Age-Related Neurometabolic Changes and Connectivity: A Whole-Brain High-Resolution MRSI Study
Poster No:
1957
Submission Type:
Abstract Submission
Authors:
Wenqi Zhang1, Danni Wang1,2, Yaoyu Zhang1,3, Yibo Zhao4, Sirui Wang5, Gai Kong5, Wen Jin4,6, Yudu Li4,7,8, Huixiang Zhuang1, Bin Bo1, Yihong Yang2, Zhi-Pei Liang4,6, Yingying Tang5, Yao Li1,3
Institutions:
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, 3Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China, 4Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 5Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 6Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 7Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 8National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL
First Author:
Co-Author(s):
Danni Wang
School of Biomedical Engineering, Shanghai Jiao Tong University|Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health
Shanghai, China|Baltimore, MD
School of Biomedical Engineering, Shanghai Jiao Tong University|Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health
Shanghai, China|Baltimore, MD
Yaoyu Zhang
School of Biomedical Engineering, Shanghai Jiao Tong University|Institute of Medical Robotics, Shanghai Jiao Tong University
Shanghai, China|Shanghai, China
School of Biomedical Engineering, Shanghai Jiao Tong University|Institute of Medical Robotics, Shanghai Jiao Tong University
Shanghai, China|Shanghai, China
Yibo Zhao
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
Urbana, IL
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
Urbana, IL
Sirui Wang
Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Gai Kong
Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Wen Jin
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Urbana, IL|Urbana, IL
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Urbana, IL|Urbana, IL
Yudu Li
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Bioengineering, University of Illinois at Urbana-Champaign|National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
Urbana, IL|Urbana, IL|Urbana, IL
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Bioengineering, University of Illinois at Urbana-Champaign|National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
Urbana, IL|Urbana, IL|Urbana, IL
Yihong Yang
Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health
Baltimore, MD
Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health
Baltimore, MD
Zhi-Pei Liang
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Urbana, IL|Urbana, IL
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign|Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Urbana, IL|Urbana, IL
Yingying Tang
Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine
Shanghai, China
Yao Li
School of Biomedical Engineering, Shanghai Jiao Tong University|Institute of Medical Robotics, Shanghai Jiao Tong University
Shanghai, China|Shanghai, China
School of Biomedical Engineering, Shanghai Jiao Tong University|Institute of Medical Robotics, Shanghai Jiao Tong University
Shanghai, China|Shanghai, China
Introduction:
Alterations in neurometabolites reflect age-related structural and functional processes within the brain, including changes in metabolite concentrations as well as shifts in the balance between the primary excitatory and inhibitory neurotransmitters: glutamate (Glu) and gamma-aminobutyric acid (GABA)1.
Using proton magnetic resonance spectroscopy (¹H-MRS), previous studies have found age-related declines in N-acetylaspartate (NAA), a marker of neuronal integrity, alongside increases in myo-Inositol (mIn), an indicator of glial activity, and choline (Cho), a marker of membrane turnover2,3. Additionally, Glu and GABA exhibit dynamic developmental trajectories, with Glu increasing sharply during childhood and plateauing in adulthood, while GABA levels tend to rise with age4,5. However, these findings were based on single-voxel MRS techniques, with low spatial resolution (typically 20×20×20 mm³ voxel size), which overlook important spatial variations in metabolite distributions. Therefore, the interregional relationships of neurometabolites across the whole brain remain unexplored.
Recently, a fast, high-resolution 3D ¹H-MRSI technology, known as SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation), has enabled whole-brain mapping of multiple neurometabolites at a nominal resolution of 2×3×3 mm³ within a 10-minute scan6-10. In this study, we used SPICE to investigate age-related differences in neurometabolite concentrations and their interregional associations. Specifically, we examine seven key neurometabolites-NAA, Cho, Cr, mIn, Glu, glutamine (Gln), and GABA-across the whole brain. By characterizing age-related differences in neurometabolite concentrations and interregional neurometabolic correlations in young and mid-age adults, we aim to understand the shifts in neurometabolic organization across different ages.
Using proton magnetic resonance spectroscopy (¹H-MRS), previous studies have found age-related declines in N-acetylaspartate (NAA), a marker of neuronal integrity, alongside increases in myo-Inositol (mIn), an indicator of glial activity, and choline (Cho), a marker of membrane turnover2,3. Additionally, Glu and GABA exhibit dynamic developmental trajectories, with Glu increasing sharply during childhood and plateauing in adulthood, while GABA levels tend to rise with age4,5. However, these findings were based on single-voxel MRS techniques, with low spatial resolution (typically 20×20×20 mm³ voxel size), which overlook important spatial variations in metabolite distributions. Therefore, the interregional relationships of neurometabolites across the whole brain remain unexplored.
Recently, a fast, high-resolution 3D ¹H-MRSI technology, known as SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation), has enabled whole-brain mapping of multiple neurometabolites at a nominal resolution of 2×3×3 mm³ within a 10-minute scan6-10. In this study, we used SPICE to investigate age-related differences in neurometabolite concentrations and their interregional associations. Specifically, we examine seven key neurometabolites-NAA, Cho, Cr, mIn, Glu, glutamine (Gln), and GABA-across the whole brain. By characterizing age-related differences in neurometabolite concentrations and interregional neurometabolic correlations in young and mid-age adults, we aim to understand the shifts in neurometabolic organization across different ages.
Methods:
Fifty-seven healthy participants were included in this study: 30 young adults (mean age=22.20, range=15–25) and 27 mid-age adults (mean age=34.59, range=26–54). MR scans were performed on a 3T scanner at Shanghai Mental Health Center, China. Whole-brain 1H-MRSI data were obtained using the SPICE sequence (TR/TE = 1.6/160 ms, resolution = 2.0×3.0×3.0 mm³, FOV = 240×240×120 mm³). Ultrafast J-resolved MRSI data acquisition is achieved by efficiently encoding spatial, spectral, and J-coupling information with FID and dual-echo-time SE signals6. Machine learning-based method was used to reconstruct high-resolution metabolite and neurotransmitter maps from the hybrid FID/SE data7-10. The cortical regions were parcellated using Yeo 7-network atlas. Pearson correlation analyses were conducted to calculate associations with significance reserved after FDR correction. Group differences were assessed using Student's t-tests.
Results:
Figure 1A displays whole-brain neurometabolic maps averaged across young and mid-aged adult groups. Compared to young adults, the middle-aged group exhibited evidently lower levels of NAA, Cho, and Cr, along with higher levels of mIns, Glu, Gln, and GABA. Statistical analyses showed significantly lower NAA, Cho, and Cr concentrations in the older group (Fig. 1B). Figure 2A demonstrates the neurometabolic connectivity matrices across the cortical regions for all metabolites. Enhanced metabolic connectivity was observed for NAA, Cho, mIns, and Glu, whereas decreases in connectivity were found for mIns and GABA. Averaged regional neurometabolic connectivity within networks is also presented in Fig. 2B. Taken together, mid-aged adults demonstrated reduced NAA and Cho concentrations along with enhanced intra-network metabolic connectivity, whereas higher mIns and GABA levels were accompanied by reduced metabolic connectivity compared to the younger group.
Conclusions:
Using whole-brain high-resolution MRSI, this study provides novel insights into age-related neurochemical organization of the brain.
Lifespan Development:
Early life, Adolescence, Aging 2
Novel Imaging Acquisition Methods:
MR Spectroscopy 1
Keywords:
ADULTS
MR SPECTROSCOPY
Other - Neurometabolites
1|2Indicates the priority used for review
Abstract Information
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