ in functional MRS. RESULTS When peaks were spectrally distant, 2D fitting improved precision by approximately 20% relative to 1D fitting, regardless of the experiment and other parameter values. These gains increased exponentially as peaks drew closer. Dependence on temporal model parameters was weak to negligible. CONCLUSION Our results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as hydrogen and deuterium, which exhibit substantial spectral overlap.
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Affiliation(s)
- Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
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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] [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,*Correspondence: Brenda Bartnik-Olson ✉
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Prostko P, Pikkemaat J, Selter P, Lukaschek M, Wechselberger R, Khamiakova T, Valkenborg D. R Shiny App for the Automated Deconvolution of NMR Spectra to Quantify the Solid-State Forms of Pharmaceutical Mixtures. Metabolites 2022; 12:metabo12121248. [PMID: 36557287 PMCID: PMC9786300 DOI: 10.3390/metabo12121248] [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: 10/01/2022] [Revised: 12/02/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Bioavailability and chemical stability are important characteristics of drug products that are strongly affected by the solid-state structure of the active pharmaceutical ingredient (API). In pharmaceutical development and quality control activities, solid-state NMR (ssNMR) has proved to be an excellent tool for the detection and accurate quantification of undesired solid-state forms. To obtain correct quantitative outcomes, the resulting spectrum of an analytical sample should be deconvoluted into the individual spectra of the pure components. However, the ssNMR deconvolution is particularly challenging due to the following: the relatively large line widths that may lead to severe peak overlap, multiple spinning sidebands as a result of applying Magic Angle Spinning (MAS), and highly irregular peak shapes commonly observed in mixture spectra. To address these challenges, we created a tailored and automated deconvolution approach of ssNMR mixture spectra that involves a linear combination modelling (LCM) of previously acquired reference spectra of pure solid-state components. For optimal model performance, the template and mixture spectra should be acquired under the same conditions and experimental settings. In addition to the parameters controlling the contributions of the components in the mixture, the proposed model includes terms for spectral processing such as phase correction and horizontal shifting that are all jointly estimated via a non-linear, constrained optimisation algorithm. Finally, our novel procedure has been implemented in a fully functional and user-friendly R Shiny webtool (hence no local R installation required) that offers interactive data visualisations, manual adjustments to the automated deconvolution results, and the traceability and reproducibility of analyses.
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Affiliation(s)
- Piotr Prostko
- Data Science Institute, UHasselt—Hasselt University, Agoralaan 1, BE 3590 Diepenbeek, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan 1, BE 3590 Diepenbeek, Belgium
| | - Jeroen Pikkemaat
- Janssen Pharmaceutica, Department of Analytical Development, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Philipp Selter
- Janssen Pharmaceutica, Department of Analytical Development, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Michail Lukaschek
- Janssen Pharmaceutica, Department of Analytical Development, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Rainer Wechselberger
- Janssen Pharmaceutica, Department of Analytical Development, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Tatsiana Khamiakova
- Janssen Pharmaceutica, Manufacturing and Applied Statistics, Turnhoutseweg 30, BE 2340 Beerse, Belgium
| | - Dirk Valkenborg
- Data Science Institute, UHasselt—Hasselt University, Agoralaan 1, BE 3590 Diepenbeek, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan 1, BE 3590 Diepenbeek, Belgium
- Correspondence:
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Gong T, Hui SCN, Zöllner HJ, Britton M, Song Y, Chen Y, Gudmundson AT, Hupfeld KE, Davies-Jenkins CW, Murali-Manohar S, Porges EC, Oeltzschner G, Chen W, Wang G, Edden RAE. Neurometabolic timecourse of healthy aging. Neuroimage 2022; 264:119740. [PMID: 36356822 PMCID: PMC9902072 DOI: 10.1016/j.neuroimage.2022.119740] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/20/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The neurometabolic timecourse of healthy aging is not well-established, in part due to diversity of quantification methodology. In this study, a large structured cross-sectional cohort of male and female subjects throughout adulthood was recruited to investigate neurometabolic changes as a function of age, using consensus-recommended magnetic resonance spectroscopy quantification methods. METHODS 102 healthy volunteers, with approximately equal numbers of male and female participants in each decade of age from the 20s, 30s, 40s, 50s, and 60s, were recruited with IRB approval. MR spectroscopic data were acquired on a 3T MRI scanner. Metabolite spectra were acquired using PRESS localization (TE=30 ms; 96 transients) in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Water-suppressed spectra were modeled using the Osprey algorithm, employing a basis set of 18 simulated metabolite basis functions and a cohort-mean measured macromolecular spectrum. Pearson correlations were conducted to assess relationships between metabolite concentrations and age for each voxel; Spearman correlations were conducted where metabolite distributions were non-normal. Paired t-tests were run to determine whether metabolite concentrations differed between the PCC and CSO. Finally, robust linear regressions were conducted to assess both age and sex as predictors of metabolite concentrations in the PCC and CSO and separately, to assess age, signal-noise ratio, and full width half maximum (FWHM) linewidth as predictors of metabolite concentrations. RESULTS Data from four voxels were excluded (2 ethanol; 2 unacceptably large lipid signal). Statistically-significant age*metabolite Pearson correlations were observed for tCho (r(98)=0.33, p<0.001), tCr (r(98)=0.60, p<0.001), and mI (r(98)=0.32, p=0.001) in the CSO and for NAAG (r(98)=0.26, p=0.008), tCho(r(98)=0.33, p<0.001), tCr (r(98)=0.39, p<0.001), and Gln (r(98)=0.21, p=0.034) in the PCC. Spearman correlations for non-normal variables revealed a statistically significant correlation between sI and age in the CSO (r(86)=0.26, p=0.013). No significant correlations were seen between age and tNAA, NAA, Glx, Glu, GSH, PE, Lac, or Asp in either region (all p>0.20). Age associations for tCho, tCr, mI and sI in the CSO and for NAAG, tCho, and tCr in the PCC remained when controlling for sex in robust regressions. CSO NAAG and Asp, as well as PCC tNAA, sI, and Lac were higher in women; PCC Gln was higher in men. When including an age*sex interaction term in robust regression models, a significant age*sex interaction was seen for tCho (F(1,96)=11.53, p=0.001) and GSH (F(1,96)=7.15, p=0.009) in the CSO and tCho (F(1,96)=9.17, p=0.003), tCr (F(1,96)=9.59, p=0.003), mI (F(1,96)=6.48, p=0.012), and Lac (F(1,78)=6.50, p=0.016) in the PCC. In all significant interactions, metabolite levels increased with age in females, but not males. There was a significant positive correlation between linewidth and age. Age relationships with tCho, tCr, and mI in the CSO and tCho, tCr, mI, and sI in the PCC were significant after controlling for linewidth and FWHM in robust regressions. CONCLUSION The primary (correlation) results indicated age relationships for tCho, tCr, mI, and sI in the CSO and for NAAG, tCho, tCr, and Gln in the PCC, while no age correlations were found for tNAA, NAA, Glx, Glu, GSH, PE, Lac, or Asp in either region. Our results provide a normative foundation for future work investigating the neurometabolic time course of healthy aging using MRS.
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Affiliation(s)
- Tao Gong
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Departments of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
| | - Steve C N Hui
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - Helge J Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - Mark Britton
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, United States of America; McKnight Brain Research Foundation, University of Florida, FL, United States of America; Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, United States of America
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - Yufan Chen
- Departments of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China
| | - Aaron T Gudmundson
- Department of Neurobiology and Behavior, University of California, Irvine, CA, United States of America
| | - Kathleen E Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - Christopher W Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - Eric C Porges
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, United States of America; McKnight Brain Research Foundation, University of Florida, FL, United States of America; Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, United States of America
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | | | - Guangbin Wang
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Departments of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China.
| | - Richard A E Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
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Wang KL, Liang K, Wang LJ, Shen JF, Zhu GH, Zhang SX, Wang XZ, Wang Y, Wang YY. The association of glutamate level in pregenual anterior cingulate, anhedonia, and emotion-behavior decoupling in patients with major depressive disorder. Asian J Psychiatr 2022; 78:103306. [PMID: 36308992 DOI: 10.1016/j.ajp.2022.103306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/13/2022] [Accepted: 10/17/2022] [Indexed: 11/30/2022]
Abstract
Impairments of translating emotional salience into effortful behavior are core features of anhedonia in cohorts with major depressive disorder. Glutamate metabolism is considered to be involved in this process, but the empirical study is relatively few. Therefore, the present study aimed to examine the correlations between glutamate level in pregenual anterior cingulate, anhedonia, and emotion-behavior decoupling in patients with major depressive disorder. Fifteen individuals diagnosed with major depressive disorder and ten healthy individuals were recruited. All participants were asked to complete self-report instruments for anhedonia and the computerized anticipatory and consummatory pleasure task, and the in vivo glutamate levels were measured by proton magnetic resonance spectroscopy. Thus, a potential lower glutamate levels in pregenual anterior cingulate in individuals with major depressive disorder were founded to be positively correlated with the ability of pleasure experiencing. The mechanism of glutamate in pregenual anterior cingulate in anhedonia in patients with major depressive disorder may be reflected in the early pleasurable experience stage, rather than in the transformation of emotional experience to motivation or reward-seeking behavior, which may be different from that in schizophrenia.
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Affiliation(s)
- Kui-Lai Wang
- School of Psychology, Weifang Medical University, Shandong 261053, China
| | - Kun Liang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Li-Jun Wang
- School of Psychology, Weifang Medical University, Shandong 261053, China
| | - Jian-Fei Shen
- School of Psychology, Weifang Medical University, Shandong 261053, China
| | - Guo-Hui Zhu
- Mental Health Centre of Weifang City, Shandong 261071, China
| | - Shu-Xian Zhang
- Magnetic Resonance Imaging Centre, Affiliated Hospital of Weifang Medical University, Shandong 261031, China
| | - Xi-Zhen Wang
- Magnetic Resonance Imaging Centre, Affiliated Hospital of Weifang Medical University, Shandong 261031, China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100048, China.
| | - Yan-Yu Wang
- School of Psychology, Weifang Medical University, Shandong 261053, China.
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Jelen LA, Green MS, King S, Morris AG, Zhang X, Lythgoe DJ, Young AH, De Belleroche J, Stone JM. Variants in the zinc transporter-3 encoding gene (SLC30A3) in schizophrenia and bipolar disorder: Effects on brain glutamate-A pilot study. Front Psychiatry 2022; 13:929306. [PMID: 36203844 PMCID: PMC9531870 DOI: 10.3389/fpsyt.2022.929306] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/23/2022] [Indexed: 11/25/2022] Open
Abstract
Zinc transporter 3 (ZnT3) has been implicated in the aetiopathology of schizophrenia. In this pilot study, we tested the hypothesis that the presence of a minor allele of two variants in the gene encoding ZnT3 (SLC30A3) affects brain glutamate and cognitive activity in patients with schizophrenia and bipolar affective disorder. Fifteen patients with schizophrenia (SCZ), 15 with bipolar affective disorder type 2 (BD), and 14 healthy volunteers (HV) were genotyped for two SLC30A3 single nucleotide polymorphisms (rs11126936 and rs11126929). They also underwent structural and functional MRI (n-back) imaging as well as static (PRESS) and functional magnetic resonance spectroscopy (n-back) on a 3 Tesla MRI system. SCZ with at least one copy of the minor allele showed reductions in dorsal anterior cingulate cortex glutamate during the n-back task, whereas SCZ without the minor allele showed an increase in glutamate. BD with the minor allele had reduced glutamate in the anterior cingulate cortex (p < 0.05). There was no effect of SLC30A3 genotype on BOLD activation during n-back or on cortical brain volume. This study supports the further investigation of SLC30A3 and its role in glutamatergic neurotransmission and in the neuropathology of mental illness.
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Affiliation(s)
- Luke A. Jelen
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, United Kingdom
| | - Mark S. Green
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Sinead King
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Psychiatry and Behavioural Sciences, Stanford University, Palo Alto, CA, United States
- Centre for Neuroimaging and Cognitive Genomics, National University of Ireland, Galway, Ireland
| | - Alex G. Morris
- Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - Xinyuan Zhang
- Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | - David J. Lythgoe
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Allan H. Young
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, United Kingdom
| | | | - James M. Stone
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, United Kingdom
- Psychiatry, Department of Neuroscience, Brighton and Sussex Medical School (BSMS), University of Sussex, Brighton, United Kingdom
- Department of Psychiatry, Sussex Partnership Foundation Trust, Eastbourne DGH, Eastbourne, United Kingdom
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40
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Does the change in glutamate to GABA ratio correlate with change in depression severity? A randomized, double-blind clinical trial. Mol Psychiatry 2022; 27:3833-3841. [PMID: 35982258 PMCID: PMC9712215 DOI: 10.1038/s41380-022-01730-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/09/2022] [Accepted: 07/29/2022] [Indexed: 02/08/2023]
Abstract
Previous proton magnetic resonance spectroscopy (1H-MRS) studies suggest a perturbation in glutamate and/or GABA in Major Depressive Disorder (MDD). However, no studies examine the ratio of glutamate and glutamine (Glx) to GABA (Glx/GABA) as it relates to depressive symptoms, which may be more sensitive than either single metabolite. Using a within-subject design, we hypothesized that reduction in depressive symptoms correlates with reduction in Glx/GABA in the anterior cingulate cortex (ACC). The present trial is a randomized clinical trial that utilized 1H-MRS to examine Glx/GABA before and after 8 weeks of escitalopram or placebo. Participants completed the 17-item Hamilton Depression Rating Scale (HDRS17) and underwent magnetic resonance spectroscopy before and after treatment. Two GABA-edited MEGA-PRESS acquisitions were interleaved with a water unsuppressed reference scan. GABA and Glx were quantified from the average difference spectrum, with preprocessing using Gannet and spectral fitting using TARQUIN. Linear mixed models were utilized to evaluate relationships between change in HDRS17 and change in Glx/GABA using a univariate linear regression model, multiple linear regression incorporating treatment type as a covariate, and Bayes Factor (BF) hypothesis testing to examine strength of evidence. No significant relationship was detected between percent change in Glx, GABA, or Glx/GABA and percent change in HDRS17, regardless of treatment type. Further, MDD severity before/after treatment did not correlate with ACC Glx/GABA. In light of variable findings in the literature and lack of association in our investigation, future directions should include evaluating glutamate and glutamine individually to shed light on the underpinnings of MDD severity. Advancing Personalized Antidepressant Treatment Using PET/MRI, ClinicalTrials.gov, NCT02623205.
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41
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Stamatelatou A, Scheenen TWJ, Heerschap A. Developments in proton MR spectroscopic imaging of prostate cancer. MAGMA (NEW YORK, N.Y.) 2022; 35:645-665. [PMID: 35445307 PMCID: PMC9363347 DOI: 10.1007/s10334-022-01011-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/04/2022] [Accepted: 03/22/2022] [Indexed: 10/25/2022]
Abstract
In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.
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Affiliation(s)
- Angeliki Stamatelatou
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
| | - Tom W J Scheenen
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Arend Heerschap
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
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42
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Craven AR, Bhattacharyya PK, Clarke WT, Dydak U, Edden RAE, Ersland L, Mandal PK, Mikkelsen M, Murdoch JB, Near J, Rideaux R, Shukla D, Wang M, Wilson M, Zöllner HJ, Hugdahl K, Oeltzschner G. Comparison of seven modelling algorithms for γ-aminobutyric acid-edited proton magnetic resonance spectroscopy. NMR IN BIOMEDICINE 2022; 35:e4702. [PMID: 35078266 PMCID: PMC9203918 DOI: 10.1002/nbm.4702] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/18/2022] [Accepted: 01/20/2022] [Indexed: 06/01/2023]
Abstract
Edited MRS sequences are widely used for studying γ-aminobutyric acid (GABA) in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multisite study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An interclass correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.
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Affiliation(s)
- Alexander R. Craven
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
- Department of Clinical EngineeringHaukeland University HospitalBergenNorway
- NORMENT Center of ExcellenceHaukeland University HospitalBergenNorway
| | | | - William T. Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- MRC Brain Network Dynamics UnitUniversity of OxfordOxfordUK
| | - Ulrike Dydak
- School of Health SciencesPurdue UniversityIndianaWest LafayetteUSA
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological ScienceThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
- F. M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA
| | - Lars Ersland
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
- Department of Clinical EngineeringHaukeland University HospitalBergenNorway
| | - Pravat K. Mandal
- NeuroImaging and NeuroSpectroscopy (NINS) Laboratory, National Brain Research CentreGurgaonIndia
- Florey Institute of Neuroscience and Mental HealthParkvilleMelbourneVictoriaAustralia
| | - Mark Mikkelsen
- Russell H. Morgan Department of Radiology and Radiological ScienceThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
- F. M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA
- Department of RadiologyWeill Cornell MedicineNew YorkNew YorkUSA
| | | | - Jamie Near
- Centre d'Imagerie CérébraleDouglas Mental Health University InstituteMontrealCanada
- Department of Biomedical EngineeringMcGill UniversityMontrealCanada
- Department of PsychiatryMcGill UniversityMontrealCanada
| | - Reuben Rideaux
- Queensland Brain InstituteThe University of QueenslandBrisbaneAustralia
| | - Deepika Shukla
- NeuroImaging and NeuroSpectroscopy (NINS) Laboratory, National Brain Research CentreGurgaonIndia
- Perinatal Trials Unit FoundationBengaluruIndia
- Centre for Perinatal NeuroscienceImperial College LondonLondonUK
| | - Min Wang
- College of Biomedical Engineering and Instrument ScienceZhejiang UniversityHangzhouChina
| | - Martin Wilson
- Centre for Human Brain Health and School of PsychologyUniversity of BirminghamBirminghamUK
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological ScienceThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
- F. M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA
| | - Kenneth Hugdahl
- Department of Biological and Medical PsychologyUniversity of BergenBergenNorway
- Division of PsychiatryHaukeland University HospitalBergenNorway
- Department of RadiologyHaukeland University HospitalBergenNorway
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological ScienceThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
- F. M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA
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43
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Zhao D, Grist JT, Rose HEL, Davies NP, Wilson M, MacPherson L, Abernethy LJ, Avula S, Pizer B, Gutierrez DR, Jaspan T, Morgan PS, Mitra D, Bailey S, Sawlani V, Arvanitis TN, Sun Y, Peet AC. Metabolite selection for machine learning in childhood brain tumour classification. NMR IN BIOMEDICINE 2022; 35:e4673. [PMID: 35088473 DOI: 10.1002/nbm.4673] [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: 02/04/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 06/14/2023]
Abstract
MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N-acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.
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Affiliation(s)
- Dadi Zhao
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - James T Grist
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - Heather E L Rose
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
| | - Nigel P Davies
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- Imaging and Medical Physics, University Hospitals Birmingham, Birmingham, UK
| | - Martin Wilson
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | | | | | | | - Barry Pizer
- Paediatric Oncology, Alder Hey Children's Hospital, Liverpool, UK
| | - Daniel R Gutierrez
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Medical Physics, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Tim Jaspan
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Neuroradiology, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Paul S Morgan
- Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, UK
- Medical Physics, Nottingham University Hospitals NHS Trust, Nottingham, UK
- Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Dipayan Mitra
- Neuroradiology, The Newcastle upon Tyne Hospitals, Newcastle upon Tyne, UK
| | - Simon Bailey
- Paediatric Oncology, Great North Children's Hospital, Newcastle upon Tyne, UK
| | - Vijay Sawlani
- Radiology, Queen Elizabeth Hospital Birmingham, Birmingham, UK
- School of Psychology, University of Birmingham, Birmingham, UK
| | - Theodoros N Arvanitis
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
| | - Yu Sun
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
- University of Birmingham and Southeast University Joint Research Centre for Biomedical Engineering, Suzhou, China
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Oncology, Birmingham Children's Hospital, Birmingham, UK
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44
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Martinello KA, Meehan C, Avdic-Belltheus A, Lingam I, Mutshiya T, Yang Q, Akin MA, Price D, Sokolska M, Bainbridge A, Hristova M, Tachtsidis I, Tann CJ, Peebles D, Hagberg H, Wolfs TGAM, Klein N, Kramer BW, Fleiss B, Gressens P, Golay X, Robertson NJ. Hypothermia is not therapeutic in a neonatal piglet model of inflammation-sensitized hypoxia-ischemia. Pediatr Res 2022; 91:1416-1427. [PMID: 34050269 PMCID: PMC8160560 DOI: 10.1038/s41390-021-01584-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/20/2021] [Accepted: 05/10/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND Perinatal inflammation combined with hypoxia-ischemia (HI) exacerbates injury in the developing brain. Therapeutic hypothermia (HT) is standard care for neonatal encephalopathy; however, its benefit in inflammation-sensitized HI (IS-HI) is unknown. METHODS Twelve newborn piglets received a 2 µg/kg bolus and 1 µg/kg/h infusion over 52 h of Escherichia coli lipopolysaccharide (LPS). HI was induced 4 h after LPS bolus. After HI, piglets were randomized to HT (33.5 °C 1-25 h after HI, n = 6) or normothermia (NT, n = 6). Amplitude-integrated electroencephalogram (aEEG) was recorded and magnetic resonance spectroscopy (MRS) was acquired at 24 and 48 h. At 48 h, terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL)-positive brain cell death, microglial activation/proliferation, astrogliosis, and cleaved caspase-3 (CC3) were quantified. Hematology and plasma cytokines were serially measured. RESULTS Two HT piglets died. aEEG recovery, thalamic and white matter MRS lactate/N-acetylaspartate, and TUNEL-positive cell death were similar between groups. HT increased microglial activation in the caudate, but had no other effect on glial activation/proliferation. HT reduced CC3 overall. HT suppressed platelet count and attenuated leukocytosis. Cytokine profile was unchanged by HT. CONCLUSIONS We did not observe protection with HT in this piglet IS-HI model based on aEEG, MRS, and immunohistochemistry. Immunosuppressive effects of HT and countering neuroinflammation by LPS may contribute to the observed lack of HT efficacy. Other immunomodulatory strategies may be more effective in IS-HI. IMPACT Acute infection/inflammation is known to exacerbate perinatal brain injury and can worsen the outcomes in neonatal encephalopathy. Therapeutic HT is the current standard of care for all infants with NE, but the benefit in infants with coinfection/inflammation is unknown. In a piglet model of inflammation (LPS)-sensitized HI, we observed no evidence of neuroprotection with cooling for 24 h, based on our primary outcome measures: aEEG, MRS Lac/NAA, and histological brain cell death. Additional neuroprotective agents, with beneficial immunomodulatory effects, require exploration in IS-HI models.
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Affiliation(s)
- Kathryn A Martinello
- Institute for Women's Health, University College London, London, UK
- Robinson Research Institute, University of Adelaide, Adelaide, SA, Australia
| | | | | | - Ingran Lingam
- Institute for Women's Health, University College London, London, UK
| | - Tatenda Mutshiya
- Institute for Women's Health, University College London, London, UK
| | - Qin Yang
- Institute for Women's Health, University College London, London, UK
| | - Mustafa Ali Akin
- Department of Paediatrics, Ondokuz Mayıs University, Samsun, Turkey
| | - David Price
- Medical Physics and Biomedical Engineering, University College London NHS Foundation Trust, London, UK
| | - Magdalena Sokolska
- Medical Physics and Biomedical Engineering, University College London NHS Foundation Trust, London, UK
| | - Alan Bainbridge
- Medical Physics and Biomedical Engineering, University College London NHS Foundation Trust, London, UK
| | - Mariya Hristova
- Institute for Women's Health, University College London, London, UK
| | - Ilias Tachtsidis
- Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Cally J Tann
- Adolescent, Reproductive and Child Health Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Donald Peebles
- Institute for Women's Health, University College London, London, UK
| | - Henrik Hagberg
- Department of Clinical Sciences, Centre of Perinatal Medicine and Health, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
- Centre for the Developing Brain, Kings College London, London, UK
| | - Tim G A M Wolfs
- Department of Pediatrics, University of Maastricht, Maastricht, The Netherlands
| | - Nigel Klein
- Paediatric Infectious Diseases and Immunology, Institute of Child Health, University College London, London, UK
| | - Boris W Kramer
- Department of Pediatrics, University of Maastricht, Maastricht, The Netherlands
| | - Bobbi Fleiss
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC, Australia
- Université de Paris, NeuroDiderot, Inserm, Paris, France
| | | | - Xavier Golay
- Institute of Neurology, University College London, London, UK
| | - Nicola J Robertson
- Institute for Women's Health, University College London, London, UK.
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
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45
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Vike NL, Bari S, Susnjar A, Lee T, Lycke RJ, Auger J, Music J, Nauman E, Talavage TM, Rispoli J. American football position-specific neurometabolic changes in high school athletes - a magnetic resonance spectroscopic study. J Neurotrauma 2022; 39:1168-1182. [PMID: 35414265 DOI: 10.1089/neu.2021.0186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Reports estimate between 1.6-3.8 million sports-related concussions occur annually, with 30% occurring in youth male American football athletes. Many studies report neurophysiological changes in these athletes, but the exact reasons for these changes remain elusive. Investigation of injury mechanics highlights a need to address how player position might impact these changes. Here, 55 high school American football athletes (20 linemen; 35 non-linemen) underwent magnetic resonance spectroscopy four times over the course of a football season (once prior to the season (Pre), twice during (In1, In2), and once following (Post)) to quantify metabolites (N-acetyl aspartate, choline, creatine, myo-inositol, and glutamate/glutamine) in the dorsolateral prefrontal cortex (DLPFC) and primary motor cortex (M1). Head acceleration events (HAEs) were monitored at each practice and game. Spectroscopic and HAE data were analyzed by imaging session and player position. Linear regression analyses were conducted between metabolite levels and HAEs, and metabolite levels in football athletes were compared to age-and gender-matched non-contact athletes. Across-season (i.e., between Pre and In1, In2, Post), different DLPFC and M1 metabolites decreased (p<0.05) according to player position (i.e., linemen vs. non-linemen). The majority of regression results involved DLPFC metabolites in linemen, where metabolite levels were higher, from Pre to Post, with increasing HAE load. Comparisons with control athletes revealed higher metabolite levels in football athletes both before and after the season. This study highlights the importance of player position when conducting analyses on American football athletes and demonstrates elevated DLPFC and M1 brain metabolites in football athletes compared to control athletes at both Pre and Post, suggesting potential HAE-related neurocompensatory mechanisms.
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Affiliation(s)
- Nicole L Vike
- Northwestern University, 3270, Chicago, Illinois, United States.,Purdue University, 311308, West Lafayette, Indiana, United States;
| | - Sumra Bari
- Northwestern University, 3270, Chicago, Illinois, United States.,Purdue University, 311308, West Lafayette, Indiana, United States;
| | - Antonia Susnjar
- Purdue University, 311308, West Lafayette, Indiana, United States;
| | - Taylor Lee
- Purdue University, 311308, West Lafayette, Indiana, United States;
| | - Roy J Lycke
- Purdue University, 311308, Weldon School of Biomedical Engineering, West Lafayette, Indiana, United States;
| | - Joshua Auger
- Purdue University, 311308, West Lafayette, Indiana, United States;
| | - Jacob Music
- Purdue University, 311308, West Lafayette, Indiana, United States;
| | - Eric Nauman
- Purdue University, School of Mechanical Engineering, West Lafayette, Indiana, United States.,University of Cincinnati, 2514, Cincinnati, Ohio, United States;
| | - Thomas M Talavage
- Purdue University, 311308, West Lafayette, Indiana, United States.,University of Cincinnati, 2514, Cincinnati, Ohio, United States;
| | - Joseph Rispoli
- Purdue University, 311308, West Lafayette, Indiana, United States;
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46
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Tomiyasu M, Harada M. In vivo Human MR Spectroscopy Using a Clinical Scanner: Development, Applications, and Future Prospects. Magn Reson Med Sci 2022; 21:235-252. [PMID: 35173095 PMCID: PMC9199975 DOI: 10.2463/mrms.rev.2021-0085] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
MR spectroscopy (MRS) is a unique and useful method for noninvasively evaluating biochemical metabolism in human organs and tissues, but its clinical dissemination has been slow and often limited to specialized institutions or hospitals with experts in MRS technology. The number of 3-T clinical MR scanners is now increasing, representing a major opportunity to promote the use of clinical MRS. In this review, we summarize the theoretical background and basic knowledge required to understand the results obtained with MRS and introduce the general consensus on the clinical utility of proton MRS in routine clinical practice. In addition, we present updates to the consensus guidelines on proton MRS published by the members of a working committee of the Japan Society of Magnetic Resonance in Medicine in 2013. Recent research into multinuclear MRS equipped in clinical MR scanners is explained with an eye toward future development. This article seeks to provide an overview of the current status of clinical MRS and to promote the understanding of when it can be useful. In the coming years, MRS-mediated biochemical evaluation is expected to become available for even routine clinical practice.
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Affiliation(s)
- Moyoko Tomiyasu
- Department of Molecular Imaging and Theranostics, National Institutes for Quantum Science and Technology.,Department of Radiology, Kanagawa Children's Medical Center
| | - Masafumi Harada
- Department of Radiology and Radiation Oncology, Graduate School of Biomedical Sciences, Tokushima University
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Nucera S, Ruga S, Cardamone A, Coppoletta AR, Guarnieri L, Zito MC, Bosco F, Macrì R, Scarano F, Scicchitano M, Maiuolo J, Carresi C, Mollace R, Cariati L, Mazzarella G, Palma E, Gliozzi M, Musolino V, Cascini GL, Mollace V. MAFLD progression contributes to altered thalamus metabolism and brain structure. Sci Rep 2022; 12:1207. [PMID: 35075185 PMCID: PMC8786899 DOI: 10.1038/s41598-022-05228-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 01/07/2022] [Indexed: 12/02/2022] Open
Abstract
Metabolic associated fatty liver disease (MAFLD), commonly known as non-alcoholic fatty liver disease, represents a continuum of events characterized by excessive hepatic fat accumulation which can progress to nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and in some severe cases hepatocellular carcinoma. MAFLD might be considered as a multisystem disease that affects not only the liver but involves wider implications, relating to several organs and systems, the brain included. The present study aims to investigate changes associated with MAFLD-induced alteration of thalamic metabolism in vivo. DIAMOND (Diet-induced animal model of non-alcoholic fatty liver disease) mice were fed a chow diet and tap water (NC NW) or fat Western Diet (WD SW) for up to 28 weeks. At the baseline and weeks 4, 8, 20, 28 the thalamic neurochemical profile and total cerebral brain volume were evaluated longitudinally in both diet groups using 1H-MRS. To confirm the disease progression, at each time point, a subgroup of animals was sacrificed, the livers excised and placed in formalin. Liver histology was assessed and reviewed by an expert liver pathologist. MAFLD development significantly increases the thalamic levels of total N-acetylaspartate, total creatine, total choline, and taurine. Furthermore, in the WD SW group a reduction in total cerebral brain volume has been observed (p < 0.05 vs NC NW). Our results suggest that thalamic energy metabolism is affected by MAFLD progression. This metabolic imbalance, that is quantifiable by 1H-MRS in vivo, might cause structural damage to brain cells and dysfunctions of neurotransmitter release.
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Affiliation(s)
- Saverio Nucera
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Stefano Ruga
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Antonio Cardamone
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Anna Rita Coppoletta
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Lorenza Guarnieri
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Maria Caterina Zito
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Francesca Bosco
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Roberta Macrì
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Federica Scarano
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Miriam Scicchitano
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Jessica Maiuolo
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Cristina Carresi
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Rocco Mollace
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Luca Cariati
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Giuseppe Mazzarella
- Nuclear Medicine Unit, Department of Diagnostic Imaging, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Ernesto Palma
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Micaela Gliozzi
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy.
| | - Vincenzo Musolino
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Giuseppe Lucio Cascini
- Nuclear Medicine Unit, Department of Diagnostic Imaging, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
| | - Vincenzo Mollace
- Institute of Research for Food Safety and Health IRC-FSH, Department of Health Sciences, University Magna Graecia of Catanzaro, 88100, Catanzaro, Italy
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Wu B, Bagshaw AP, Hickey C, Kühn S, Wilson M. Evidence for distinct neuro-metabolic phenotypes in humans. Neuroimage 2022; 249:118902. [PMID: 35033676 DOI: 10.1016/j.neuroimage.2022.118902] [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: 05/28/2021] [Revised: 01/05/2022] [Accepted: 01/09/2022] [Indexed: 11/17/2022] Open
Abstract
Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however, remains largely unexplored - despite the potential for the discovery of novel behavioural and disease phenotypes. In this study, we measured 14 metabolite levels, normalised as ratios to total-creatine, with 1H magnetic resonance spectroscopy (MRS) acquired from the bilateral anterior cingulate cortices of six healthy participants, repeatedly over a period of four months. ANOVA tests revealed statistically significant differences of 3 metabolites and 3 commonly used combinations (total-choline, glutamate + glutamine and total-N-acetylaspartate) between the participants, with scyllo-inositol (F=85, p=6e-26) and total-choline (F=39, p=1e-17) having the greatest discriminatory power. This was not attributable to structural differences. When predicting individuals from the repeated MRS measurements, a leave-one-out classification accuracy of 88% was achieved using a support vector machine based on scyllo-inositol and total-choline levels. Accuracy increased to 98% with the addition of total-N-acetylaspartate and myo-inositol - demonstrating the efficacy of combining MRS with machine learning and metabolomic methodology. These results provide evidence for the existence of neuro-metabolic phenotypes, which may be non-invasively measured using widely available 3 Tesla MRS. Establishing these phenotypes in a larger cohort and investigating their connection to brain health and function presents an important area for future study.
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Affiliation(s)
- Bofan Wu
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Andrew P Bagshaw
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Clayton Hickey
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, Neuronal Plasticity Working Group, University Medical Center Hamburg-Eppendorf, Germany; Max Planck Institute for Human Development, Lise Meitner Group for Environmental Neuroscience, Germany
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.
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Ostojic SM, Ostojic J, Zanini D, Jezdimirovic T, Stajer V. Guanidinoacetate-creatine in secondary progressive multiple sclerosis: a case report. J Int Med Res 2022; 50:3000605211073305. [PMID: 35000485 PMCID: PMC8753084 DOI: 10.1177/03000605211073305] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Acute secondary progressive multiple sclerosis (SPMS) is characterized by escalating neurological disability, with limited disease-modifying therapeutic options. A 48-year-old woman with acute SPMS being treated with interferon beta-1a and oral corticosteroids presented as a clinical outpatient with no disease-modifying effects after treatment. A decision was made to treat her with a combination of guanidinoacetate and creatine for 21 days. She had made clinical progress at follow-up, with the intensity of fatigue dropping from severe to mild. Magnetic resonance spectroscopy revealed increased brain choline, creatine, N-acetylaspartate, and glutathione. Patients with SPMS may benefit from guanidinoacetate-creatine treatment in terms of patient- and clinician-reported outcomes; this requires additional study.
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Affiliation(s)
- Sergej M Ostojic
- FSPE Applied Bioenergetics Lab, 84981University of Novi Sad, University of Novi Sad, Novi Sad, Serbia.,Faculty of Health Sciences, University of Pécs, Pécs, Hungary.,Department of Nutrition and Public Health, University of Agder, Kristiansand, Norway
| | - Jelena Ostojic
- Faculty of Medicine, 84981University of Novi Sad, University of Novi Sad, Novi Sad, Serbia
| | - Dragana Zanini
- FSPE Applied Bioenergetics Lab, 84981University of Novi Sad, University of Novi Sad, Novi Sad, Serbia
| | - Tatjana Jezdimirovic
- FSPE Applied Bioenergetics Lab, 84981University of Novi Sad, University of Novi Sad, Novi Sad, Serbia
| | - Valdemar Stajer
- FSPE Applied Bioenergetics Lab, 84981University of Novi Sad, University of Novi Sad, Novi Sad, Serbia
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Hui SC, Zöllner HJ, Oeltzschner G, Edden RAE, Saleh MG. In vivo spectral editing of phosphorylethanolamine. Magn Reson Med 2022; 87:50-56. [PMID: 34411324 PMCID: PMC8616810 DOI: 10.1002/mrm.28976] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/07/2021] [Accepted: 07/29/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To demonstrate J-difference editing of phosphorylethanolamine (PE) with chemical shifts at 3.22 (PE3.22 ) and 3.98 (PE3.98 ) ppm, and compare the merits of two editing strategies. METHODS Density-matrix simulations of MEGA-PRESS (Mescher-Garwood PRESS) for PE were performed at TEs ranging from 80 to 200 ms in steps of 2 ms, applying 20-ms editing pulses (ON/OFF) at (1) 3.98/7.5 ppm to detect PE3.22 and (2) 3.22/7.5 ppm to detect PE3.98 . Phantom experiments were performed using a PE phantom to validate simulation results. Ten subjects were scanned using a Philips 3T MRI scanner at TEs of 90 ms and 110 ms to edit PE3.22 and PE3.98 . Osprey was used for data processing, modeling, and quantification. RESULTS Simulations show substantial TE modulation of the intensity and shape of the edited signals due to coupling evolution. Simulated and phantom integrals suggest that TEs of 110 ms and 90 ms were optimal for the edited detection of PE3.22 and PE3.98 , respectively. Phantom results indicated strong agreement with the simulated spectra and integrals. In vivo quantification of the PE3.22 /total creatine and PE3.98 /total creatine concentration ratio yielded values of 0.26 ± 0.04 (between-subject coefficient of variation [CV]: 15.4%) and 0.18 ± 0.04 (CV: 22.8%), respectively, at TE = 90 ms, and 0.24 ± 0.02 (CV: 8.2%) and 0.23 ± 0.04 (CV: 18.0%), respectively, at TE = 110 ms. CONCLUSION Simulations and in vivo MEGA-PRESS of PE demonstrate that both PE3.22 and PE3.98 are potential candidates for editing, but PE3.22 at TE = 110 ms yields lower variation across TEs.
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Affiliation(s)
- Steve C.N. Hui
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Muhammad G. Saleh
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, USA
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