1
|
Hui SCN, Murali-Manohar S, Zöllner HJ, Hupfeld KE, Davies-Jenkins CW, Gudmundson AT, Song Y, Yedavalli V, Wisnowski JL, Gagoski B, Oeltzschner G, Edden RAE. Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for Advanced MRS. bioRxiv 2024:2024.02.15.580516. [PMID: 38659947 PMCID: PMC11042202 DOI: 10.1101/2024.02.15.580516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Background To examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study. Methods ISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5±6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T 2 correction based on the dual-TE water integrals were compared with: 1) T 2 correction based the default white matter and gray matter T 2 reference values in Osprey; 2) shorter WM and GM T 2 values from recent literature; and 3) reduced CSF fractions. Results No significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T 2 correction based on shorter T 2 values showed better agreement to the dual-TE water integral ratio. Conclusions ISTHMUS facilitated and standardized acquisition and post-processing and reduced operator workload to eliminate potential human error. Highlights ISTHMUS has been implemented into the HBCD study protocol.It acquires both short-TE and Hadamard-edited transients.ISTHMUS reduces operator workload.ISTHMUS potentially allows improved T2 relaxation correction.
Collapse
|
2
|
Murali-Manohar S, Gudmundson AT, Hupfeld KE, Zöllner HJ, Hui SCN, Song Y, Simicic D, Davies-Jenkins CW, Gong T, Wang G, Oeltzschner G, Edden RAE. Metabolite T 1 relaxation times decrease across the adult lifespan. NMR Biomed 2024:e5152. [PMID: 38565525 DOI: 10.1002/nbm.5152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 01/08/2024] [Accepted: 03/02/2024] [Indexed: 04/04/2024]
Abstract
Relaxation correction is an integral step in quantifying brain metabolite concentrations measured by in vivo magnetic resonance spectroscopy (MRS). While most quantification routines assume constant T1 relaxation across age, it is possible that aging alters T1 relaxation rates, as is seen for T2 relaxation. Here, we investigate the age dependence of metabolite T1 relaxation times at 3 T in both gray- and white-matter-rich voxels using publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3 T using Point RESolved Spectroscopy (PRESS) localization. Data were acquired from voxels in the posterior cingulate cortex (PCC) and centrum semiovale (CSO) in 102 healthy volunteers across 5 decades of life (aged 20-69 years). All spectra were analyzed in Osprey v.2.4.0. To estimate T1 relaxation times for total N-acetyl aspartate at 2.0 ppm (tNAA2.0) and total creatine at 3.0 ppm (tCr3.0), the ratio of modeled metabolite residual amplitudes in the metabolite-nulled spectrum to the full metabolite signal was calculated using the single-inversion-recovery signal equation. Correlations between T1 and subject age were evaluated. Spearman correlations revealed that estimated T1 relaxation times of tNAA2.0 (rs = -0.27; p < 0.006) and tCr3.0 (rs = -0.40; p < 0.001) decreased significantly with age in white-matter-rich CSO, and less steeply for tNAA2.0 (rs = -0.228; p = 0.005) and (not significantly for) tCr3.0 (rs = -0.13; p = 0.196) in graymatter-rich PCC. The analysis harnessed a large publicly available cross-sectional dataset to test an important hypothesis, that metabolite T1 relaxation times change with age. This preliminary study stresses the importance of further work to measure age-normed metabolite T1 relaxation times for accurate quantification of metabolite levels in studies of aging.
Collapse
Affiliation(s)
- Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Aaron T Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Kathleen E Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Helge J Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Steve C N Hui
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Dunja Simicic
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Christopher W Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Tao Gong
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Departments of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Guangbin Wang
- Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Departments of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Richard A E Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| |
Collapse
|
3
|
Simicic D, Zöllner HJ, Davies-Jenkins CW, Hupfeld KE, Edden RAE, Oeltzschner G. Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling. bioRxiv 2024:2024.03.26.586804. [PMID: 38585798 PMCID: PMC10996641 DOI: 10.1101/2024.03.26.586804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Purpose Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a two-dimensional linear-combination model (2D-LCM) of individual transients ('model-based FPC'). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates. Methods We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the standard deviation of those ground-truth errors, and amplitude Cramér Rao Lower Bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data. Results 2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of frequency and phase correction and amplitudes performed substantially better at low-to-very-low SNR. Conclusion Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, e.g., long TEs or strong diffusion weighting.
Collapse
Affiliation(s)
- Dunja Simicic
- 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
| | - 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
| | - Kathleen E. Hupfeld
- 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
| | - 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
| |
Collapse
|
4
|
Song Y, Hupfeld KE, Davies-Jenkins CW, Zöllner HJ, Murali-Manohar S, Mumuni AN, Crocetti D, Yedavalli V, Oeltzschner G, Alessi N, Batschelett MA, Puts NA, Mostofsky SH, Edden RA. Brain glutathione and GABA+ levels in autistic children. Autism Res 2024; 17:512-528. [PMID: 38279628 PMCID: PMC10963146 DOI: 10.1002/aur.3097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/28/2023] [Indexed: 01/28/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication challenges and repetitive behaviors. Altered neurometabolite levels, including glutathione (GSH) and gamma-aminobutyric acid (GABA), have been proposed as potential contributors to the biology underlying ASD. This study investigated whether cerebral GSH or GABA levels differ between a cohort of children aged 8-12 years with ASD (n = 52) and typically developing children (TDC, n = 49). A comprehensive analysis of GSH and GABA levels in multiple brain regions, including the primary motor cortex (SM1), thalamus (Thal), medial prefrontal cortex (mPFC), and supplementary motor area (SMA), was conducted using single-voxel HERMES MR spectroscopy at 3T. The results revealed no significant differences in cerebral GSH or GABA levels between the ASD and TDC groups across all examined regions. These findings suggest that the concentrations of GSH (an important antioxidant and neuromodulator) and GABA (a major inhibitory neurotransmitter) do not exhibit marked alterations in children with ASD compared to TDC. A statistically significant positive correlation was observed between GABA levels in the SM1 and Thal regions with ADHD inattention scores. No significant correlation was found between metabolite levels and hyper/impulsive scores of ADHD, measures of core ASD symptoms (ADOS-2, SRS-P) or adaptive behavior (ABAS-2). While both GSH and GABA have been implicated in various neurological disorders, the current study provides valuable insights into the specific context of ASD and highlights the need for further research to explore other neurochemical alterations that may contribute to the pathophysiology of this complex disorder.
Collapse
Affiliation(s)
- Yulu Song
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| | - Kathleen E. Hupfeld
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| | - Helge J. Zöllner
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| | | | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Vivek Yedavalli
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Georg Oeltzschner
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| | - Natalie Alessi
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Mitchell A. Batschelett
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Nicolaas A.J. Puts
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
- MRC Center for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Stewart H. Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Richard A.E. Edden
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| |
Collapse
|
5
|
Hui SC, Zöllner HJ, Gong T, Hupfeld KE, Gudmundson AT, Murali-Manohar S, Davies-Jenkins CW, Song Y, Chen Y, Oeltzschner G, Wang G, Edden RAE. sLASER and PRESS perform similarly at revealing metabolite-age correlations at 3 T. Magn Reson Med 2024; 91:431-442. [PMID: 37876339 PMCID: PMC10942734 DOI: 10.1002/mrm.29895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 10/03/2023] [Accepted: 10/04/2023] [Indexed: 10/26/2023]
Abstract
PURPOSE To compare the respective ability of PRESS and sLASER to reveal biological relationships, using age as a validation covariate at 3 T. METHODS MRS data were acquired from 102 healthy volunteers using PRESS and sLASER in centrum semiovale and posterior cingulate cortex (PCC). Acquisition parameters included TR/TE = 2000/30 ms, 96 transients, and 2048 datapoints sampled at 2 kHz. Spectra were analyzed using Osprey. SNR, FWHM linewidth of total creatine, and metabolite concentrations were extracted. A linear model was used to compare SNR and linewidth. Paired t-tests were used to assess differences in metabolite measurements between PRESS and sLASER. Correlations were used to evaluate the relationship between PRESS and sLASER metabolite estimates, as well as the strength of each metabolite-age relationship. Coefficients of variation were calculated to assess inter-subject variability in each metabolite measurement. RESULTS SNR and linewidth were significantly higher (p < 0.01) for sLASER than PRESS in PCC. Paired t-tests showed significant differences between PRESS and sLASER in most metabolite measurements. PRESS-sLASER measurements were significantly correlated (p < 0.05) for most metabolites. Metabolite-age relationships were consistently identified using both methods. Similar coefficients of variation were observed for most metabolites. CONCLUSION The study results suggest strong agreement between PRESS and sLASER in identifying relationships between brain metabolites and age in centrum semiovale and PCC data acquired at 3 T. sLASER is technically desirable due to the reduced chemical shift displacement artifact; however, PRESS performed similarly in homogeneous brain regions at clinical field strength.
Collapse
Affiliation(s)
- Steve C.N. Hui
- 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
- Developing Brain Institute, Children’s National Hospital, Washington, DC, 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
| | - Tao Gong
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - 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
| | - Aaron T. Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - 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
| | - 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
| | - Yufan Chen
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - 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
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - 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
| |
Collapse
|
6
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
7
|
Song Y, Hupfeld KE, Davies-Jenkins CW, Zöllner HJ, Murali-Manohar S, Mumuni AN, Crocetti D, Yedavalli V, Oeltzschner G, Alessi N, Batschelett MA, Puts NAJ, Mostofsky SH, Edden RAE. Brain Glutathione and GABA+ levels in autistic children. bioRxiv 2023:2023.09.28.559718. [PMID: 37808813 PMCID: PMC10557661 DOI: 10.1101/2023.09.28.559718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication challenges and repetitive behaviors. Altered neurometabolite levels, including glutathione (GSH) and gamma-aminobutyric acid (GABA), have been proposed as potential contributors to the biology underlying ASD. This study investigated whether cerebral GSH or GABA levels differ between a large cohort of children aged 8-12 years with ASD (n=52) and typically developing children (TDC, n=49). A comprehensive analysis of GSH and GABA levels in multiple brain regions, including the primary motor cortex (SM1), thalamus (Thal), medial prefrontal cortex (mPFC), and supplementary motor area (SMA), was conducted using single-voxel HERMES MR spectroscopy at 3T. The results revealed no significant differences in cerebral GSH or GABA levels between the ASD and TDC groups across all examined regions. These findings suggest that the concentrations of GSH (an important antioxidant and neuromodulator) and GABA (a major inhibitory neurotransmitter) do not exhibit marked alterations in children with ASD compared to TDC. A statistically significant positive correlation was observed between GABA levels in the SM1 and Thal regions with ADHD inattention scores. No significant correlation was found between metabolite levels and hyper/impulsive scores of ADHD, measures of core ASD symptoms (ADOS-2, SRS-P) or adaptive behavior (ABAS-2). While both GSH and GABA have been implicated in various neurological disorders, the current study provides valuable insights into the specific context of ASD and highlights the need for further research to explore other neurochemical alterations that may contribute to the pathophysiology of this complex disorder.
Collapse
Affiliation(s)
- Yulu Song
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| | - Kathleen E Hupfeld
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| | - Helge J Zöllner
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| | | | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Vivek Yedavalli
- The Russel H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Georg Oeltzschner
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| | - Natalie Alessi
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Mitchell A Batschelett
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Nicolaas A J Puts
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
- MRC Center for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Richard A E Edden
- The Russel H. Morgan Department of Radiology and Radiological Science, 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
| |
Collapse
|
8
|
Gudmundson AT, Davies-Jenkins CW, Özdemir İ, Murali-Manohar S, Zöllner HJ, Song Y, Hupfeld KE, Schnitzler A, Oeltzschner G, Stark CEL, Edden RAE. Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts. bioRxiv 2023:2023.05.08.539813. [PMID: 37215030 PMCID: PMC10197548 DOI: 10.1101/2023.05.08.539813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.
Collapse
Affiliation(s)
- Aaron T. Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Christopher W. Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - İpek Özdemir
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Kathleen E. Hupfeld
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Craig E. L. Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| |
Collapse
|
9
|
Zöllner HJ, Davies-Jenkins CW, Lee EG, Hendrickson TJ, Clarke WT, Edden RAE, Wisnowski JL, Gudmundson AT, Oeltzschner G. Continuous Automated Analysis Workflow for MRS Studies. J Med Syst 2023; 47:69. [PMID: 37418036 PMCID: PMC10947169 DOI: 10.1007/s10916-023-01969-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 07/02/2023] [Indexed: 07/08/2023]
Abstract
Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.
Collapse
Affiliation(s)
- Helge Jörn Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA.
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Christopher W Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Erik G Lee
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, Department of Clinical Neurosciences, FMRIB, University of Oxford, Oxford, Nuffield, UK
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jessica L Wisnowski
- Department of Radiology, Keck School of Medicine, Children's Hospital Los Angeles, University of Southern California, Los Angeles, USA
- Fetal and Neonatal Institute, CHLA Division of Neonatology, Department of Pediatrics, Keck School of Medicine, Children's Hospital Los Angeles, University of Southern California, Los Angeles, USA
| | - Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| |
Collapse
|
10
|
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 Biomed 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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
11
|
Hui SC, Gong T, Zöllner HJ, Hupfeld KE, Gudmundson AT, Murali-Manohar S, Davies-Jenkins CW, Song Y, Chen Y, Oeltzschner G, Wang G, Edden RAE. sLASER and PRESS Perform Similarly at Revealing Metabolite-Age Correlations. bioRxiv 2023:2023.01.18.524597. [PMID: 36711794 PMCID: PMC9882274 DOI: 10.1101/2023.01.18.524597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Purpose To compare the respective ability of PRESS and sLASER to reveal biological relationships, using age as a validation covariate. Methods MRS data were acquired from 102 healthy volunteers using PRESS and sLASER in centrum semiovale (CSO) and posterior cingulate cortex (PCC) regions. Acquisition parameters included TR/TE 2000/30 ms; 96 transients; 2048 datapoints sampled at 2 kHz.Spectra were analyzed using Osprey. Signal-to-noise ratio (SNR), full-width-half-maximum linewidth of tCr, and metabolite concentrations were extracted. A linear model was used to compare SNR and linewidth. Paired t-tests were used to assess differences in metabolite measurements between PRESS and sLASER. Correlations were used to evaluate the relationship between PRESS and sLASER metabolite estimates, as well as the strength of each metabolite-age relationship. Coefficients of variation were calculated to assess inter-subject variability in each metabolite measurement. Results SNR and linewidth were significantly higher (p<0.05) for sLASER than PRESS. Paired t-tests showed significant differences between PRESS and sLASER in most metabolite measurements. Metabolite measures were significantly correlated (p<0.05) for most metabolites between the two methods except GABA, Gln and Lac in CSO and GSH, Lac and NAAG in PCC. Metabolite-age relationships were consistently identified using both PRESS and sLASER. Similar CVs were observed for most metabolites. Conclusion The study results suggest strong agreement between PRESS and sLASER in identifying relationships between brain metabolites and age in CSO and PCC data acquired at 3T. sLASER is technically desirable due to the reduced chemical shift displacement artifact; however, PRESS performed similarly in 'good' brain regions at clinical field strength.
Collapse
Affiliation(s)
- Steve C.N. Hui
- 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
| | - Tao Gong
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Helge J. Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Kathleen E. Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Aaron T. Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - 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
| | - 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
| | - Yufan Chen
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - 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
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - 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
| |
Collapse
|
12
|
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: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|