Feasibility and implications of using subject‐specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data
Expert consensus recommends linear‐combination modeling (LCM) of 1H 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 s...
Saved in:
| Published in | NMR in biomedicine Vol. 36; no. 3; pp. e4854 - n/a |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Wiley Subscription Services, Inc
01.03.2023
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Expert consensus recommends linear‐combination modeling (LCM) of 1H 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.
Linear‐combination modeling results of short‐TE PRESS from a 100‐subject healthy aging cohort using different strategies to model macromolecular (MM) signals (parameterized vs. measured) analyzed with Osprey. The choice of MM strategy accounts for a majority of the observed variance in the metabolite estimates for tNAA and GSH. |
|---|---|
| AbstractList | 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.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. 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. Expert consensus recommends linear‐combination modeling (LCM) of 1H 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. Expert consensus recommends linear‐combination modeling (LCM) of 1H 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. Linear‐combination modeling results of short‐TE PRESS from a 100‐subject healthy aging cohort using different strategies to model macromolecular (MM) signals (parameterized vs. measured) analyzed with Osprey. The choice of MM strategy accounts for a majority of the observed variance in the metabolite estimates for tNAA and GSH. Expert consensus recommends linear-combination modeling (LCM) of 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. |
| Author | Oeltzschner, Georg Gong, Tao Hui, Steve C. N. Zöllner, Helge J. Davies‐Jenkins, Christopher W. Murali‐Manohar, Saipavitra Chen, Weibo Wang, Guangbin Song, Yulu Edden, Richard A. E. |
| Author_xml | – sequence: 1 givenname: Helge J. orcidid: 0000-0002-7148-292X surname: Zöllner fullname: Zöllner, Helge J. email: hzoelln2@jhmi.edu organization: Kennedy Krieger Institute – sequence: 2 givenname: Christopher W. orcidid: 0000-0002-6015-762X surname: Davies‐Jenkins fullname: Davies‐Jenkins, Christopher W. organization: Kennedy Krieger Institute – sequence: 3 givenname: Saipavitra orcidid: 0000-0002-4978-0736 surname: Murali‐Manohar fullname: Murali‐Manohar, Saipavitra organization: Kennedy Krieger Institute – sequence: 4 givenname: Tao orcidid: 0000-0001-9689-0420 surname: Gong fullname: Gong, Tao organization: Cheeloo College of Medicine – sequence: 5 givenname: Steve C. N. orcidid: 0000-0002-1523-4040 surname: Hui fullname: Hui, Steve C. N. organization: Kennedy Krieger Institute – sequence: 6 givenname: Yulu surname: Song fullname: Song, Yulu organization: Kennedy Krieger Institute – sequence: 7 givenname: Weibo surname: Chen fullname: Chen, Weibo organization: Philips Healthcare – sequence: 8 givenname: Guangbin orcidid: 0000-0002-3099-7273 surname: Wang fullname: Wang, Guangbin organization: Cheeloo College of Medicine – sequence: 9 givenname: Richard A. E. orcidid: 0000-0002-0671-7374 surname: Edden fullname: Edden, Richard A. E. organization: Kennedy Krieger Institute – sequence: 10 givenname: Georg orcidid: 0000-0003-3083-9811 surname: Oeltzschner fullname: Oeltzschner, Georg organization: Kennedy Krieger Institute |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36271899$$D View this record in MEDLINE/PubMed |
| BookMark | eNp10c9qVDEUBvAgFTutgk8gATdu7ph_MzdZarEqVN3oOiS557YZcpMxyaXMzhcQfEafxExnqiC6CoTf-Th85wydxBQBoaeULCkh7GW001LIlXiAFpQo1VGh2AlaELViHReSnKKzUjaEECk4e4RO-Zr1VCq1QN8vwRRvffB1h00csJ-2wTtTfYoFpxHPxcdrXGa7AVd_fvtRtuD86B2ejMtpSgHcHEzG-_-aDa4JT2mAgMtNyhWDu0m4-gmav45Q22CGkqKJDo4zqbi03eHBVPMYPRxNKPDk-J6jL5dvPl-8664-vX1_8eqqc1xy0fWWG0INs1RJPhhhGay5s2Z0o3XcsJ4pYziXAKpnY2-aIbSXoKwcRykUP0cvDrnbnL7OUKqefHEQgomQ5qJbQr8WdEV4o8__ops059i2a6qV2IIFaerZUc12gkFvs59M3un7ohtYHkArrZQMo3a-3rXcSvNBU6L3l9Ttknp_yT8r_h64z_wH7Q701gfY_dfpj68_3Plfpn-wlA |
| CitedBy_id | crossref_primary_10_1002_mrm_30061 crossref_primary_10_1002_nbm_5152 crossref_primary_10_1002_mrm_30110 crossref_primary_10_3389_fnins_2023_1258408 crossref_primary_10_1002_mrm_29895 crossref_primary_10_1002_mrm_30158 crossref_primary_10_1002_nbm_5056 crossref_primary_10_1016_j_jneumeth_2024_110206 |
| Cites_doi | 10.1148/radiol.13130531 10.1016/B978-012372560-8/50002-4 10.1002/nbm.1112 10.1002/mrm.20901 10.1214/aos/1013699998 10.1002/mrm.1910140104 10.1109/TAC.1974.1100705 10.1002/mrm.28942 10.1002/nbm.4257 10.1002/nbm.1219 10.1111/j.1749-6632.1987.tb32915.x 10.1002/mrm.25013 10.1016/j.neuroimage.2017.07.021 10.1002/nbm.4197 10.1002/mrm.28385 10.1006/jmre.1999.1799 10.12688/wellcomeopenres.15191.1 10.18637/jss.v059.i09 10.1016/0022‐2364(87)90023‐0 10.1002/mrm.22490 10.1371/journal.pone.0121945 10.1002/mrm.27467 10.1002/mrm.27742 10.1002/mrm.26778 10.1088/0031‐9155/30/4/008 10.1002/nbm.4393 10.1002/mrm.26022 10.1002/nbm.2896 10.1148/radiol.2020191037 10.1002/nbm.4484 10.1002/mrm.1910300604 10.1002/mrm.1269 10.1016/j.jneumeth.2020.108827 10.1002/mrm.29093 10.1080/00221300309601282 10.1002/nbm.4328 10.1002/(SICI)1522-2594(199904)41:4<649::AID-MRM2>3.0.CO;2-G 10.1002/mrm.26091 10.3390/antiox10091407 10.1002/mrm.29367 10.1007/s10334‐013‐0393‐4 10.1093/cercor/bhab107 10.1101/2022.06.08.495050 10.1002/mrm.22579 10.1101/2022.03.22.485310 10.1016/J.NEUROIMAGE.2019.02.059 10.1002/nbm.4411 10.1101/2022.07.07.499181 10.1080/10618600.2013.829780 10.1002/nbm.4482 10.1002/(SICI)1522-2586(199904)9:4<531::AID-JMRI4>3.0.CO;2-L 10.1002/nbm.4618 10.1007/978-0-387-98141-3 |
| ContentType | Journal Article |
| Copyright | 2022 John Wiley & Sons Ltd. 2023 John Wiley & Sons, Ltd. |
| Copyright_xml | – notice: 2022 John Wiley & Sons Ltd. – notice: 2023 John Wiley & Sons, Ltd. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QO 8FD FR3 K9. P64 7X8 |
| DOI | 10.1002/nbm.4854 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Biotechnology Research Abstracts Technology Research Database Engineering Research Database ProQuest Health & Medical Complete (Alumni) Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Health & Medical Complete (Alumni) Engineering Research Database Biotechnology Research Abstracts Technology Research Database Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef ProQuest Health & Medical Complete (Alumni) MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Chemistry Physics |
| EISSN | 1099-1492 |
| EndPage | n/a |
| ExternalDocumentID | 36271899 10_1002_nbm_4854 NBM4854 |
| Genre | article Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: National Institutes of Health – fundername: NIH funderid: R21 EB033516; R21A G060245; R01 EB023963; R01 EB016089; P41 EB031771; R00 AG062230 – fundername: NIBIB NIH HHS grantid: P41 EB031771 – fundername: NIBIB NIH HHS grantid: R01 EB023963 – fundername: NIBIB NIH HHS grantid: R01 EB016089 – fundername: NIA NIH HHS grantid: R21 AG060245 – fundername: NIBIB NIH HHS grantid: R21 EB033516 – fundername: NIA NIH HHS grantid: R00 AG062230 |
| GroupedDBID | --- .3N .GA .Y3 05W 0R~ 10A 123 1L6 1OB 1OC 1ZS 31~ 33P 3SF 3WU 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52V 52W 52X 53G 5RE 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A01 A03 AAESR AAEVG AAHHS AAHQN AAIPD AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABEML ABIJN ABPVW ABQWH ABXGK ACAHQ ACBWZ ACCFJ ACCZN ACFBH ACGFS ACGOF ACIWK ACMXC ACPOU ACPRK ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADBTR ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN AEEZP AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFRAH AFWVQ AFZJQ AHBTC AIACR AITYG AIURR AIWBW AJBDE ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ASPBG ATUGU AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMXJE BROTX BRXPI BY8 CS3 D-6 D-7 D-E D-F DCZOG DPXWK DR2 DRFUL DRMAN DRSTM DU5 DUUFO EBD EBS EJD EMOBN F00 F01 F04 F5P FEDTE FUBAC G-S G.N GNP GODZA H.X HBH HF~ HGLYW HHY HHZ HVGLF HZ~ IX1 J0M JPC KBYEO LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES M65 MEWTI MK4 MRFUL MRMAN MRSTM MSFUL MSMAN MSSTM MXFUL MXMAN MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG P2P P2W P2X P2Z P4D PALCI Q.N Q11 QB0 QRW R.K RGB RIWAO RJQFR ROL RWI RX1 SAMSI SUPJJ SV3 UB1 V2E W8V W99 WBKPD WHWMO WIB WIH WIJ WIK WJL WOHZO WQJ WRC WUP WVDHM WXSBR XG1 XPP XV2 ZZTAW ~IA ~WT AAYXX AEYWJ AGHNM AGQPQ AGYGG CITATION AAMMB AEFGJ AGXDD AIDQK AIDYY CGR CUY CVF ECM EIF NPM 7QO 8FD FR3 K9. P64 7X8 |
| ID | FETCH-LOGICAL-c3834-7b3a01a2b1983da4b2e63cbafcfbc3a2729aa338ee972f7a3da0178e9b8ff8493 |
| IEDL.DBID | DR2 |
| ISSN | 0952-3480 1099-1492 |
| IngestDate | Fri Jul 11 10:25:11 EDT 2025 Sun Jul 13 04:22:27 EDT 2025 Mon Jul 21 05:40:36 EDT 2025 Tue Jul 01 02:45:47 EDT 2025 Thu Apr 24 23:08:47 EDT 2025 Wed Jan 22 16:17:19 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | aging, linear-combination modeling, macromolecules, short-TE MRS |
| Language | English |
| License | 2022 John Wiley & Sons Ltd. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3834-7b3a01a2b1983da4b2e63cbafcfbc3a2729aa338ee972f7a3da0178e9b8ff8493 |
| Notes | Funding information National Institutes of Health; NIH, Grant Numbers: R00 AG062230, P41 EB031771, R01 EB016089, R01 EB023963, R21A G060245, and R21 EB033516 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-0671-7374 0000-0002-7148-292X 0000-0002-6015-762X 0000-0001-9689-0420 0000-0002-3099-7273 0000-0002-4978-0736 0000-0003-3083-9811 0000-0002-1523-4040 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/9930668 |
| PMID | 36271899 |
| PQID | 2771801740 |
| PQPubID | 2029982 |
| PageCount | 19 |
| ParticipantIDs | proquest_miscellaneous_2727641503 proquest_journals_2771801740 pubmed_primary_36271899 crossref_citationtrail_10_1002_nbm_4854 crossref_primary_10_1002_nbm_4854 wiley_primary_10_1002_nbm_4854_NBM4854 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | March 2023 2023-03-00 20230301 |
| PublicationDateYYYYMMDD | 2023-03-01 |
| PublicationDate_xml | – month: 03 year: 2023 text: March 2023 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: Oxford |
| PublicationTitle | NMR in biomedicine |
| PublicationTitleAlternate | NMR Biomed |
| PublicationYear | 2023 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | 2013; 26 1987; 73 1990; 14 2015; 77 2016; 76 2014; 27 1987; 508 1999; 41 2001; 46 2014; 23 2017; 159 1974; 19 2021; 35 2010; 64 2021; 31 2021; 34 2020; 295 1993; 30 2014; 59 2011; 65 2008; 21 2007; 20 2018; 79 2019; 4 2019; 191 2020; 85 2006; 55 2019; 34 2015; 10 2009 2007 1999; 140 2020; 343 2020; 34 2022; 87 2020; 33 2014; 270 2022; 88 2001; 29 1999; 9 2003; 130 2019; 82 2021; 10 2019; 81 2022 2020 2017 1985; 30 2014; 72 e_1_2_9_31_1 e_1_2_9_52_1 e_1_2_9_50_1 e_1_2_9_10_1 e_1_2_9_56_1 e_1_2_9_12_1 e_1_2_9_33_1 e_1_2_9_54_1 e_1_2_9_14_1 e_1_2_9_39_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_18_1 e_1_2_9_41_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_45_1 e_1_2_9_24_1 e_1_2_9_43_1 e_1_2_9_8_1 e_1_2_9_6_1 e_1_2_9_4_1 e_1_2_9_2_1 e_1_2_9_26_1 e_1_2_9_49_1 e_1_2_9_28_1 e_1_2_9_47_1 e_1_2_9_30_1 e_1_2_9_53_1 e_1_2_9_51_1 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_57_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_55_1 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_19_1 e_1_2_9_42_1 e_1_2_9_40_1 e_1_2_9_21_1 e_1_2_9_46_1 e_1_2_9_23_1 e_1_2_9_44_1 e_1_2_9_7_1 e_1_2_9_5_1 e_1_2_9_3_1 R Core Team (e_1_2_9_35_1) 2017 e_1_2_9_9_1 e_1_2_9_25_1 e_1_2_9_27_1 e_1_2_9_48_1 e_1_2_9_29_1 |
| References_xml | – year: 2009 – volume: 34 issue: 5 year: 2021 article-title: Minimum Reporting Standards for in vivo Magnetic Resonance Spectroscopy (MRSinMRS): Experts' consensus recommendations publication-title: NMR Biomed. – volume: 81 start-page: 746 issue: 2 year: 2019 end-page: 758 article-title: Investigation of the influence of macromolecules and spline baseline in the fitting model of human brain spectra at 9.4T publication-title: Magn Reson Med. – volume: 59 start-page: 1 issue: 1 year: 2014 end-page: 32 article-title: A Kenward‐Roger approximation and parametric bootstrap methods for tests in linear mixed models: the R Package pbkrtest publication-title: J Stat Softw. – volume: 64 start-page: 939 issue: 4 year: 2010 end-page: 946 article-title: Diffusion‐weighted spectroscopy: A novel approach to determine macromolecule resonances in short‐echo time 1H‐MRS publication-title: Magn Reson Med. – volume: 29 start-page: 1165 issue: 4 year: 2001 end-page: 1188 article-title: The control of the false discovery rate in multiple testing under dependency publication-title: Ann Stat. – volume: 79 start-page: 1231 issue: 3 year: 2018 end-page: 1240 article-title: Simultaneous mapping of metabolites and individual macromolecular components via ultra‐short acquisition delay 1H MRSI in the brain at 7T publication-title: Magn Reson Med. – volume: 27 start-page: 113 issue: 2 year: 2014 end-page: 130 article-title: Quantification in magnetic resonance spectroscopy based on semi‐parametric approaches publication-title: Magn Reson Mater Phys Biol Med. – volume: 159 start-page: 32 year: 2017 end-page: 45 article-title: Big GABA: Edited MR spectroscopy at 24 research sites publication-title: NeuroImage. – volume: 46 start-page: 855 issue: 5 year: 2001 end-page: 863 article-title: Characterization of the macromolecule baseline in localized 1H‐MR spectra of human brain publication-title: Magn Reson Med. – year: 2022 article-title: Neurometabolic timecourse of healthy aging publication-title: bioRxiv. – volume: 26 start-page: 593 issue: 5 year: 2013 end-page: 599 article-title: Quantification of the neurochemical profile using simulated macromolecule resonances at 3 T publication-title: NMR Biomed. – volume: 270 start-page: 658 issue: 3 year: 2014 end-page: 679 article-title: Clinical proton MR spectroscopy in central nervous system disorders publication-title: Radiology. – volume: 34 issue: 4 year: 2021 article-title: Comparison of different linear‐combination modeling algorithms for short‐TE proton spectra publication-title: NMR Biomed. – volume: 34 year: 2019 article-title: Influence of fitting approaches in LCModel on MRS quantification focusing on age‐specific macromolecules and the spline baseline publication-title: NMR Biomed. – volume: 88 start-page: 1 year: 2022 end-page: 16 article-title: Macromolecular background signal and non‐Gaussian metabolite diffusion determined in human brain using ultra‐high diffusion weighting publication-title: Magn Reson Med. – year: 2022 – volume: 87 start-page: 1711 issue: 4 year: 2022 end-page: 1719 article-title: The macromolecular MR spectrum does not change with healthy aging publication-title: Magn Reson Med. – volume: 34 year: 2021 article-title: Spectral editing in 1H magnetic resonance spectroscopy: Experts' consensus recommendations publication-title: NMR in Biomed. – volume: 30 start-page: 672 issue: 6 year: 1993 end-page: 679 article-title: Estimation of metabolite concentrations from localized in vivo proton NMR spectra publication-title: Magn Reson Med. – volume: 4 start-page: 63 year: 2019 article-title: Raincloud plots: a multi‐platform tool for robust data visualization publication-title: Wellcome Open Res. – volume: 20 start-page: 493 issue: 5 year: 2007 end-page: 504 article-title: An automated quantitation of short echo time MRS spectra in an open source software environment: AQSES publication-title: NMR Biomed. – volume: 34 year: 2020 article-title: Preprocessing, analysis and quantification in single‐voxel magnetic resonance spectroscopy: experts' consensus recommendations publication-title: NMR Biomed. – volume: 41 start-page: 649 issue: 4 year: 1999 end-page: 656 article-title: In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time publication-title: Magn Reson Med. – volume: 19 start-page: 716 issue: 6 year: 1974 end-page: 723 article-title: A new look at the statistical model identification publication-title: IEEE Trans Autom Control. – volume: 10 issue: 9 year: 2021 article-title: In vivo brain GSH: MRS methods and clinical applications publication-title: Antioxidants. – volume: 73 start-page: 553 issue: 3 year: 1987 end-page: 557 article-title: Improved algorithm for noniterative time‐domain model fitting to exponentially damped magnetic resonance signals publication-title: J Magn Reson. – volume: 85 start-page: 13 year: 2020 end-page: 29 article-title: Adaptive baseline fitting for H MR spectroscopy analysis publication-title: bioRxiv. – year: 2007 – volume: 191 start-page: 537 year: 2019 end-page: 548 article-title: Big GABA II: Water‐referenced edited MR spectroscopy at 25 research sites publication-title: NeuroImage. – volume: 87 start-page: 11 issue: 1 year: 2022 end-page: 32 article-title: the 2016 IMSGFC Team. Results and interpretation of a fitting challenge for MR spectroscopy set up by the MRS study group of ISMRM publication-title: Magn Reson Med. – volume: 31 start-page: 4576 issue: 10 year: 2021 end-page: 4594 article-title: In vivo brain glutathione is higher in older age and correlates with mobility publication-title: Cereb Cortex. – volume: 10 issue: 4 year: 2015 article-title: cocor: A comprehensive solution for the statistical comparison of correlations publication-title: PLoS ONE. – volume: 55 start-page: 1219 issue: 6 year: 2006 end-page: 1226 article-title: Use of tissue water as a concentration reference for proton spectroscopic imaging publication-title: Magn Reson Med. – volume: 30 start-page: 341 issue: 4 year: 1985 end-page: 344 article-title: 1H NMR chemical shift selective (CHESS) imaging publication-title: Phys Med Biol. – volume: 23 start-page: 636 issue: 3 year: 2014 end-page: 656 article-title: Variable selection diagnostics measures for high‐dimensional regression publication-title: J Comput Graph Stat. – volume: 82 start-page: 527 year: 2019 end-page: 550 – volume: 130 start-page: 149 issue: 2 year: 2003 end-page: 168 article-title: A Monte Carlo evaluation of tests for comparing dependent correlations publication-title: J Gen Psychol. – volume: 9 start-page: 531 issue: 4 year: 1999 end-page: 538 article-title: NMR relaxation times in the human brain at 3.0 Tesla publication-title: J Magn Reson Imaging. – volume: 72 start-page: 913 issue: 4 year: 2014 end-page: 922 article-title: Smoothness of in vivo spectral baseline determined by mean‐square error publication-title: Magn Reson Med. – volume: 88 start-page: 1994 year: 2022 end-page: 2004 article-title: MRSCloud: a Cloud‐based MR spectroscopy tool for basis set simulation publication-title: Magn Reson Med. – year: 2020 – volume: 21 start-page: 507 issue: 5 year: 2008 end-page: 517 article-title: Short‐TE localised 1H MRS of the human brain at 3T: Quantification of the metabolite signals using two approaches to account for macromolecular signal contributions publication-title: NMR Biomed. – volume: 65 start-page: 1 issue: 1 year: 2011 end-page: 12 article-title: A constrained least‐squares approach to the automated quantitation of in vivo 1H magnetic resonance spectroscopy data publication-title: Magn Reson Med. – volume: 14 start-page: 26 issue: 1 year: 1990 end-page: 30 article-title: In vivo proton spectroscopy in presence of eddy currents publication-title: Magn Reson Med. – volume: 33 issue: 9 year: 2020 article-title: Parameterization of metabolite and macromolecule contributions in interrelated MR spectra of human brain using multidimensional modeling publication-title: NMR Biomed. – volume: 295 issue: 1 year: 2020 article-title: Comparison of multivendor single‐voxel MR spectroscopy data acquired in healthy brain at 26 sites publication-title: Radiology. – volume: 76 start-page: 1083 issue: 4 year: 2016 end-page: 1091 article-title: Test‐retest reproducibility of neurochemical profiles with short‐echo, single‐voxel MR spectroscopy at 3T and 7T publication-title: Magn Reson Med. – volume: 140 start-page: 228 issue: 1 year: 1999 end-page: 234 article-title: Application of multiple inversion recovery for suppression of macromolecule resonances in short echo time 1H NMR spectroscopy of human brain publication-title: J Magn Reson. – volume: 34 issue: 5 year: 2021 article-title: Contribution of macromolecules to brain 1H MR spectra: Experts' consensus recommendations publication-title: NMR Biomed. – volume: 343 year: 2020 article-title: Osprey: Open‐source processing, reconstruction & estimation of magnetic resonance spectroscopy data publication-title: J Neurosci Methods. – volume: 77 start-page: 23 issue: 1 year: 2015 end-page: 33 article-title: Advanced processing and simulation of MRS data using the FID appliance (FID‐A)—An open source, MATLAB‐based toolkit publication-title: Magn Reson Med. – year: 2017 – volume: 508 start-page: 333 year: 1987 end-page: 348 – volume: 35 year: 2021 article-title: Comparison of linear combination modeling strategies for edited magnetic resonance spectroscopy at 3 T publication-title: NMR Biomed. – volume-title: R: A Language and Environment for Statistical Computing year: 2017 ident: e_1_2_9_35_1 – ident: e_1_2_9_2_1 doi: 10.1148/radiol.13130531 – ident: e_1_2_9_30_1 doi: 10.1016/B978-012372560-8/50002-4 – ident: e_1_2_9_55_1 doi: 10.1002/nbm.1112 – ident: e_1_2_9_28_1 doi: 10.1002/mrm.20901 – ident: e_1_2_9_41_1 doi: 10.1214/aos/1013699998 – ident: e_1_2_9_20_1 doi: 10.1002/mrm.1910140104 – ident: e_1_2_9_42_1 doi: 10.1109/TAC.1974.1100705 – ident: e_1_2_9_57_1 doi: 10.1002/mrm.28942 – ident: e_1_2_9_4_1 doi: 10.1002/nbm.4257 – ident: e_1_2_9_12_1 doi: 10.1002/nbm.1219 – ident: e_1_2_9_17_1 doi: 10.1111/j.1749-6632.1987.tb32915.x – ident: e_1_2_9_13_1 doi: 10.1002/mrm.25013 – ident: e_1_2_9_38_1 doi: 10.1016/j.neuroimage.2017.07.021 – ident: e_1_2_9_52_1 doi: 10.1002/nbm.4197 – ident: e_1_2_9_50_1 doi: 10.1002/mrm.28385 – ident: e_1_2_9_54_1 doi: 10.1006/jmre.1999.1799 – ident: e_1_2_9_31_1 doi: 10.12688/wellcomeopenres.15191.1 – ident: e_1_2_9_40_1 doi: 10.18637/jss.v059.i09 – ident: e_1_2_9_21_1 doi: 10.1016/0022‐2364(87)90023‐0 – ident: e_1_2_9_53_1 doi: 10.1002/mrm.22490 – ident: e_1_2_9_8_1 – ident: e_1_2_9_33_1 doi: 10.1371/journal.pone.0121945 – ident: e_1_2_9_51_1 doi: 10.1002/mrm.27467 – ident: e_1_2_9_3_1 doi: 10.1002/mrm.27742 – ident: e_1_2_9_25_1 doi: 10.1002/mrm.26778 – ident: e_1_2_9_19_1 doi: 10.1088/0031‐9155/30/4/008 – ident: e_1_2_9_5_1 doi: 10.1002/nbm.4393 – ident: e_1_2_9_45_1 doi: 10.1002/mrm.26022 – ident: e_1_2_9_11_1 doi: 10.1002/nbm.2896 – ident: e_1_2_9_24_1 doi: 10.1148/radiol.2020191037 – ident: e_1_2_9_27_1 doi: 10.1002/nbm.4484 – ident: e_1_2_9_7_1 doi: 10.1002/mrm.1910300604 – ident: e_1_2_9_14_1 doi: 10.1002/mrm.1269 – ident: e_1_2_9_15_1 doi: 10.1016/j.jneumeth.2020.108827 – ident: e_1_2_9_9_1 doi: 10.1002/mrm.29093 – ident: e_1_2_9_32_1 doi: 10.1080/00221300309601282 – ident: e_1_2_9_48_1 doi: 10.1002/nbm.4328 – ident: e_1_2_9_18_1 doi: 10.1002/(SICI)1522-2594(199904)41:4<649::AID-MRM2>3.0.CO;2-G – ident: e_1_2_9_23_1 doi: 10.1002/mrm.26091 – ident: e_1_2_9_44_1 doi: 10.3390/antiox10091407 – ident: e_1_2_9_47_1 doi: 10.1002/mrm.29367 – ident: e_1_2_9_49_1 doi: 10.1007/s10334‐013‐0393‐4 – ident: e_1_2_9_43_1 doi: 10.1093/cercor/bhab107 – ident: e_1_2_9_10_1 doi: 10.1101/2022.06.08.495050 – ident: e_1_2_9_16_1 doi: 10.1002/mrm.22579 – ident: e_1_2_9_22_1 doi: 10.1101/2022.03.22.485310 – ident: e_1_2_9_39_1 doi: 10.1016/J.NEUROIMAGE.2019.02.059 – ident: e_1_2_9_46_1 doi: 10.1002/nbm.4411 – ident: e_1_2_9_37_1 doi: 10.1101/2022.07.07.499181 – ident: e_1_2_9_34_1 doi: 10.1080/10618600.2013.829780 – ident: e_1_2_9_26_1 – ident: e_1_2_9_6_1 doi: 10.1002/nbm.4482 – ident: e_1_2_9_29_1 doi: 10.1002/(SICI)1522-2586(199904)9:4<531::AID-JMRI4>3.0.CO;2-L – ident: e_1_2_9_56_1 doi: 10.1002/nbm.4618 – ident: e_1_2_9_36_1 doi: 10.1007/978-0-387-98141-3 |
| SSID | ssj0008432 |
| Score | 2.4205406 |
| Snippet | Expert consensus recommends linear‐combination modeling (LCM) of 1H MR spectra with sequence‐specific simulated metabolite basis function and experimentally... Expert consensus recommends linear‐combination modeling (LCM) of 1 H MR spectra with sequence‐specific simulated metabolite basis function and experimentally... Expert consensus recommends linear-combination modeling (LCM) of H MR spectra with sequence-specific simulated metabolite basis function and experimentally... Expert consensus recommends linear-combination modeling (LCM) of 1 H MR spectra with sequence-specific simulated metabolite basis function and experimentally... |
| SourceID | proquest pubmed crossref wiley |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | e4854 |
| SubjectTerms | Age aging, linear‐combination modeling, macromolecules, short‐TE MRS Algorithms Basis functions Biological products Brain - metabolism Choline Choline - metabolism Conserved sequence Correlation analysis Creatine Estimates Feasibility Studies Humans Impact analysis Life span Macromolecular Substances - metabolism Macromolecules Magnetic resonance spectroscopy Magnetic Resonance Spectroscopy - methods Mathematical models Mean Metabolites Parameterization Parietal lobe Receptors, Antigen, T-Cell - metabolism Signal-To-Noise Ratio Spectra Spectrum analysis Statistical models Variance analysis |
| Title | Feasibility and implications of using subject‐specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fnbm.4854 https://www.ncbi.nlm.nih.gov/pubmed/36271899 https://www.proquest.com/docview/2771801740 https://www.proquest.com/docview/2727641503 |
| Volume | 36 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6hSjwuFJZHtxRkJASnbLO2m9hHqKgqpO0BUakSh8h27BZBk2qzeygn_gASv7G_pDOOs7Q8JMRpDzt52TP2N8k33wC8QNDrilIWmSxEnUnvfKbzMM2CMMHWqsSIouLk2UGxfyjfHe0cJVYl1cL0-hCrF24UGXG9pgA3ttu-IhpqTydS7ZAUKFG1CA-9_6kcpWTsTYYAgmdCqnzQnc359nDg9Z3oN3h5Ha3G7WZvHT4ON9qzTD5Plgs7cV9_0XD8vye5B3cTCmWve7e5Dzd8M4Lbu0PztxHcmqVv7iO4GUmirnsA3xEvJjbtOTNNzT5doaOzNjAi0R-zbmnp5c7Ftx9UxklUJHZqIu0vdeJlsbxzbtiiZbETD-tOMAtgHpdiRs3u0f64oepKNveUKqBjpmNaqqI5Z8RrfQiHe28_7O5nqZ1D5jANlllphcmnhtupVqI20nJfCGdNcME6YTjCfGMwY_ZelzyUBm1wuVBeWxWCklo8grWmbfwGsBpRVcA51hLTRxxFPXW8dl6ZkhtZBj6GV8PUVi5pnVPLjS9Vr9LMKxzzisZ8DM9Xlme9vscfbLYG76hShHcVL3FXx_uTOZ5i9TfOEn1wMY1vl2TDywIRUi7G8Lj3qtVFEDjgCbQew8voG3-9enXwZka_m_9q-ATucERiPVFuC9YW86V_ishpYZ_FGLkEDQgZLw |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB6VIiiXAstr2wJGQnDKNmu7saOeoKJaoLsH1Eo9IEW2YxcETdA-DuXEH0DiN_JLmHGSpeUhIU45ZGIn9oz9jfPNDMBjBL0uUzJLZCbKRHrnkzwNwyQIE2ypFVoUBSePJ9noSL463jlegd0uFqbJD7E8cCPLiOs1GTgdSG-fyxpqTwdS78hLcJkKekd_6s3P3FFaxupkCCF4IqROu8yzKd_unry4F_0GMC_i1bjh7F-Ht92rNjyTD4PF3A7c51-yOP7nt9yA9RaIsmeN5tyEFV_1YG2vq__Wg6vj9rd7D65Enqib3YKvCBlbQu0ZM1XJ3p9jpLM6MOLRn7DZwtL5zvcv3yiSk9hI7NRE5l9bjJfFCM-pYfOaxWI8bPYOHQHmcTVmVO8e5U8qCrBkU0_eAupm-0xNgTRnjKitt-Fo_8Xh3ihpKzokDj1hmSgrTDo03A5zLUojLfeZcNYEF6wThiPSNwadZu9zxYMyKIMrhva51SFomYs7sFrVlb8HrERgFXCSc4keJI5iPnS8dF4bxY1UgffhaTe3hWvTnVPVjY9Fk6iZFzjmBY15Hx4tJT81KT7-ILPVqUfRGvms4Ao3dnw_mWITy9s4S_TPxVS-XpAMVxmCpFT04W6jVstOEDtgA3nehydROf7aezF5Pqbrxr8KPoS10eH4oDh4OXm9Cdc4ArOGN7cFq_Ppwt9HIDW3D6LB_AB6mB1K |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB5BES0XHstroYCREJyyzTpuYh-hZVUeu0KISpU4RLZjtwiaVPs4tKf-ASR-I7-EGcdZWh4S4pRDJnZiz9jfON_MADxB0GvzQuSJyLMqEc66RKV-mPhMe1PJAi2KgpPHk3xnV7ze29yLrEqKhWnzQywP3MgywnpNBn5U-Y0zSUPN4UDITXERLok8laTR2-9_po6SIhQnQwTBk0zItEs8m_KN7snzW9Fv-PI8XA37zegafOzetKWZfB4s5mZgT35J4vh_n3IdrkYYyp63enMDLri6B2tbXfW3HqyO40_3HlwOLFE7uwlfETBGOu0x03XFPp3ho7PGM2LR77PZwtDpzvfTbxTHSVwkdqgD7y-W4mUhvnOq2bxhoRQPmx2gG8AcrsWMqt2j_H5N4ZVs6shXQM2MzzQURnPMiNh6C3ZHLz9s7SSxnkNi0Q8WSWEynQ41N0Mls0oLw12eWaO99cZmmiPO1xpdZudUwX2hUQbXC-mUkd5LobLbsFI3tbsLrEJY5XGOlUD_EUdRDS2vrJO64FoUnvfhWTe1pY3JzqnmxpeyTdPMSxzzksa8D4-Xkkdtgo8_yKx32lFGE5-VvMBtHd9PpNjE8jbOEv1x0bVrFiTDixwhUpr14U6rVctOEDlgA0r14WnQjb_2Xk5ejOl6718FH8Hqu-1R-fbV5M19uMIRlbWkuXVYmU8X7gGiqLl5GMzlBziQHAI |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Feasibility+and+implications+of+using+subject%E2%80%90specific+macromolecular+spectra+to+model+short+echo+time+magnetic+resonance+spectroscopy+data&rft.jtitle=NMR+in+biomedicine&rft.au=Z%C3%B6llner%2C+Helge+J.&rft.au=Davies%E2%80%90Jenkins%2C+Christopher+W.&rft.au=Murali%E2%80%90Manohar%2C+Saipavitra&rft.au=Gong%2C+Tao&rft.date=2023-03-01&rft.issn=0952-3480&rft.eissn=1099-1492&rft.volume=36&rft.issue=3&rft_id=info:doi/10.1002%2Fnbm.4854&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_nbm_4854 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0952-3480&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0952-3480&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0952-3480&client=summon |