Transcriptomic Architectures of Neurovascular Coupling Variability in Mild Cognitive Impairment
Poster No:
886
Submission Type:
Abstract Submission
Authors:
Zihao Zheng1,2, Yulin He1,2, Haiyang Sun1,2, Yulan Zhou1,2, Ziqi Wang1, Cheng Luo1,2, Li Dong1,2, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
Chengdu, China|Chengdu, China
Co-Author(s):
Yulin He
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
Chengdu, China|Chengdu, China
Ziqi Wang
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China
Chengdu, China
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China
Chengdu, China
Cheng Luo
The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, 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, University of Electronic Science and Technology of China|School of Life Science and Technology, Center for information in medicine, University of Electronic Science and Technology of China
Chengdu, China|Chengdu, China
Introduction:
Neurovascular coupling (NVC) is the mechanism that links local neural activity to changes in cerebral blood flow (CBF), ensuring that active brain regions receive adequate blood supply. Understanding the mechanisms of NVC is crucial because neurovascular dysfunction contributes to cognitive impairment in Alzheimer's disease (Zlokovic, 2011). According to previous studies, neurovascular coupling is decreased in mild cognitive impairment (MCI) compared to age-matched controls, particularly in the left-dorsolateral prefrontal cortex (Owens et al., 2024). Traditionally, CBF-BOLD coupling has been constructed using various functional brain activity metrics, such as ALFF (Baller et al., 2022), ReHo (Guo et al., 2019), and FCS (Wei et al., 2021). These methods are typically derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and provide static measures of the relationship between CBF and neural activity. However, the relationship between CBF and cerebral metabolic rate of oxygen is still not clear in MCI. Here, we used the Receptor-Enriched Analysis of functional connectivity by targets (REACT) (Dipasquale et al., 2019) to calculate CBF-enriched functional connectivity (CBF-BOLD coupling), providing a more personalized measure of neurovascular function that accounts for individual variability in CBF. We also explore the transcriptomic architectures of CBF-BOLD coupling in MCI.
Methods:
The rs-fMRI data and Arterial Spin Labeling (ASL) data were collected from the Fourth People's Hospital of Chengdu. All fMRI images were preprocessed using SPM as implemented in the Neuroscience Information Toolbox (http://www.neuro.uestc.edu.cn/NIT.html) (Dong et al., 2018). CBF data were preprocessed using the ASLtbx toolbox (Wang et al., 2008), implemented in SPM. Then we used REACT to calculate the individual CBF-BOLD coupling. Next, partial least squares (PLS) correlation was employed to explore the relationship between CBF-BOLD coupling and transcriptional activity across all genes provided by the Allen Human Brain Atlas (Sunkin et al., 2013). To investigate the biological pathways and disease associations linked with CBF-BOLD coupling, we performed functional enrichment analysis for Gene Ontology (GO) gene sets and brain disease-related pathways using Metascape (Zhou et al., 2019). To further interpret the biological implications of gene expression patterns associated with CBF-BOLD coupling, we conducted a functional meta-analysis using the Neurosynth database (Lariviere et al., 2023).
Results:
The weighted gene expression pattern of the first PLS component accounted for the greatest spatial variance (P<0.0001, Fig.1.a), with high expression (red areas) in the frontal networks and lower expression (blue) in the somatomotor areas (Fig.1.b). The relationships between decoders and PLS1 are shown in Fig.1.c. Notably, we found positive correlations between the PLS1 and dementia, as well as frontotemporal decoders (r=0.01~0.4). The top-ranked genes with positive PLS1 weights were significantly enriched for several synapse-related terms, such as "synaptic signaling" (-log(P)=13.5, Fig.1.d). Additionally, we found the top-ranked genes with PLS1 weights associated with memory impairment (-log(P)=6.3, Fig.1.e).
Conclusions:
In conclusion, the enriched pathways derived from PLS1 of CBF-BOLD coupling collectively suggested the neuropathogenesis underlying MCI, which not only strongly supported the informative role of CBF-BOLD coupling but also revealed the molecular mechanisms associated with altered neurovascular coupling. These findings highlight the relationships between cerebral blood flow and global neuronal activity, which changes during disease progression, and provide a new perspective for understanding the disruption of brain function in patients with MCI.
Lifespan Development:
Aging 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics
Keywords:
Cerebral Blood Flow
FUNCTIONAL MRI
Other - Mild Cognitive Impairment, Neurovascular coupling
1|2Indicates the priority used for review
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Provide references using APA citation style.
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