Aging Effects the Glutamate-enriched Functional Connectivity in Resting-state fMRI
Poster No:
1166
Submission Type:
Abstract Submission
Authors:
Zihao Zheng1,2, Ao Xie1,2, Wei Jian1,2, Yulin He1,2, Haiyang Sun1,2, Yulan Zhou1,2, Jianfu Li1,2, Li Dong*1,2, Dezhong Yao1,2
Institutions:
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China, 2School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China, Chengdu, China
First Author:
Zihao Zheng
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
Co-Author(s):
Ao Xie
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
Wei Jian
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
Yulin He
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
Haiyang Sun
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
Yulan Zhou
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
Jianfu Li
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
Li Dong*
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
Dezhong Yao
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
University of Electronic Science and Technology of China, Chengdu, China|Chengdu, China
Introduction:
In recent years, age-related changes in functional connectivity of older adults have been researched widely. According to the previous studies, obtained results are different which illustrated the functional connectivity decreased in large part of the brain (e.g. default mode network) and increased in some particular regions and areas such as sensorimotor areas (Farras-Permanyer et al., 2019). The glutamate receptor is a necessary component in the brain glutamatergic system and this receptor was proved to be associated with cognitive function and brain aging (Mecca et al., 2021). However, because the functional magnetic resonance imaging (fMRI) cannot directly provide a molecular insight into the main effect of compounds in brain aging research (Attwell and Iadecola, 2002), the potential functional network changes in older related to specific molecular systems of glutamate still unclear. Here we used a new method which utilizes the glutamate information about target distribution provide by Positron Emission Tomography (PET) to enrich the fMRI connectivity analysis in brain aging.
Methods:
The resting-state fMRI ( rs-fMRI ) data were collected from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN, http://www.cam-can.org/) (Taylor et al., 2017). All fMRI images (Older:185, younger:198) were preprocessed using SPM as implemented in the Neuroscience Information Toolbox (http://www.neuro.uestc.edu.cn/NIT.html) (Dong et al., 2018). The first 5 scans were deleted. Then the images were preprocessed by following steps: realignment, slice time correction, spatial normalization (3×3×3mm3) and smoothing (8-mm full width at half maximum kernel, FWHM). The metabotropic glutamate receptor 5 (mGluR5) (Hansen et al., 2022) was used to enrich the resting-state fMRI by applying Receptor-Enriched Analysis of Functional Connectivity by Targets (REACT) (Dipasquale et al., 2019) to estimate subject-specific functional connectivity (FC) map in each group. Next, these glutamate-enriched connectivity maps of the older and younger were compared using two sample T-test. All voxel-wise statistical analyses were performed with false discovery rate correction (P<0.05, FDR corrected). In addition, we sought to describe whether effects of aging tend to be in specific functional brain networks, conducting network enrichment analysis (a spin-based spatial permutation test) (Baller et al., 2022).
Results:
The effect of aging on FCs in the glutamate enriched maps involves different cerebral cortex and deep regions. Compared with young group (P<0.05, FDR corrected), FCs significantly decreased in the precentral gyrus and middle temporal gyrus in older group. And, FCs in older group increased in the middle frontal gyrus and middle occipital gyrus, as well as in the. caudate and thalamus regions. Network enrichment analysis further revealed that age-related declines in glutamate enriched FCs were most prominent in the ventral attention network (VAN, P=0.006) and somatomotor network (SMN, P=0.04). In addition, the FCs in SMN were significantly related with fluid intelligence (r=0.18, P=0.0008), and the FC in thalamus showed negative relationship with fluid intelligence (r=-0.20, P=0.00007). The details can be found in Figure1.
·Figure1:A: The group difference of enriched FCs (P<0.05 FDR). B: The violin plots reflect null distribution of network enrichment. C: The relationships between fluid intelligence and enriched FCs
Conclusions:
In conclusion, our results showed that aging related glutamate-enriched networks were mainly involved in the somatomotor network, ventral attention network and deep brain regions, and these effects perhaps were correlated with cognitive functions. The glutamate enriched FCs may provide insights into understanding brain aging in the light of the molecular mechanisms.
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Aging
Glutamate
Other - fMRI; Network
1|2Indicates the priority used for review
Provide references using author date format
Baller, E.B. (2022). 'Developmental coupling of cerebral blood flow and fMRI fluctuations in youth'. Cell Rep. 38, 110576
Dipasquale, O. (2019). 'Receptor-Enriched Analysis of functional connectivity by targets (REACT): A novel, multimodal analytical approach informed by PET to study the pharmacodynamic response of the brain under MDMA'. Neuroimage. 195, 252-260.
Dong, L., (2018). 'Neuroscience Information Toolbox: An Open Source Toolbox for EEG-fMRI Multimodal Fusion Analysis'. Frontiers in Neuroinformatics. 12.
Farras-Permanyer, L. (2019). 'Age-related changes in resting-state functional connectivity in older adults'. Neural Regen Res. 14,
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Hansen, J.Y. (2022). 'Mapping neurotransmitter systems to the structural and functional organization of the human neocortex'. Nat Neurosci. 25, 1569-1581.
Mecca, A.P. (2021). 'Effect of age on brain metabotropic glutamate receptor subtype 5 measured with [(18)F]FPEB PET'. Neuroimage. 238, 118217.
Taylor, J.R. (2017). 'The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample'. Neuroimage. 144, 262-269.