Steve C N Hui1,2, Tao Gong3,4, Helge J Zöllner1,2, Yulu Song1,2, Saipavitra Murali-Manohar1,2, Georg Oeltzschner1,2, Mark Mikkelsen1,2,5, Sofie Tapper1,2, Yufan Chen3, Muhammad G Saleh6, Eric C Porges7,8,9, Weibo Chen10, Guangbin Wang3,4, Richard A E Edden1,2. 1. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 2. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA. 3. Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China. 4. Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China. 5. Department of Radiology, Weill Cornell Medicine, New York, New York, USA. 6. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA. 7. Center for Cognitive Aging and Memory, University of Florida, Gainesville, Florida, USA. 8. McKnight Brain Research Foundation, University of Florida, Gainesville, Florida, USA. 9. Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA. 10. Philips Healthcare, Shanghai, China.
Abstract
PURPOSE: To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex. METHODS: 102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm3 , pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a "clean" MM spectrum using the Osprey software. Pearson's correlation coefficient was calculated between integrals and age for the 14 MM signals. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes. RESULTS: MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM-predominant CSO (R = -0.29) and GM-predominant PCC regions (R = -0.57) respectively while CSF increased significantly with age in both regions. CONCLUSION: Our findings suggest that a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.
PURPOSE: To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex. METHODS: 102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm3 , pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a "clean" MM spectrum using the Osprey software. Pearson's correlation coefficient was calculated between integrals and age for the 14 MM signals. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes. RESULTS: MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM-predominant CSO (R = -0.29) and GM-predominant PCC regions (R = -0.57) respectively while CSF increased significantly with age in both regions. CONCLUSION: Our findings suggest that a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.
Authors: Saipavitra Murali-Manohar; Andrew Martin Wright; Tamas Borbath; Nikolai I Avdievich; Anke Henning Journal: Magn Reson Med Date: 2020-08-30 Impact factor: 4.668
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